Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes ================ Nguyen Lam Vuong 29-Jul-2021 ``` r library(tidyverse) library(gtsummary) # need to install package 'flextable' library(rms) library(MuMIn) # for best subset selection library(facetscales) # need to install package 'facetscales' from devtools::install_github("zeehio/facetscales") source("Elife ERA functions.R") # to include my functions options(gtsummary.tbl_summary.percent_fun = function(x) sprintf(x * 100, fmt='%1.0f')) # to report percentages without decimal options(knitr.kable.NA = '') # to set NA to '' in kable results theme_set(theme_bw()) # I love black & white theme options(na.action = "na.fail") # for 'dredge' function [MuMIn] ``` ``` r # Full data dat0 <- read_csv("Dengue_Biomarkers_data_27Jul2021.csv") %>% mutate(group2 = factor(sev.or.inte, levels=c(0,1), labels=c("Uncomplicated dengue","Severe/moderate dengue")), Country = as.factor(Country), Serotype = as.factor(Serotype), Serology = factor(Serology, levels = c("Probable primary", "Probable secondary", "Inconclusive")), WHO2009 = factor(WHO2009, levels = c("Mild dengue", "Dengue with warning signs", "Severe dengue", "Unknown"))) # Data at enrollment (for Table 1 & Appendix 4-table 1) dat <- dat0 %>% filter(Time == "Enrolment") # Data at enrollment for models ## with inverse probability weights (IPW) for inclusion probability by countries (for the analysis of secondary outcomes) ## transform the biomarker's levels to log-2 and viremia to log-10 ## set all biomarker's levels under the limit of detection (u...=1) to the limit of detection dat1 <- dat %>% mutate(age15 = factor(age15, levels=c("No","Yes"), labels=c("Under 15","15 and above")), Serotype = ifelse(Serotype=="Unknown", NA, as.character(Serotype)), Serotype = ifelse(Serotype=="DENV-1", 1, 2), Serotype2 = Serotype, # for getting results for Appendix5-tables 1, 2 Serotype = factor(Serotype, levels=c(1,2), labels=c("DENV-1","Others")), # set Serotype to DENV-1 and others ipw = ifelse(sev.or.inte==1, 1, ifelse(Country=="Vietnam", (1505-204)/436, ifelse(Country=="Malaysia", (259-29)/58, ifelse(Country=="El Salvador", (306-18)/23, ifelse(Country=="Cambodia", (302-30)/39, NA))))), ipwsd = ipw * 837/2372, VCAM = ifelse(uVCAM==1, log2(0.028), log2(VCAM1)), SDC = log2(SDC1), Ang = ifelse(uAng==1, log2(17.1), log2(Ang2)), IL8 = ifelse(uIL8==1, log2(1.8), log2(IL8)), IP10 = ifelse(uIP10==1, log2(1.18), log2(IP10)), IL1RA = ifelse(uIL1RA==1, log2(18), log2(IL1RA)), CD163 = log2(CD163), TREM = ifelse(uTREM==1, log2(10.65), log2(TREM1)), Fer = log2(Fer), CRP = log2(CRP), Vir = log10(Viremia)) %>% select(Code, Age, age15, Serotype, Serotype2, sev.or.inte, sev.only, sev.or.ws, hospital, VCAM, SDC, Ang, IL8, IP10, IL1RA, CD163, TREM, Fer, CRP, Vir, uVCAM, uAng, uIL8, uIP10, uCD163, uTREM, uVir, ipw, ipwsd) dat1c <- dat1 %>% filter(age15 == "Under 15") ## Data for children dat1a <- dat1 %>% filter(age15 == "15 and above") ## Data for adults # Set references for calculating the ORs and 95% CIs ref0 <- c(median(dat1$VCAM), median(dat1$SDC), median(dat1$Ang), median(dat1$IL8), median(dat1$IP10), median(dat1$IL1RA), median(dat1$CD163), median(dat1$TREM), median(dat1$Fer), median(dat1$CRP)) # Create data for plotting biomarkers (for Figure 2 & Appendix 4-figure 1) ## Enrollment tmp1 <- dat0 %>% filter(Time == "Enrolment") %>% select(Code, group2, Day, Daygr, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP) %>% gather(., "Biomarker", "Result", 5:14) ## Follow up tmp2 <- dat0 %>% filter(Time == "Follow up") %>% select(Code, group2, Day, Daygr, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP) %>% gather(., "Biomarker", "Result", 5:14) ## Merge long data for biomarkers dat_plot <- bind_rows(tmp1, tmp2) %>% mutate(Daygr = factor(Daygr, levels = c("Day 1", "Day 2", "Day 3", "Day 10-20", "Day >20"), labels = c("1", "2", "3", "10-20", ">20")), Biomarker = factor(Biomarker, levels=c("VCAM1", "SDC1", "Ang2", "IL8", "IP10", "IL1RA", "CD163", "TREM1", "Fer", "CRP"), labels=c("VCAM-1 (ng/ml)", "SDC-1 (pg/ml)", "Ang-2 (pg/ml)", "IL-8 (pg/ml)", "IP-10 (pg/ml)", "IL-1RA (pg/ml)", "sCD163 (ng/ml)", "sTREM-1 (pg/ml)", "Ferritin (ng/ml)", "CRP (mg/l)"))) ``` ########################################################################################### #### Table 1. Summary of clinical data by primary outcome. ``` r # All patients t1 <- dat %>% select(group2, Country, Age, Sex, Day, Serotype, Serology, Obesity, Diabetes, WHO2009, hospital) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})", all_categorical() ~ "{n} ({p})"), value = list(Sex ~ "Male"), digits = list(all_continuous() ~ c(0,0)), label = list(Age ~ "Age (years)", Sex ~ "Gender male", Day ~ "Illness day at enrolment", Serology ~ "Immune status", WHO2009 ~ "WHO 2009 classification", hospital ~ "Hospitalization")) %>% add_stat_label(label = c(all_categorical() ~ "n (%)", Age ~ "median (1st, 3rd quartiles)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") # Children (<15 years of age) t2 <- dat %>% filter(age15=="No") %>% select(group2, Country, Age, Sex, Day, Serotype, Serology, Obesity, Diabetes, WHO2009, hospital) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})", all_categorical() ~ "{n} ({p})"), value = list(Sex ~ "Male"), digits = list(all_continuous() ~ c(0,0)), label = list(Age ~ "Age (years)", Sex ~ "Gender male", Day ~ "Illness day at enrolment", Serology ~ "Immune status", WHO2009 ~ "WHO 2009 classification", hospital ~ "Hospitalization")) %>% add_stat_label(label = c(all_categorical() ~ "n (%)", Age ~ "median (1st, 3rd quartiles)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") # Adults (>=15 years of age) t3 <- dat %>% filter(age15=="Yes") %>% select(group2, Country, Age, Sex, Day, Serotype, Serology, Obesity, Diabetes, WHO2009, hospital) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})", all_categorical() ~ "{n} ({p})"), value = list(Sex ~ "Male"), digits = list(all_continuous() ~ c(0,0)), label = list(Age ~ "Age (years)", Sex ~ "Gender male", Day ~ "Illness day at enrolment", Serology ~ "Immune status", WHO2009 ~ "WHO 2009 classification", hospital ~ "Hospitalization")) %>% add_stat_label(label = c(all_categorical() ~ "n (%)", Age ~ "median (1st, 3rd quartiles)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") # Merge tables tbl_merge(tbls = list(t1, t2, t3), tab_spanner = c("**All patients**", "**Children**", "**Adults**")) ``` <div id="bycqwepuoj" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;"> <style>html { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif; } #bycqwepuoj .gt_table { display: table; border-collapse: collapse; margin-left: auto; margin-right: auto; color: #333333; font-size: 16px; font-weight: normal; font-style: normal; background-color: #FFFFFF; width: auto; border-top-style: solid; border-top-width: 2px; border-top-color: #A8A8A8; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #A8A8A8; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; } #bycqwepuoj .gt_heading { background-color: #FFFFFF; text-align: center; border-bottom-color: #FFFFFF; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; } #bycqwepuoj .gt_title { color: #333333; font-size: 125%; font-weight: initial; padding-top: 4px; padding-bottom: 4px; border-bottom-color: #FFFFFF; border-bottom-width: 0; } #bycqwepuoj .gt_subtitle { color: #333333; font-size: 85%; font-weight: initial; padding-top: 0; padding-bottom: 4px; border-top-color: #FFFFFF; border-top-width: 0; } #bycqwepuoj .gt_bottom_border { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; } #bycqwepuoj .gt_col_headings { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; } #bycqwepuoj .gt_col_heading { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 6px; padding-left: 5px; padding-right: 5px; overflow-x: hidden; } #bycqwepuoj .gt_column_spanner_outer { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; padding-top: 0; padding-bottom: 0; padding-left: 4px; padding-right: 4px; } #bycqwepuoj .gt_column_spanner_outer:first-child { padding-left: 0; } #bycqwepuoj .gt_column_spanner_outer:last-child { padding-right: 0; } #bycqwepuoj .gt_column_spanner { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 6px; overflow-x: hidden; display: inline-block; width: 100%; } #bycqwepuoj .gt_group_heading { padding: 8px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; } #bycqwepuoj .gt_empty_group_heading { padding: 0.5px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: middle; } #bycqwepuoj .gt_from_md > :first-child { margin-top: 0; } #bycqwepuoj .gt_from_md > :last-child { margin-bottom: 0; } #bycqwepuoj .gt_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; margin: 10px; border-top-style: solid; border-top-width: 1px; border-top-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; overflow-x: hidden; } #bycqwepuoj .gt_stub { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 12px; } #bycqwepuoj .gt_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; } #bycqwepuoj .gt_first_summary_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; } #bycqwepuoj .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; } #bycqwepuoj .gt_first_grand_summary_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; } #bycqwepuoj .gt_striped { background-color: rgba(128, 128, 128, 0.05); } #bycqwepuoj .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; } #bycqwepuoj .gt_footnotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; } #bycqwepuoj .gt_footnote { margin: 0px; font-size: 90%; padding: 4px; } #bycqwepuoj .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; } #bycqwepuoj .gt_sourcenote { font-size: 90%; padding: 4px; } #bycqwepuoj .gt_left { text-align: left; } #bycqwepuoj .gt_center { text-align: center; } #bycqwepuoj .gt_right { text-align: right; font-variant-numeric: tabular-nums; } #bycqwepuoj .gt_font_normal { font-weight: normal; } #bycqwepuoj .gt_font_bold { font-weight: bold; } #bycqwepuoj .gt_font_italic { font-style: italic; } #bycqwepuoj .gt_super { font-size: 65%; } #bycqwepuoj .gt_footnote_marks { font-style: italic; font-weight: normal; font-size: 65%; } </style> <table class="gt_table"> <thead class="gt_col_headings"> <tr> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1"></th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>All patients</strong></span> </th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>Children</strong></span> </th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>Adults</strong></span> </th> </tr> <tr> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=556)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=281)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=337)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=127)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=219)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=154)</strong></th> </tr> </thead> <tbody class="gt_table_body"> <tr><td class="gt_row gt_left">Country, n (%)</td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Cambodia</td> <td class="gt_row gt_center">39 (7)</td> <td class="gt_row gt_center">30 (11)</td> <td class="gt_row gt_center">37 (11)</td> <td class="gt_row gt_center">29 (23)</td> <td class="gt_row gt_center">2 (1)</td> <td class="gt_row gt_center">1 (1)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">El Salvador</td> <td class="gt_row gt_center">23 (4)</td> <td class="gt_row gt_center">18 (6)</td> <td class="gt_row gt_center">23 (7)</td> <td class="gt_row gt_center">18 (14)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">0 (0)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Malaysia</td> <td class="gt_row gt_center">58 (10)</td> <td class="gt_row gt_center">29 (10)</td> <td class="gt_row gt_center">3 (1)</td> <td class="gt_row gt_center">1 (1)</td> <td class="gt_row gt_center">55 (25)</td> <td class="gt_row gt_center">28 (18)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Vietnam</td> <td class="gt_row gt_center">436 (78)</td> <td class="gt_row gt_center">204 (73)</td> <td class="gt_row gt_center">274 (81)</td> <td class="gt_row gt_center">79 (62)</td> <td class="gt_row gt_center">162 (74)</td> <td class="gt_row gt_center">125 (81)</td></tr> <tr><td class="gt_row gt_left">Age (years), median (1st, 3rd quartiles)</td> <td class="gt_row gt_center">12 (9, 22)</td> <td class="gt_row gt_center">16 (10, 24)</td> <td class="gt_row gt_center">10 (8, 12)</td> <td class="gt_row gt_center">10 (7, 12)</td> <td class="gt_row gt_center">26 (20, 34)</td> <td class="gt_row gt_center">22 (18, 30)</td></tr> <tr><td class="gt_row gt_left">Gender male, n (%)</td> <td class="gt_row gt_center">299 (54)</td> <td class="gt_row gt_center">170 (60)</td> <td class="gt_row gt_center">173 (51)</td> <td class="gt_row gt_center">80 (63)</td> <td class="gt_row gt_center">126 (58)</td> <td class="gt_row gt_center">90 (58)</td></tr> <tr><td class="gt_row gt_left">Illness day at enrolment, n (%)</td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">1</td> <td class="gt_row gt_center">91 (16)</td> <td class="gt_row gt_center">49 (17)</td> <td class="gt_row gt_center">57 (17)</td> <td class="gt_row gt_center">25 (20)</td> <td class="gt_row gt_center">34 (16)</td> <td class="gt_row gt_center">24 (16)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">2</td> <td class="gt_row gt_center">260 (47)</td> <td class="gt_row gt_center">130 (46)</td> <td class="gt_row gt_center">156 (46)</td> <td class="gt_row gt_center">52 (41)</td> <td class="gt_row gt_center">104 (47)</td> <td class="gt_row gt_center">78 (51)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">3</td> <td class="gt_row gt_center">205 (37)</td> <td class="gt_row gt_center">102 (36)</td> <td class="gt_row gt_center">124 (37)</td> <td class="gt_row gt_center">50 (39)</td> <td class="gt_row gt_center">81 (37)</td> <td class="gt_row gt_center">52 (34)</td></tr> <tr><td class="gt_row gt_left">Serotype, n (%)</td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">DENV-1</td> <td class="gt_row gt_center">228 (41)</td> <td class="gt_row gt_center">121 (43)</td> <td class="gt_row gt_center">161 (48)</td> <td class="gt_row gt_center">61 (48)</td> <td class="gt_row gt_center">67 (31)</td> <td class="gt_row gt_center">60 (39)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">DENV-2</td> <td class="gt_row gt_center">74 (13)</td> <td class="gt_row gt_center">47 (17)</td> <td class="gt_row gt_center">22 (7)</td> <td class="gt_row gt_center">16 (13)</td> <td class="gt_row gt_center">52 (24)</td> <td class="gt_row gt_center">31 (20)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">DENV-3</td> <td class="gt_row gt_center">59 (11)</td> <td class="gt_row gt_center">29 (10)</td> <td class="gt_row gt_center">43 (13)</td> <td class="gt_row gt_center">18 (14)</td> <td class="gt_row gt_center">16 (7)</td> <td class="gt_row gt_center">11 (7)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">DENV-4</td> <td class="gt_row gt_center">161 (29)</td> <td class="gt_row gt_center">70 (25)</td> <td class="gt_row gt_center">91 (27)</td> <td class="gt_row gt_center">26 (20)</td> <td class="gt_row gt_center">70 (32)</td> <td class="gt_row gt_center">44 (29)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Unknown</td> <td class="gt_row gt_center">34 (6)</td> <td class="gt_row gt_center">14 (5)</td> <td class="gt_row gt_center">20 (6)</td> <td class="gt_row gt_center">6 (5)</td> <td class="gt_row gt_center">14 (6)</td> <td class="gt_row gt_center">8 (5)</td></tr> <tr><td class="gt_row gt_left">Immune status, n (%)</td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Probable primary</td> <td class="gt_row gt_center">124 (22)</td> <td class="gt_row gt_center">41 (15)</td> <td class="gt_row gt_center">86 (26)</td> <td class="gt_row gt_center">15 (12)</td> <td class="gt_row gt_center">38 (17)</td> <td class="gt_row gt_center">26 (17)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Probable secondary</td> <td class="gt_row gt_center">355 (64)</td> <td class="gt_row gt_center">218 (78)</td> <td class="gt_row gt_center">202 (60)</td> <td class="gt_row gt_center">100 (79)</td> <td class="gt_row gt_center">153 (70)</td> <td class="gt_row gt_center">118 (77)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Inconclusive</td> <td class="gt_row gt_center">77 (14)</td> <td class="gt_row gt_center">22 (8)</td> <td class="gt_row gt_center">49 (15)</td> <td class="gt_row gt_center">12 (9)</td> <td class="gt_row gt_center">28 (13)</td> <td class="gt_row gt_center">10 (6)</td></tr> <tr><td class="gt_row gt_left">Obesity, n (%)</td> <td class="gt_row gt_center">78 (14)</td> <td class="gt_row gt_center">29 (10)</td> <td class="gt_row gt_center">62 (18)</td> <td class="gt_row gt_center">19 (15)</td> <td class="gt_row gt_center">16 (7)</td> <td class="gt_row gt_center">10 (6)</td></tr> <tr><td class="gt_row gt_left">Diabetes, n (%)</td> <td class="gt_row gt_center">4 (1)</td> <td class="gt_row gt_center">1 (0)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">4 (2)</td> <td class="gt_row gt_center">1 (1)</td></tr> <tr><td class="gt_row gt_left">WHO 2009 classification, n (%)</td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td> <td class="gt_row gt_center"></td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Mild dengue</td> <td class="gt_row gt_center">266 (48)</td> <td class="gt_row gt_center">49 (17)</td> <td class="gt_row gt_center">168 (50)</td> <td class="gt_row gt_center">17 (13)</td> <td class="gt_row gt_center">98 (45)</td> <td class="gt_row gt_center">32 (21)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Dengue with warning signs</td> <td class="gt_row gt_center">288 (52)</td> <td class="gt_row gt_center">186 (66)</td> <td class="gt_row gt_center">169 (50)</td> <td class="gt_row gt_center">81 (64)</td> <td class="gt_row gt_center">119 (54)</td> <td class="gt_row gt_center">105 (68)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Severe dengue</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">43 (15)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">27 (21)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">16 (10)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Unknown</td> <td class="gt_row gt_center">2 (0)</td> <td class="gt_row gt_center">3 (1)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">2 (2)</td> <td class="gt_row gt_center">2 (1)</td> <td class="gt_row gt_center">1 (1)</td></tr> <tr><td class="gt_row gt_left">Hospitalization, n (%)</td> <td class="gt_row gt_center">175 (31)</td> <td class="gt_row gt_center">161 (57)</td> <td class="gt_row gt_center">127 (38)</td> <td class="gt_row gt_center">83 (65)</td> <td class="gt_row gt_center">48 (22)</td> <td class="gt_row gt_center">78 (51)</td></tr> </tbody> </table> </div> ########################################################################################### #### Figure 2. Biomarker levels by groups. ``` r tick <- c(0,1,4,10,40,100,200,400,1000,4000,10000,20000,40000,70000) # for y-axis tick labels p2 <- dat_plot %>% ggplot(., aes(Daygr, Result^(1/4), fill=group2, color=group2)) + geom_boxplot(alpha=.5, outlier.size=.9, lwd=.4, fatten=1) + geom_boxplot(alpha=0, outlier.color=NA, color="black", lwd=.4, fatten=1) + facet_wrap(~ Biomarker, scales="free", ncol=5) + scale_y_continuous(breaks=tick^(1/4), labels=tick) + xlab("Illness day (day 1 [n=140]; day 2 [n=390]; day 3 [n=307]; day 10-20 [n=625]; day >20 [n=43])") + theme(axis.title.y=element_blank(), legend.position="top", legend.title=element_blank(), axis.text.y=element_text(size=rel(.8))) p2 ``` <!-- --> ``` r #ggsave(filename="Fig 2.pdf", dpi=300, plot=p2, width=11, height=8) ``` ########################################################################################### #### Table 2. Results from models for the primary endpoint (severe or moderate dengue). ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # Use my function 'get_est1' to get ORs and CIs from models tmp1 <- data.frame( sort1 = rep(c(1:10), 2), sort2 = c(rep(2,10), rep(3,10)), bio = rep(c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP"), 2), ref1 = c(ref0-1, ref0), ref2 = c(ref0, ref0+1) ) %>% arrange(sort1, sort2) %>% group_by(sort1, sort2) %>% do(cbind(., # Children - single models or1c = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo1c = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up1c = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Children - global model or2c = get_est2(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo2c = get_est2(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up2c = get_est2(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Adults - single models or1a = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo1a = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up1a = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1), # Adults - global model or2a = get_est2(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo2a = get_est2(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up2a = get_est2(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1))) %>% ungroup() for (i in 6:17) {tmp1[[i]] <- sprintf("%.2f", round(tmp1[[i]],2))} # Use my function 'get_est1' to get p-values from models tmp2 <- data.frame( sort1 = c(1:10), sort2 = rep(1,10), bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") ) %>% group_by(sort1) %>% do(cbind(., p.s1 = get_est1(out="sev.or.inte", bio=.$bio, est="p", dat=dat1), # P overall from single models p.s1_int = get_est1(out="sev.or.inte", bio=.$bio, est="p int", dat=dat1), # P interaction from single models p.g1 = get_est2(out="sev.or.inte", bio=.$bio, est="p", dat=dat1), # P overall from global models p.g1_int = get_est2(out="sev.or.inte", bio=.$bio, est="p int", dat=dat1))) %>% # P interaction from global models ungroup() for (i in 4:7) {tmp2[[i]] <- ifelse(tmp2[[i]]<0.001, "<0.001", sprintf("%.3f", round(tmp2[[i]],3)))} # Combine results into a table res1 <- bind_rows(tmp1, tmp2) %>% arrange(sort1, sort2) %>% mutate(bio = ifelse(!is.na(ref1), paste(" -", round(2^ref2,0), "vs", round(2^ref1,0), sep=" "), as.character(bio)), or.sc1 = ifelse(is.na(lo1c), NA, paste(or1c, " (", lo1c, "-", up1c, ")", sep="")), # s: single model; c: children or.gc1 = ifelse(is.na(lo2c), NA, paste(or2c, " (", lo2c, "-", up2c, ")", sep="")), # g: global model or.sa1 = ifelse(is.na(lo1a), NA, paste(or1a, " (", lo1a, "-", up1a, ")", sep="")), # a: adults or.ga1 = ifelse(is.na(lo2a), NA, paste(or2a, " (", lo2a, "-", up2a, ")", sep=""))) %>% select(bio, or.sc1, or.sa1, p.s1, p.s1_int, or.gc1, or.ga1, p.g1, p.g1_int) names(res1) <- c("", "OR (children - single)", "OR (adults - single)", "P overall (single)", "P interaction (single)", "OR (children - global)", "OR (adults - global)", "P overall (global)", "P interaction (global)") # Report the results knitr::kable(res1) ``` | | OR (children - single) | OR (adults - single) | P overall (single) | P interaction (single) | OR (children - global) | OR (adults - global) | P overall (global) | P interaction (global) | |:-----------------|:-----------------------|:---------------------|:-------------------|:-----------------------|:-----------------------|:---------------------|:-------------------|:-----------------------| | VCAM | | | <0.001 | 0.715 | | | 0.441 | 0.213 | | \- 1636 vs 818 | 1.20 (1.04-1.38) | 1.35 (1.15-1.58) | | | 0.90 (0.73-1.10) | 1.22 (0.96-1.57) | | | | \- 3272 vs 1636 | 1.25 (1.02-1.53) | 1.48 (1.19-1.85) | | | 0.87 (0.66-1.15) | 1.30 (0.93-1.80) | | | | SDC | | | <0.001 | 0.088 | | | 0.002 | 0.588 | | \- 2519 vs 1260 | 2.67 (1.31-5.43) | 3.33 (1.32-8.42) | | | 2.03 (0.77-5.34) | 5.11 (1.56-16.78) | | | | \- 5039 vs 2519 | 1.71 (1.18-2.47) | 3.71 (2.09-6.58) | | | 1.76 (0.98-3.14) | 2.52 (1.17-5.42) | | | | Ang | | | <0.001 | 0.524 | | | 0.039 | 0.068 | | \- 1204 vs 602 | 1.64 (1.39-1.94) | 1.51 (1.26-1.82) | | | 1.67 (1.23-2.25) | 1.01 (0.74-1.38) | | | | \- 2409 vs 1204 | 2.21 (1.58-3.10) | 2.00 (1.40-2.85) | | | 1.95 (1.25-3.05) | 1.01 (0.65-1.57) | | | | IL8 | | | <0.001 | <0.001 | | | <0.001 | <0.001 | | \- 14 vs 7 | 1.42 (1.05-1.91) | 2.18 (1.47-3.24) | | | 0.91 (0.63-1.34) | 1.69 (1.05-2.71) | | | | \- 28 vs 14 | 0.99 (0.78-1.25) | 2.33 (1.63-3.33) | | | 0.53 (0.36-0.77) | 2.05 (1.34-3.13) | | | | IP10 | | | <0.001 | 0.984 | | | 0.206 | 0.630 | | \- 3093 vs 1546 | 1.46 (1.26-1.68) | 1.45 (1.21-1.73) | | | 0.94 (0.73-1.19) | 0.80 (0.57-1.12) | | | | \- 6186 vs 3093 | 1.68 (1.35-2.09) | 1.69 (1.29-2.22) | | | 1.08 (0.77-1.51) | 0.82 (0.52-1.29) | | | | IL1RA | | | <0.001 | 0.082 | | | <0.001 | 0.032 | | \- 6434 vs 3217 | 1.69 (1.42-2.03) | 1.48 (1.21-1.81) | | | 2.07 (1.52-2.84) | 1.45 (0.98-2.15) | | | | \- 12868 vs 6434 | 1.82 (1.46-2.27) | 1.70 (1.29-2.24) | | | 2.16 (1.53-3.05) | 1.47 (0.94-2.30) | | | | CD163 | | | <0.001 | 0.551 | | | 0.217 | 0.341 | | \- 295 vs 147 | 1.57 (1.14-2.15) | 1.49 (1.13-1.98) | | | 1.40 (0.89-2.22) | 1.27 (0.84-1.91) | | | | \- 589 vs 295 | 1.46 (1.10-1.93) | 1.61 (1.09-2.37) | | | 1.21 (0.87-1.69) | 1.39 (0.89-2.18) | | | | TREM | | | 0.059 | 0.997 | | | 0.555 | 0.393 | | \- 85 vs 42 | 1.87 (1.23-2.84) | 1.79 (1.10-2.93) | | | 1.13 (0.70-1.81) | 1.21 (0.65-2.26) | | | | \- 169 vs 85 | 1.12 (0.91-1.38) | 1.12 (0.82-1.53) | | | 0.89 (0.65-1.21) | 0.61 (0.38-0.99) | | | | Fer | | | 0.042 | 0.054 | | | 0.008 | 0.002 | | \- 243 vs 122 | 1.18 (1.01-1.38) | 1.06 (0.89-1.27) | | | 1.30 (1.04-1.64) | 0.78 (0.61-0.99) | | | | \- 487 vs 243 | 1.26 (1.00-1.58) | 0.90 (0.66-1.23) | | | 1.22 (0.89-1.67) | 0.66 (0.44-1.00) | | | | CRP | | | <0.001 | 0.031 | | | 0.184 | 0.138 | | \- 28 vs 14 | 1.26 (1.12-1.41) | 1.25 (1.03-1.52) | | | 1.08 (0.93-1.25) | 1.10 (0.85-1.44) | | | | \- 56 vs 28 | 1.13 (0.95-1.34) | 1.38 (1.11-1.71) | | | 0.93 (0.75-1.15) | 1.36 (1.02-1.81) | | | ########################################################################################### #### Figure 3. Results from models for the primary endpoint (severe or moderate dengue). ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # Get results from models for children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred1(out="sev.or.inte", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m2 <- get_pred1(out="sev.or.inte", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m3 <- get_pred1(out="sev.or.inte", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m4 <- get_pred1(out="sev.or.inte", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m5 <- get_pred1(out="sev.or.inte", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m6 <- get_pred1(out="sev.or.inte", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m7 <- get_pred1(out="sev.or.inte", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m8 <- get_pred1(out="sev.or.inte", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m9 <- get_pred1(out="sev.or.inte", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m10 <- get_pred1(out="sev.or.inte", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg1 <- get_pred2(out="sev.or.inte", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg2 <- get_pred2(out="sev.or.inte", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg3 <- get_pred2(out="sev.or.inte", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg4 <- get_pred2(out="sev.or.inte", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg5 <- get_pred2(out="sev.or.inte", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg6 <- get_pred2(out="sev.or.inte", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg7 <- get_pred2(out="sev.or.inte", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg8 <- get_pred2(out="sev.or.inte", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg9 <- get_pred2(out="sev.or.inte", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg10 <- get_pred2(out="sev.or.inte", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_p1 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(age = "10 years") # Get results from models for adults dd$limits["Adjust to","Age"] <- 25 dat_m1 <- get_pred1(out="sev.or.inte", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m2 <- get_pred1(out="sev.or.inte", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m3 <- get_pred1(out="sev.or.inte", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m4 <- get_pred1(out="sev.or.inte", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m5 <- get_pred1(out="sev.or.inte", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m6 <- get_pred1(out="sev.or.inte", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m7 <- get_pred1(out="sev.or.inte", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m8 <- get_pred1(out="sev.or.inte", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m9 <- get_pred1(out="sev.or.inte", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m10 <- get_pred1(out="sev.or.inte", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg1 <- get_pred2(out="sev.or.inte", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg2 <- get_pred2(out="sev.or.inte", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg3 <- get_pred2(out="sev.or.inte", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg4 <- get_pred2(out="sev.or.inte", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg5 <- get_pred2(out="sev.or.inte", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg6 <- get_pred2(out="sev.or.inte", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg7 <- get_pred2(out="sev.or.inte", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg8 <- get_pred2(out="sev.or.inte", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg9 <- get_pred2(out="sev.or.inte", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg10 <- get_pred2(out="sev.or.inte", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_p2 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(age = "25 years") # Merge results for children and adults vis1 <- rbind(dat_p1, dat_p2) %>% mutate(outcome = "outcome 1") # Merge data for plots tmp0 <- dat1 %>% arrange(age15) %>% select(sev.or.inte) tmp <- data.frame(sev.or.inte = rep(tmp0$sev.or.inte, 20)) tmp_p1 <- vis1 %>% arrange(biomarker, model) %>% mutate(value1 = 2^value, age = factor(age, levels=c("10 years", "25 years")), gr = ifelse(model=="Single model" & age=="10 years", 1, ifelse(model=="Single model" & age=="25 years", 2, ifelse(model=="Global model" & age=="10 years", 3, 4))), gr = factor(gr, levels=c(1:4), labels=c("Single children", "Single adults", "Global children", "Global adults")), model = factor(model, levels=c("Single model", "Global model"))) %>% bind_cols(., tmp) vline <- tmp_p1 %>% filter(model=="Single model") %>% filter(yhat==0) %>% select(biomarker, value, value1) %>% rename(vline=value, vline1=value1) dat_p <- left_join(tmp_p1, vline, by="biomarker") # Alternative data to limit to 5th - 95th of the values dat_alt <- dat_p %>% group_by(biomarker, model) %>% mutate(up = quantile(value, .95), lo = quantile(value, .05)) %>% ungroup() %>% mutate(is.outlier = value<lo | value>up | value<0, value = ifelse(is.outlier, NA, value), value1 = ifelse(is.outlier, NA, value1), yhat = ifelse(is.outlier, NA, yhat), lower = ifelse(is.outlier, NA, lower), upper = ifelse(is.outlier, NA, upper)) dat_1_5 <- dat_alt %>% filter(biomarker %in% c("VCAM", "SDC", "Ang", "IL8", "IP10")) %>% mutate(biomarker = factor(biomarker, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10"))) dat_6_10 <- dat_alt %>% filter(biomarker %in% c("IL1RA", "CD163", "TREM", "Fer", "CRP")) %>% mutate(biomarker = factor(biomarker, levels=c("IL1RA", "CD163", "TREM", "Fer", "CRP"))) # Modify facets' scales #require(facetscales) xVCAM <- c(1,4,15,60,250,1000,4000) xSDC <- c(1400,2000,2800,4000,5600) xAng <- c(50,100,250,500,1000,2000) xIL8 <- c(5,7,10,14,20,28,40) xIP10 <- c(25,100,400,1600,6400) xIL1RA <- c(1000,2000,4000,8000,16000) xCD163 <- c(75,150,300,600) xTREM <- c(35,50,70,100,140,200) xFer <- c(50,100,200,400,800) xCRP <- c(2.5,5,10,20,40,80) scales_x <- list( `VCAM` = scale_x_continuous(breaks = log2(xVCAM), labels = xVCAM), `SDC` = scale_x_continuous(breaks = log2(xSDC), labels = xSDC), `Ang` = scale_x_continuous(breaks = log2(xAng), labels = xAng), `IL8` = scale_x_continuous(breaks = log2(xIL8), labels = xIL8), `IP10` = scale_x_continuous(breaks = log2(xIP10), labels = xIP10), `IL1RA` = scale_x_continuous(breaks = log2(xIL1RA), labels = xIL1RA), `CD163` = scale_x_continuous(breaks = log2(xCD163), labels = xCD163), `TREM` = scale_x_continuous(breaks = log2(xTREM), labels = xTREM), `Fer` = scale_x_continuous(breaks = log2(xFer), labels = xFer), `CRP` = scale_x_continuous(breaks = log2(xCRP), labels = xCRP) ) ybreak1 <- c(.125, .25, .5, 1, 2, 4, 8) ybreak2 <- c(.125, .25, .5, 1, 2, 4) scales_y1 <- list( `Single model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1), `Global model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1) ) scales_y2 <- list( `Single model` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2), `Global model` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2) ) # Set facets' names models <- c(`Single model` = "Single model", `Global model` = "Global model") biomarkers <- c(`VCAM` = "VCAM-1 (ng/ml)", `SDC`= "SDC-1 (pg/ml)", `Ang` = "Ang-2 (pg/ml)", `IL8` = "IL-8 (pg/ml)", `IP10` = "IP-10 (pg/ml)", `IL1RA` = "IL-1RA (pg/ml)", `CD163` = "sCD163 (ng/ml)", `TREM` = "sTREM-1 (pg/ml)", `Fer` = "Ferritin (ng/ml)", `CRP` = "CRP (mg/l)") # Plot for the first 5 biomarkers p.1.5 <- ggplot(dat_1_5, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_1_5, model=="Single model" & sev.or.inte==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Single model" & sev.or.inte==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Global model" & sev.or.inte==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_1_5, model=="Global model" & sev.or.inte==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="top", legend.title=element_blank(), axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y1), labeller = as_labeller(c(models, biomarkers))) # Plot for the last 5 biomarkers p.6.10 <- ggplot(dat_6_10, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_6_10, model=="Single model" & sev.or.inte==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, model=="Single model" & sev.or.inte==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, model=="Global model" & sev.or.inte==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_6_10, model=="Global model" & sev.or.inte==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="none", axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y2), labeller = as_labeller(c(models, biomarkers))) # Merge plots p3 <- gridExtra::grid.arrange(p.1.5, p.6.10, nrow=2, heights=c(1,.86)) ``` <!-- --> ``` r #ggsave(filename="Fig 3.pdf", p3, dpi=300, width=11, height=8) ``` ########################################################################################### #### Table 3. Best combinations of biomarkers associated with severe or moderate dengue for children. ``` r # EPV -------------------------------------------------------------- pred <- c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") # Estimate full model ---------------------------------------------- full_mod <- glm(sev.or.inte ~ VCAM + SDC + Ang + IL8 + IP10 + IL1RA + CD163 + TREM + Fer + CRP, family=binomial, data=dat1c, x=T, y=T) # Selected model --------------------------------------------------- sel_var <- matrix(0, ncol=length(pred)+1, nrow=5, dimnames=list(NULL, c(pred, "aic"))) for (i in 1:5) { if (i==1) { bs <- dredge(full_mod, rank="AIC") } else { bs <- dredge(full_mod, rank="AIC", m.lim=c(i,i)) } bs_var <- attr(get.models(bs, 1)[[1]]$terms, "term.labels") for (j in 1:(ncol(sel_var)-1)) {sel_var[i,j] <- ifelse(names(sel_var[i,j]) %in% bs_var, 1, 0)} formula <- paste("sev.or.inte~", paste(names(sel_var[i,][sel_var[i,]==1]), collapse = "+")) sel_mod <- glm(formula, data = dat1c, family = binomial, x = T, y = T) sel_var[i, ncol(sel_var)] <- AIC(sel_mod) } # Report results -------------------------------------------------- out1 <- as.data.frame(sel_var) %>% mutate(AIC = round(aic,1)) %>% select(-aic) for (i in 1:(ncol(out1)-1)) {out1[,i] <- ifelse(out1[,i]==0, NA, "+")} out2 <- as.data.frame(t(out1)) colnames(out2) <- c("Best of all combinations", "Best combination of 2 variables", "Best combination of 3 variables", "Best combination of 4 variables", "Best combination of 5 variables") rownames(out2) <- c("- VCAM-1", "- SDC-1", "- Ang-2", "- IL-8", "- IP-10", "- IL-1RA", "- sCD163", "- sTREM-1", "- Ferritin", "- CRP", "AIC of the selected model") knitr::kable(out2) ``` | | Best of all combinations | Best combination of 2 variables | Best combination of 3 variables | Best combination of 4 variables | Best combination of 5 variables | |:--------------------------|:-------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------| | \- VCAM-1 | | | | | | | \- SDC-1 | \+ | | | | | | \- Ang-2 | \+ | | \+ | \+ | \+ | | \- IL-8 | \+ | | | | \+ | | \- IP-10 | \+ | | | \+ | \+ | | \- IL-1RA | \+ | \+ | \+ | \+ | \+ | | \- sCD163 | | | | | | | \- sTREM-1 | | | | | | | \- Ferritin | \+ | \+ | \+ | \+ | \+ | | \- CRP | | | | | | | AIC of the selected model | 465.9 | 484.7 | 480.0 | 473.7 | 467.6 | ``` r # For bootstrap results please look at Appendix 7-tables 1, 2 (for the best of all combinations) # and the bootstrap codes for the best combinations of 2, 3, 4, and 5 variables ``` ########################################################################################### #### Table 4. Best combinations of biomarkers associated with severe or moderate dengue for adults. ``` r # EPV -------------------------------------------------------------- pred <- c("VCAM", "SDC", "Ang", "IL8", "ns1(IP10)", "IL1RA", "CD163", "TREM", "Fer", "CRP") # Estimate full model ---------------------------------------------- full_mod <- glm(sev.or.inte ~ VCAM + SDC + Ang + IL8 + ns1(IP10) + IL1RA + CD163 + TREM + Fer + CRP, family=binomial, data=dat1a, x=T, y=T) # Selected model --------------------------------------------------- sel_var <- matrix(0, ncol=length(pred)+1, nrow=5, dimnames=list(NULL, c(pred, "aic"))) for (i in 1:5) { if (i==1) { bs <- dredge(full_mod, rank="AIC") } else { bs <- dredge(full_mod, rank="AIC", m.lim=c(i,i)) } bs_var <- attr(get.models(bs, 1)[[1]]$terms, "term.labels") for (j in 1:(ncol(sel_var)-1)) {sel_var[i,j] <- ifelse(names(sel_var[i,j]) %in% bs_var, 1, 0)} formula <- paste("sev.or.inte~", paste(names(sel_var[i,][sel_var[i,]==1]), collapse = "+")) sel_mod <- glm(formula, data = dat1a, family = binomial, x = T, y = T) sel_var[i, ncol(sel_var)] <- AIC(sel_mod) } # Report results -------------------------------------------------- out1 <- as.data.frame(sel_var) %>% mutate(AIC = round(aic,1)) %>% select(-aic) for (i in 1:(ncol(out1)-1)) {out1[,i] <- ifelse(out1[,i]==0, NA, "+")} out2 <- as.data.frame(t(out1)) colnames(out2) <- c("Best of all combinations", "Best combination of 2 variables", "Best combination of 3 variables", "Best combination of 4 variables", "Best combination of 5 variables") rownames(out2) <- c("- VCAM-1", "- SDC-1", "- Ang-2", "- IL-8", "- IP-10", "- IL-1RA", "- sCD163", "- sTREM-1", "- Ferritin", "- CRP", "AIC of the selected model") knitr::kable(out2) ``` | | Best of all combinations | Best combination of 2 variables | Best combination of 3 variables | Best combination of 4 variables | Best combination of 5 variables | |:--------------------------|:-------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------| | \- VCAM-1 | | | | | | | \- SDC-1 | \+ | \+ | \+ | \+ | \+ | | \- Ang-2 | | | | | | | \- IL-8 | \+ | \+ | \+ | \+ | \+ | | \- IP-10 | \+ | | | | | | \- IL-1RA | \+ | | | \+ | \+ | | \- sCD163 | \+ | | | | | | \- sTREM-1 | \+ | | | | \+ | | \- Ferritin | \+ | | \+ | \+ | \+ | | \- CRP | | | | | | | AIC of the selected model | 430.5 | 441.1 | 434.2 | 431.6 | 430.7 | ``` r # For bootstrap results please look at Appendix 7-tables 3, 4 (for the best of all combinations) # and the bootstrap codes for the best combinations of 2, 3, 4, and 5 variables ``` ########################################################################################### #### Appendix 3. Statistical analysis. Analysis to find the best combination of biomarkers to predict the primary endpoint (aim \#2) - Step \#1 - AICs of models ``` r # For children screen_child <- data.frame(bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP")) %>% group_by(bio) %>% do(cbind(., model1_child = screen(mod=1, bio=.$bio, dat=dat1c), model2_child = screen(mod=2, bio=.$bio, dat=dat1c), model3_child = screen(mod=3, bio=.$bio, dat=dat1c), model4_child = screen(mod=4, bio=.$bio, dat=dat1c))) %>% ungroup() %>% mutate(bio = factor(bio, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP"))) %>% arrange(bio) # For adults screen_adult <- data.frame(bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP")) %>% group_by(bio) %>% do(cbind(., model1_adult = screen(mod=1, bio=.$bio, dat=dat1a), model2_adult = screen(mod=2, bio=.$bio, dat=dat1a), model3_adult = screen(mod=3, bio=.$bio, dat=dat1a), model4_adult = screen(mod=4, bio=.$bio, dat=dat1a))) %>% ungroup() %>% mutate(bio = factor(bio, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP"))) %>% arrange(bio) # Combine results out <- full_join(screen_child, screen_adult, by="bio") knitr::kable(out) ``` | bio | model1\_child | model2\_child | model3\_child | model4\_child | model1\_adult | model2\_adult | model3\_adult | model4\_adult | |:------|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:| | VCAM | 530.8 | 531.3 | 530.6 | 532.6 | 499.8 | 496.7 | 501.3 | 496.4 | | SDC | 537.9 | 538.0 | | | 459.5 | 461.0 | | | | Ang | 511.0 | 509.6 | 512.5 | 509.7 | 493.5 | 490.8 | 493.3 | 492.8 | | IL8 | 548.5 | 549.0 | 548.1 | 546.8 | 457.7 | 457.8 | 456.5 | 457.8 | | IP10 | 521.2 | 517.2 | 523.0 | 518.3 | 500.7 | 492.6 | 502.5 | 494.5 | | IL1RA | 492.9 | 494.9 | | | 493.5 | 494.2 | | | | CD163 | 531.9 | 533.2 | 531.9 | 533.2 | 497.6 | 499.3 | 499.5 | 501.1 | | TREM | 545.7 | 544.2 | 545.6 | 545.4 | 507.8 | 509.5 | 507.0 | 505.0 | | Fer | 542.6 | 544.4 | | | 509.3 | 505.2 | | | | CRP | 536.7 | 536.4 | | | 505.1 | 506.8 | | | ########################################################################################### #### Appendix 4—table 1. Summary of clinical phenotype of the primary endpoint. ``` r dat %>% filter(sev.or.inte == 1) %>% mutate(age15 = factor(age15, levels = c("No", "Yes"), labels = c("Children", "Adults")), mod.only = sev.or.inte - sev.only) %>% # to create 'moderate dengue' select(age15, sev.only, sev.leak, shock, res.dis, sev.neu, sev.bleed, sev.other, sev.liver, mod.only, mod.leak, mod.liver, mod.bleed, mod.other, mod.neu) %>% tbl_summary(by = age15, statistic = list(all_categorical() ~ "{n} ({p})"), label = list(sev.only ~ "Severe dengue, n (%)", sev.leak ~ "- Severe plasma leakage", shock ~ " + Dengue shock syndrome", res.dis ~ " + Respiratory distress", sev.neu ~ "- Severe neurologic involvement", sev.bleed ~ "- Severe bleeding", sev.other ~ "- Severe other major organ failure", sev.liver ~ "- Severe hepatic involvement", mod.only ~ "Moderate dengue, n (%)", mod.leak ~ "- Moderate plasma leakage", mod.liver ~ "- Moderate hepatic involvement", mod.bleed ~ "- Moderate bleeding", mod.other ~ "- Moderate other major organ involvement", mod.neu ~ "- Moderate neurologic involvement")) %>% add_overall() %>% modify_header(label = "", stat_0 = "**All patients (N={N})**", stat_by = "**{level} (N={n})**") %>% modify_footnote(update = everything() ~ NA) ``` <div id="nhaxycjzdb" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;"> <style>html { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif; } #nhaxycjzdb .gt_table { display: table; border-collapse: collapse; margin-left: auto; margin-right: auto; color: #333333; font-size: 16px; font-weight: normal; font-style: normal; background-color: #FFFFFF; width: auto; border-top-style: solid; border-top-width: 2px; border-top-color: #A8A8A8; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #A8A8A8; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; } #nhaxycjzdb .gt_heading { background-color: #FFFFFF; text-align: center; border-bottom-color: #FFFFFF; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; } #nhaxycjzdb .gt_title { color: #333333; font-size: 125%; font-weight: initial; padding-top: 4px; padding-bottom: 4px; border-bottom-color: #FFFFFF; border-bottom-width: 0; } #nhaxycjzdb .gt_subtitle { color: #333333; font-size: 85%; font-weight: initial; padding-top: 0; padding-bottom: 4px; border-top-color: #FFFFFF; border-top-width: 0; } #nhaxycjzdb .gt_bottom_border { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; } #nhaxycjzdb .gt_col_headings { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; } #nhaxycjzdb .gt_col_heading { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 6px; padding-left: 5px; padding-right: 5px; overflow-x: hidden; } #nhaxycjzdb .gt_column_spanner_outer { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; padding-top: 0; padding-bottom: 0; padding-left: 4px; padding-right: 4px; } #nhaxycjzdb .gt_column_spanner_outer:first-child { padding-left: 0; } #nhaxycjzdb .gt_column_spanner_outer:last-child { padding-right: 0; } #nhaxycjzdb .gt_column_spanner { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 6px; overflow-x: hidden; display: inline-block; width: 100%; } #nhaxycjzdb .gt_group_heading { padding: 8px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; } #nhaxycjzdb .gt_empty_group_heading { padding: 0.5px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: middle; } #nhaxycjzdb .gt_from_md > :first-child { margin-top: 0; } #nhaxycjzdb .gt_from_md > :last-child { margin-bottom: 0; } #nhaxycjzdb .gt_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; margin: 10px; border-top-style: solid; border-top-width: 1px; border-top-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; overflow-x: hidden; } #nhaxycjzdb .gt_stub { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 12px; } #nhaxycjzdb .gt_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; } #nhaxycjzdb .gt_first_summary_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; } #nhaxycjzdb .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; } #nhaxycjzdb .gt_first_grand_summary_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; } #nhaxycjzdb .gt_striped { background-color: rgba(128, 128, 128, 0.05); } #nhaxycjzdb .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; } #nhaxycjzdb .gt_footnotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; } #nhaxycjzdb .gt_footnote { margin: 0px; font-size: 90%; padding: 4px; } #nhaxycjzdb .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; } #nhaxycjzdb .gt_sourcenote { font-size: 90%; padding: 4px; } #nhaxycjzdb .gt_left { text-align: left; } #nhaxycjzdb .gt_center { text-align: center; } #nhaxycjzdb .gt_right { text-align: right; font-variant-numeric: tabular-nums; } #nhaxycjzdb .gt_font_normal { font-weight: normal; } #nhaxycjzdb .gt_font_bold { font-weight: bold; } #nhaxycjzdb .gt_font_italic { font-style: italic; } #nhaxycjzdb .gt_super { font-size: 65%; } #nhaxycjzdb .gt_footnote_marks { font-style: italic; font-weight: normal; font-size: 65%; } </style> <table class="gt_table"> <thead class="gt_col_headings"> <tr> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>All patients (N=281)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Children (N=127)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Adults (N=154)</strong></th> </tr> </thead> <tbody class="gt_table_body"> <tr><td class="gt_row gt_left">Severe dengue, n (%)</td> <td class="gt_row gt_center">38 (14)</td> <td class="gt_row gt_center">29 (23)</td> <td class="gt_row gt_center">9 (6)</td></tr> <tr><td class="gt_row gt_left">- Severe plasma leakage</td> <td class="gt_row gt_center">33 (12)</td> <td class="gt_row gt_center">24 (19)</td> <td class="gt_row gt_center">9 (6)</td></tr> <tr><td class="gt_row gt_left"> + Dengue shock syndrome</td> <td class="gt_row gt_center">25 (9)</td> <td class="gt_row gt_center">18 (14)</td> <td class="gt_row gt_center">7 (5)</td></tr> <tr><td class="gt_row gt_left"> + Respiratory distress</td> <td class="gt_row gt_center">12 (4)</td> <td class="gt_row gt_center">9 (7)</td> <td class="gt_row gt_center">3 (2)</td></tr> <tr><td class="gt_row gt_left">- Severe neurologic involvement</td> <td class="gt_row gt_center">3 (1)</td> <td class="gt_row gt_center">3 (2)</td> <td class="gt_row gt_center">0 (0)</td></tr> <tr><td class="gt_row gt_left">- Severe bleeding</td> <td class="gt_row gt_center">2 (1)</td> <td class="gt_row gt_center">2 (2)</td> <td class="gt_row gt_center">0 (0)</td></tr> <tr><td class="gt_row gt_left">- Severe other major organ failure</td> <td class="gt_row gt_center">1 (0)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">1 (1)</td></tr> <tr><td class="gt_row gt_left">- Severe hepatic involvement</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">0 (0)</td></tr> <tr><td class="gt_row gt_left">Moderate dengue, n (%)</td> <td class="gt_row gt_center">243 (86)</td> <td class="gt_row gt_center">98 (77)</td> <td class="gt_row gt_center">145 (94)</td></tr> <tr><td class="gt_row gt_left">- Moderate plasma leakage</td> <td class="gt_row gt_center">159 (57)</td> <td class="gt_row gt_center">73 (57)</td> <td class="gt_row gt_center">86 (56)</td></tr> <tr><td class="gt_row gt_left">- Moderate hepatic involvement</td> <td class="gt_row gt_center">102 (36)</td> <td class="gt_row gt_center">35 (28)</td> <td class="gt_row gt_center">67 (44)</td></tr> <tr><td class="gt_row gt_left">- Moderate bleeding</td> <td class="gt_row gt_center">9 (3)</td> <td class="gt_row gt_center">3 (2)</td> <td class="gt_row gt_center">6 (4)</td></tr> <tr><td class="gt_row gt_left">- Moderate other major organ involvement</td> <td class="gt_row gt_center">1 (0)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">1 (1)</td></tr> <tr><td class="gt_row gt_left">- Moderate neurologic involvement</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">0 (0)</td> <td class="gt_row gt_center">0 (0)</td></tr> </tbody> </table> </div> ########################################################################################### #### Appendix 4—table 2. Summary of biomarkers’ data (at enrollment) ``` r # All patients t1 <- dat0 %>% filter(Time == "Enrolment") %>% mutate(viremia = Viremia/(10^6)) %>% # to summarize Viremia as 10^6 copies/ml select(group2, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP, viremia) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})"), digits = list(all_continuous() ~ c(0,0), viremia ~ c(1,1)), label = list(VCAM1 ~ "VCAM-1 (ng/ml)", SDC1 ~ "SDC-1 (pg/ml)", Ang2 ~ "Ang-2 (pg/ml)", IL8 ~ "IL-8 (pg/ml)", IP10 ~ "IP-10 (pg/ml)", IL1RA ~ "IL-1RA (pg/ml)", CD163 ~ "sCD163 (ng/ml)", TREM1 ~ "sTREM-1 (pg/ml)", Fer ~ "Ferritin (ng/ml)", CRP ~ "CRP (mg/l)", viremia ~ "Viremia (10^6 copies/ml)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") %>% modify_footnote(update = everything() ~ NA) # Children (<15 years of age) t2 <- dat0 %>% filter(Time == "Enrolment") %>% filter(age15 == "No") %>% mutate(viremia = Viremia/(10^6)) %>% # to summarize Viremia as 10^6 copies/ml select(group2, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP, viremia) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})"), digits = list(all_continuous() ~ c(0,0), viremia ~ c(1,1)), label = list(VCAM1 ~ "VCAM-1 (ng/ml)", SDC1 ~ "SDC-1 (pg/ml)", Ang2 ~ "Ang-2 (pg/ml)", IL8 ~ "IL-8 (pg/ml)", IP10 ~ "IP-10 (pg/ml)", IL1RA ~ "IL-1RA (pg/ml)", CD163 ~ "sCD163 (ng/ml)", TREM1 ~ "sTREM-1 (pg/ml)", Fer ~ "Ferritin (ng/ml)", CRP ~ "CRP (mg/l)", viremia ~ "Viremia (10^6 copies/ml)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") %>% modify_footnote(update = everything() ~ NA) # Adults (>=15 years of age) t3 <- dat0 %>% filter(Time == "Enrolment") %>% filter(age15 == "Yes") %>% mutate(viremia = Viremia/(10^6)) %>% # to summarize Viremia as 10^6 copies/ml select(group2, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP, viremia) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})"), digits = list(all_continuous() ~ c(0,0), viremia ~ c(1,1)), label = list(VCAM1 ~ "VCAM-1 (ng/ml)", SDC1 ~ "SDC-1 (pg/ml)", Ang2 ~ "Ang-2 (pg/ml)", IL8 ~ "IL-8 (pg/ml)", IP10 ~ "IP-10 (pg/ml)", IL1RA ~ "IL-1RA (pg/ml)", CD163 ~ "sCD163 (ng/ml)", TREM1 ~ "sTREM-1 (pg/ml)", Fer ~ "Ferritin (ng/ml)", CRP ~ "CRP (mg/l)", viremia ~ "Viremia (10^6 copies/ml)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") %>% modify_footnote(update = everything() ~ NA) # Merge tables tbl_merge(tbls = list(t1, t2, t3), tab_spanner = c("**All patients**", "**Children**", "**Adults**")) ``` <div id="lqziuwxbxz" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;"> <style>html { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif; } #lqziuwxbxz .gt_table { display: table; border-collapse: collapse; margin-left: auto; margin-right: auto; color: #333333; font-size: 16px; font-weight: normal; font-style: normal; background-color: #FFFFFF; width: auto; border-top-style: solid; border-top-width: 2px; border-top-color: #A8A8A8; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #A8A8A8; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; } #lqziuwxbxz .gt_heading { background-color: #FFFFFF; text-align: center; border-bottom-color: #FFFFFF; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; } #lqziuwxbxz .gt_title { color: #333333; font-size: 125%; font-weight: initial; padding-top: 4px; padding-bottom: 4px; border-bottom-color: #FFFFFF; border-bottom-width: 0; } #lqziuwxbxz .gt_subtitle { color: #333333; font-size: 85%; font-weight: initial; padding-top: 0; padding-bottom: 4px; border-top-color: #FFFFFF; border-top-width: 0; } #lqziuwxbxz .gt_bottom_border { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; } #lqziuwxbxz .gt_col_headings { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; } #lqziuwxbxz .gt_col_heading { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 6px; padding-left: 5px; padding-right: 5px; overflow-x: hidden; } #lqziuwxbxz .gt_column_spanner_outer { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; padding-top: 0; padding-bottom: 0; padding-left: 4px; padding-right: 4px; } #lqziuwxbxz .gt_column_spanner_outer:first-child { padding-left: 0; } #lqziuwxbxz .gt_column_spanner_outer:last-child { padding-right: 0; } #lqziuwxbxz .gt_column_spanner { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 6px; overflow-x: hidden; display: inline-block; width: 100%; } #lqziuwxbxz .gt_group_heading { padding: 8px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; } #lqziuwxbxz .gt_empty_group_heading { padding: 0.5px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: middle; } #lqziuwxbxz .gt_from_md > :first-child { margin-top: 0; } #lqziuwxbxz .gt_from_md > :last-child { margin-bottom: 0; } #lqziuwxbxz .gt_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; margin: 10px; border-top-style: solid; border-top-width: 1px; border-top-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; overflow-x: hidden; } #lqziuwxbxz .gt_stub { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 12px; } #lqziuwxbxz .gt_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; } #lqziuwxbxz .gt_first_summary_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; } #lqziuwxbxz .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; } #lqziuwxbxz .gt_first_grand_summary_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; } #lqziuwxbxz .gt_striped { background-color: rgba(128, 128, 128, 0.05); } #lqziuwxbxz .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; } #lqziuwxbxz .gt_footnotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; } #lqziuwxbxz .gt_footnote { margin: 0px; font-size: 90%; padding: 4px; } #lqziuwxbxz .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; } #lqziuwxbxz .gt_sourcenote { font-size: 90%; padding: 4px; } #lqziuwxbxz .gt_left { text-align: left; } #lqziuwxbxz .gt_center { text-align: center; } #lqziuwxbxz .gt_right { text-align: right; font-variant-numeric: tabular-nums; } #lqziuwxbxz .gt_font_normal { font-weight: normal; } #lqziuwxbxz .gt_font_bold { font-weight: bold; } #lqziuwxbxz .gt_font_italic { font-style: italic; } #lqziuwxbxz .gt_super { font-size: 65%; } #lqziuwxbxz .gt_footnote_marks { font-style: italic; font-weight: normal; font-size: 65%; } </style> <table class="gt_table"> <thead class="gt_col_headings"> <tr> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1"></th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>All patients</strong></span> </th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>Children</strong></span> </th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>Adults</strong></span> </th> </tr> <tr> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=556)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=281)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=337)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=127)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=219)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=154)</strong></th> </tr> </thead> <tbody class="gt_table_body"> <tr><td class="gt_row gt_left">VCAM-1 (ng/ml)</td> <td class="gt_row gt_center">1,404 (540, 2,548)</td> <td class="gt_row gt_center">2,027 (1,122, 3,577)</td> <td class="gt_row gt_center">1,442 (447, 2,546)</td> <td class="gt_row gt_center">2,020 (1,232, 3,384)</td> <td class="gt_row gt_center">1,356 (568, 2,560)</td> <td class="gt_row gt_center">2,092 (1,060, 4,202)</td></tr> <tr><td class="gt_row gt_left">SDC-1 (pg/ml)</td> <td class="gt_row gt_center">2,334 (1,864, 3,131)</td> <td class="gt_row gt_center">2,997 (2,230, 4,201)</td> <td class="gt_row gt_center">2,369 (1,861, 3,423)</td> <td class="gt_row gt_center">2,846 (2,164, 4,173)</td> <td class="gt_row gt_center">2,260 (1,879, 2,898)</td> <td class="gt_row gt_center">3,122 (2,278, 4,211)</td></tr> <tr><td class="gt_row gt_left">Ang-2 (pg/ml)</td> <td class="gt_row gt_center">1,064 (550, 1,584)</td> <td class="gt_row gt_center">1,521 (899, 2,318)</td> <td class="gt_row gt_center">1,102 (584, 1,563)</td> <td class="gt_row gt_center">1,547 (967, 2,318)</td> <td class="gt_row gt_center">944 (516, 1,585)</td> <td class="gt_row gt_center">1,516 (885, 2,321)</td></tr> <tr><td class="gt_row gt_left">IL-8 (pg/ml)</td> <td class="gt_row gt_center">12 (8, 22)</td> <td class="gt_row gt_center">17 (11, 28)</td> <td class="gt_row gt_center">15 (9, 26)</td> <td class="gt_row gt_center">16 (10, 27)</td> <td class="gt_row gt_center">10 (7, 15)</td> <td class="gt_row gt_center">19 (12, 29)</td></tr> <tr><td class="gt_row gt_left">IP-10 (pg/ml)</td> <td class="gt_row gt_center">2,502 (732, 4,509)</td> <td class="gt_row gt_center">4,092 (2,436, 6,441)</td> <td class="gt_row gt_center">2,245 (458, 4,531)</td> <td class="gt_row gt_center">3,942 (2,046, 6,287)</td> <td class="gt_row gt_center">2,793 (1,370, 4,495)</td> <td class="gt_row gt_center">4,242 (2,524, 6,469)</td></tr> <tr><td class="gt_row gt_left">IL-1RA (pg/ml)</td> <td class="gt_row gt_center">5,237 (2,603, 9,082)</td> <td class="gt_row gt_center">9,105 (5,933, 14,977)</td> <td class="gt_row gt_center">4,491 (2,318, 8,977)</td> <td class="gt_row gt_center">9,688 (6,109, 16,786)</td> <td class="gt_row gt_center">5,721 (3,479, 9,703)</td> <td class="gt_row gt_center">8,993 (5,953, 12,935)</td></tr> <tr><td class="gt_row gt_left">sCD163 (ng/ml)</td> <td class="gt_row gt_center">278 (185, 447)</td> <td class="gt_row gt_center">322 (228, 503)</td> <td class="gt_row gt_center">326 (212, 481)</td> <td class="gt_row gt_center">386 (256, 603)</td> <td class="gt_row gt_center">226 (157, 374)</td> <td class="gt_row gt_center">291 (207, 410)</td></tr> <tr><td class="gt_row gt_left">sTREM-1 (pg/ml)</td> <td class="gt_row gt_center">81 (59, 114)</td> <td class="gt_row gt_center">96 (69, 132)</td> <td class="gt_row gt_center">80 (58, 115)</td> <td class="gt_row gt_center">93 (67, 128)</td> <td class="gt_row gt_center">84 (60, 114)</td> <td class="gt_row gt_center">98 (73, 134)</td></tr> <tr><td class="gt_row gt_left">Ferritin (ng/ml)</td> <td class="gt_row gt_center">233 (116, 406)</td> <td class="gt_row gt_center">261 (133, 433)</td> <td class="gt_row gt_center">177 (99, 324)</td> <td class="gt_row gt_center">224 (110, 402)</td> <td class="gt_row gt_center">303 (161, 510)</td> <td class="gt_row gt_center">278 (160, 448)</td></tr> <tr><td class="gt_row gt_left">CRP (mg/l)</td> <td class="gt_row gt_center">25 (10, 54)</td> <td class="gt_row gt_center">34 (17, 72)</td> <td class="gt_row gt_center">18 (7, 41)</td> <td class="gt_row gt_center">24 (13, 58)</td> <td class="gt_row gt_center">38 (17, 65)</td> <td class="gt_row gt_center">45 (25, 80)</td></tr> <tr><td class="gt_row gt_left">Viremia (10^6 copies/ml)</td> <td class="gt_row gt_center">15.8 (0.7, 148.5)</td> <td class="gt_row gt_center">79.2 (5.3, 582.7)</td> <td class="gt_row gt_center">21.8 (1.8, 167.0)</td> <td class="gt_row gt_center">105.4 (8.4, 646.0)</td> <td class="gt_row gt_center">9.8 (0.3, 115.5)</td> <td class="gt_row gt_center">56.2 (3.6, 496.0)</td></tr> </tbody> </table> </div> #### Appendix 4—table 2. Summary of biomarkers’ data (at follow-up) ``` r # All patients t1 <- dat0 %>% filter(Time == "Follow up") %>% select(group2, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})"), digits = list(all_continuous() ~ c(0,0), c(IL8, CRP) ~ c(1,1)), label = list(VCAM1 ~ "VCAM-1 (ng/ml)", SDC1 ~ "SDC-1 (pg/ml)", Ang2 ~ "Ang-2 (pg/ml)", IL8 ~ "IL-8 (pg/ml)", IP10 ~ "IP-10 (pg/ml)", IL1RA ~ "IL-1RA (pg/ml)", CD163 ~ "sCD163 (ng/ml)", TREM1 ~ "sTREM-1 (pg/ml)", Fer ~ "Ferritin (ng/ml)", CRP ~ "CRP (mg/l)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") %>% modify_footnote(update = everything() ~ NA) # Children (<15 years of age) t2 <- dat0 %>% filter(Time == "Follow up") %>% filter(age15 == "No") %>% select(group2, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})"), digits = list(all_continuous() ~ c(0,0), c(IL8, CRP) ~ c(1,1)), label = list(VCAM1 ~ "VCAM-1 (ng/ml)", SDC1 ~ "SDC-1 (pg/ml)", Ang2 ~ "Ang-2 (pg/ml)", IL8 ~ "IL-8 (pg/ml)", IP10 ~ "IP-10 (pg/ml)", IL1RA ~ "IL-1RA (pg/ml)", CD163 ~ "sCD163 (ng/ml)", TREM1 ~ "sTREM-1 (pg/ml)", Fer ~ "Ferritin (ng/ml)", CRP ~ "CRP (mg/l)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") %>% modify_footnote(update = everything() ~ NA) # Adults (>=15 years of age) t3 <- dat0 %>% filter(Time == "Follow up") %>% filter(age15 == "Yes") %>% select(group2, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP) %>% tbl_summary(by = group2, statistic = list(all_continuous() ~ "{median} ({p25}, {p75})"), digits = list(all_continuous() ~ c(0,0), c(IL8, CRP) ~ c(1,1)), label = list(VCAM1 ~ "VCAM-1 (ng/ml)", SDC1 ~ "SDC-1 (pg/ml)", Ang2 ~ "Ang-2 (pg/ml)", IL8 ~ "IL-8 (pg/ml)", IP10 ~ "IP-10 (pg/ml)", IL1RA ~ "IL-1RA (pg/ml)", CD163 ~ "sCD163 (ng/ml)", TREM1 ~ "sTREM-1 (pg/ml)", Fer ~ "Ferritin (ng/ml)", CRP ~ "CRP (mg/l)")) %>% modify_header(label = "", stat_by = "**{level} (N={n})**") %>% modify_footnote(update = everything() ~ NA) # Merge tables tbl_merge(tbls = list(t1, t2, t3), tab_spanner = c("**All patients**", "**Children**", "**Adults**")) ``` <div id="aerqfvnzyi" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;"> <style>html { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif; } #aerqfvnzyi .gt_table { display: table; border-collapse: collapse; margin-left: auto; margin-right: auto; color: #333333; font-size: 16px; font-weight: normal; font-style: normal; background-color: #FFFFFF; width: auto; border-top-style: solid; border-top-width: 2px; border-top-color: #A8A8A8; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #A8A8A8; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; } #aerqfvnzyi .gt_heading { background-color: #FFFFFF; text-align: center; border-bottom-color: #FFFFFF; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; } #aerqfvnzyi .gt_title { color: #333333; font-size: 125%; font-weight: initial; padding-top: 4px; padding-bottom: 4px; border-bottom-color: #FFFFFF; border-bottom-width: 0; } #aerqfvnzyi .gt_subtitle { color: #333333; font-size: 85%; font-weight: initial; padding-top: 0; padding-bottom: 4px; border-top-color: #FFFFFF; border-top-width: 0; } #aerqfvnzyi .gt_bottom_border { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; } #aerqfvnzyi .gt_col_headings { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; } #aerqfvnzyi .gt_col_heading { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 6px; padding-left: 5px; padding-right: 5px; overflow-x: hidden; } #aerqfvnzyi .gt_column_spanner_outer { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; padding-top: 0; padding-bottom: 0; padding-left: 4px; padding-right: 4px; } #aerqfvnzyi .gt_column_spanner_outer:first-child { padding-left: 0; } #aerqfvnzyi .gt_column_spanner_outer:last-child { padding-right: 0; } #aerqfvnzyi .gt_column_spanner { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 6px; overflow-x: hidden; display: inline-block; width: 100%; } #aerqfvnzyi .gt_group_heading { padding: 8px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; } #aerqfvnzyi .gt_empty_group_heading { padding: 0.5px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: middle; } #aerqfvnzyi .gt_from_md > :first-child { margin-top: 0; } #aerqfvnzyi .gt_from_md > :last-child { margin-bottom: 0; } #aerqfvnzyi .gt_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; margin: 10px; border-top-style: solid; border-top-width: 1px; border-top-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; overflow-x: hidden; } #aerqfvnzyi .gt_stub { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 12px; } #aerqfvnzyi .gt_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; } #aerqfvnzyi .gt_first_summary_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; } #aerqfvnzyi .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; } #aerqfvnzyi .gt_first_grand_summary_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; } #aerqfvnzyi .gt_striped { background-color: rgba(128, 128, 128, 0.05); } #aerqfvnzyi .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; } #aerqfvnzyi .gt_footnotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; } #aerqfvnzyi .gt_footnote { margin: 0px; font-size: 90%; padding: 4px; } #aerqfvnzyi .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; } #aerqfvnzyi .gt_sourcenote { font-size: 90%; padding: 4px; } #aerqfvnzyi .gt_left { text-align: left; } #aerqfvnzyi .gt_center { text-align: center; } #aerqfvnzyi .gt_right { text-align: right; font-variant-numeric: tabular-nums; } #aerqfvnzyi .gt_font_normal { font-weight: normal; } #aerqfvnzyi .gt_font_bold { font-weight: bold; } #aerqfvnzyi .gt_font_italic { font-style: italic; } #aerqfvnzyi .gt_super { font-size: 65%; } #aerqfvnzyi .gt_footnote_marks { font-style: italic; font-weight: normal; font-size: 65%; } </style> <table class="gt_table"> <thead class="gt_col_headings"> <tr> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1"></th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>All patients</strong></span> </th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>Children</strong></span> </th> <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2"> <span class="gt_column_spanner"><strong>Adults</strong></span> </th> </tr> <tr> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=437)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=231)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=292)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=112)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Uncomplicated dengue (N=145)</strong></th> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Severe/moderate dengue (N=119)</strong></th> </tr> </thead> <tbody class="gt_table_body"> <tr><td class="gt_row gt_left">VCAM-1 (ng/ml)</td> <td class="gt_row gt_center">402 (102, 730)</td> <td class="gt_row gt_center">686 (344, 961)</td> <td class="gt_row gt_center">579 (182, 858)</td> <td class="gt_row gt_center">782 (402, 1,078)</td> <td class="gt_row gt_center">173 (26, 388)</td> <td class="gt_row gt_center">622 (343, 835)</td></tr> <tr><td class="gt_row gt_left">SDC-1 (pg/ml)</td> <td class="gt_row gt_center">2,769 (2,298, 3,514)</td> <td class="gt_row gt_center">3,417 (2,815, 5,495)</td> <td class="gt_row gt_center">2,957 (2,319, 4,115)</td> <td class="gt_row gt_center">3,122 (2,748, 5,507)</td> <td class="gt_row gt_center">2,666 (2,196, 3,058)</td> <td class="gt_row gt_center">3,745 (2,971, 5,495)</td></tr> <tr><td class="gt_row gt_left">Ang-2 (pg/ml)</td> <td class="gt_row gt_center">953 (478, 1,479)</td> <td class="gt_row gt_center">1,155 (675, 1,567)</td> <td class="gt_row gt_center">1,163 (738, 1,646)</td> <td class="gt_row gt_center">1,352 (710, 1,856)</td> <td class="gt_row gt_center">565 (302, 923)</td> <td class="gt_row gt_center">1,044 (626, 1,345)</td></tr> <tr><td class="gt_row gt_left">IL-8 (pg/ml)</td> <td class="gt_row gt_center">4.9 (2.3, 12.4)</td> <td class="gt_row gt_center">5.7 (2.7, 10.4)</td> <td class="gt_row gt_center">6.8 (3.0, 15.1)</td> <td class="gt_row gt_center">5.9 (2.4, 10.5)</td> <td class="gt_row gt_center">2.7 (1.6, 4.8)</td> <td class="gt_row gt_center">5.5 (3.1, 10.4)</td></tr> <tr><td class="gt_row gt_left">IP-10 (pg/ml)</td> <td class="gt_row gt_center">57 (24, 91)</td> <td class="gt_row gt_center">76 (47, 133)</td> <td class="gt_row gt_center">67 (33, 98)</td> <td class="gt_row gt_center">86 (38, 143)</td> <td class="gt_row gt_center">39 (22, 70)</td> <td class="gt_row gt_center">75 (48, 108)</td></tr> <tr><td class="gt_row gt_left">IL-1RA (pg/ml)</td> <td class="gt_row gt_center">412 (279, 635)</td> <td class="gt_row gt_center">455 (328, 626)</td> <td class="gt_row gt_center">441 (323, 687)</td> <td class="gt_row gt_center">501 (352, 664)</td> <td class="gt_row gt_center">336 (210, 480)</td> <td class="gt_row gt_center">407 (308, 615)</td></tr> <tr><td class="gt_row gt_left">sCD163 (ng/ml)</td> <td class="gt_row gt_center">337 (216, 553)</td> <td class="gt_row gt_center">412 (257, 661)</td> <td class="gt_row gt_center">340 (226, 562)</td> <td class="gt_row gt_center">456 (279, 680)</td> <td class="gt_row gt_center">328 (199, 523)</td> <td class="gt_row gt_center">386 (241, 589)</td></tr> <tr><td class="gt_row gt_left">sTREM-1 (pg/ml)</td> <td class="gt_row gt_center">99 (73, 132)</td> <td class="gt_row gt_center">90 (68, 116)</td> <td class="gt_row gt_center">98 (72, 132)</td> <td class="gt_row gt_center">91 (67, 115)</td> <td class="gt_row gt_center">101 (73, 134)</td> <td class="gt_row gt_center">90 (70, 116)</td></tr> <tr><td class="gt_row gt_left">Ferritin (ng/ml)</td> <td class="gt_row gt_center">202 (120, 309)</td> <td class="gt_row gt_center">273 (181, 382)</td> <td class="gt_row gt_center">177 (112, 263)</td> <td class="gt_row gt_center">209 (154, 311)</td> <td class="gt_row gt_center">267 (160, 404)</td> <td class="gt_row gt_center">322 (247, 436)</td></tr> <tr><td class="gt_row gt_left">CRP (mg/l)</td> <td class="gt_row gt_center">0.7 (0.3, 1.8)</td> <td class="gt_row gt_center">0.8 (0.4, 2.0)</td> <td class="gt_row gt_center">0.6 (0.3, 1.3)</td> <td class="gt_row gt_center">0.6 (0.3, 1.1)</td> <td class="gt_row gt_center">1.1 (0.5, 2.7)</td> <td class="gt_row gt_center">1.1 (0.5, 3.4)</td></tr> <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Unknown</td> <td class="gt_row gt_center">1</td> <td class="gt_row gt_center">3</td> <td class="gt_row gt_center">0</td> <td class="gt_row gt_center">1</td> <td class="gt_row gt_center">1</td> <td class="gt_row gt_center">2</td></tr> </tbody> </table> </div> ########################################################################################### #### Appendix 4-figure 1. Biomarker levels by individual. ``` r tick <- c(0,1,10,40,100,200,400,1000,4000,10000,20000,40000,70000) # for y-axis tick labels p41 <- ggplot(dat_plot, aes(Day, Result^(1/4), color=group2)) + geom_point(alpha = 0.5, size = .5) + geom_line(aes(group = Code), alpha = 0.07) + facet_wrap(~ Biomarker, scales="free", ncol=5) + scale_y_continuous(breaks=tick^(1/4), labels=tick) + scale_x_continuous(breaks = c(1,3,10,15,20,25,30), limit = c(1,31), name = "Illness day") + theme_bw() + theme(axis.title.y=element_blank(), legend.position="top", legend.title=element_blank(), axis.text.y=element_text(size=rel(.8))) p41 ``` <!-- --> ``` r #ggsave(filename="A4_fig1.pdf", plot=p41, width=8.5, height=6.2) ``` ########################################################################################### #### Appendix 4—figure 2. Pairwise correlation of biomarker levels at enrollment and age. ``` r collabel <- c("Age", "VCAM-1", "SDC-1", "Ang-2", "IL-8", "IP-10", "IL-1RA", "sCD163", "sTREM-1", "Ferritin", "CRP", "Viremia") # Change biomarker's values to log-2 and viremia to log dat_fig42 <- dat %>% mutate(log2_VCAM1 = as.numeric(log2(VCAM1)), log2_SDC1 = as.numeric(log2(SDC1)), log2_Ang2 = as.numeric(log2(Ang2)), log2_IL8 = as.numeric(log2(IL8)), log2_IP10 = as.numeric(log2(IP10)), log2_IL1RA = as.numeric(log2(IL1RA)), log2_CD163 = as.numeric(log2(CD163)), log2_TREM1 = as.numeric(log2(TREM1)), log2_Fer = as.numeric(log2(Fer)), log2_CRP = as.numeric(log2(CRP)), log_vir = as.numeric(log10(Viremia))) %>% select(Age, log2_VCAM1, log2_SDC1, log2_Ang2, log2_IL8, log2_IP10, log2_IL1RA, log2_CD163, log2_TREM1, log2_Fer, log2_CRP, log_vir) %>% as.data.frame(.) p42 <- GGally::ggpairs(dat_fig42, lower = list(continuous = GGscatterPlot), # GGscatterPlot is a customized function upper = "blank", columnLabels = collabel) + theme(panel.grid.minor = element_blank()) p42 ``` <!-- --> ``` r #ggsave(filename="A4_fig2.pdf", plot=p42, width=9.5, height=9.5) ``` ########################################################################################### #### Appendix 5—figure 1. Results from single models for severe/moderate dengue with the interaction with serotype. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # For DENV-1 dd$limits["Adjust to","Serotype"] <- "DENV-1" ## For children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred1s(out="sev.or.inte", bio="VCAM", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m2 <- get_pred1s(out="sev.or.inte", bio="SDC", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m3 <- get_pred1s(out="sev.or.inte", bio="Ang", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m4 <- get_pred1s(out="sev.or.inte", bio="IL8", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m5 <- get_pred1s(out="sev.or.inte", bio="IP10", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m6 <- get_pred1s(out="sev.or.inte", bio="IL1RA", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m7 <- get_pred1s(out="sev.or.inte", bio="CD163", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m8 <- get_pred1s(out="sev.or.inte", bio="TREM", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m9 <- get_pred1s(out="sev.or.inte", bio="Fer", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m10 <- get_pred1s(out="sev.or.inte", bio="CRP", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") ## For adults dd$limits["Adjust to","Age"] <- 25 dat_mg1 <- get_pred1s(out="sev.or.inte", bio="VCAM", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg2 <- get_pred1s(out="sev.or.inte", bio="SDC", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg3 <- get_pred1s(out="sev.or.inte", bio="Ang", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg4 <- get_pred1s(out="sev.or.inte", bio="IL8", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg5 <- get_pred1s(out="sev.or.inte", bio="IP10", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg6 <- get_pred1s(out="sev.or.inte", bio="IL1RA", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg7 <- get_pred1s(out="sev.or.inte", bio="CD163", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg8 <- get_pred1s(out="sev.or.inte", bio="TREM", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg9 <- get_pred1s(out="sev.or.inte", bio="Fer", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg10 <- get_pred1s(out="sev.or.inte", bio="CRP", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_p1 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(serotype = "DENV-1") # For others dd$limits["Adjust to","Serotype"] <- "Others" ## For children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred1s(out="sev.or.inte", bio="VCAM", age=10, serotype="Others", dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m2 <- get_pred1s(out="sev.or.inte", bio="SDC", age=10, serotype="Others", dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m3 <- get_pred1s(out="sev.or.inte", bio="Ang", age=10, serotype="Others", dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m4 <- get_pred1s(out="sev.or.inte", bio="IL8", age=10, serotype="Others", dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m5 <- get_pred1s(out="sev.or.inte", bio="IP10", age=10, serotype="Others", dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m6 <- get_pred1s(out="sev.or.inte", bio="IL1RA", age=10, serotype="Others", dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m7 <- get_pred1s(out="sev.or.inte", bio="CD163", age=10, serotype="Others", dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m8 <- get_pred1s(out="sev.or.inte", bio="TREM", age=10, serotype="Others", dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m9 <- get_pred1s(out="sev.or.inte", bio="Fer", age=10, serotype="Others", dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m10 <- get_pred1s(out="sev.or.inte", bio="CRP", age=10, serotype="Others", dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") ## For adults dd$limits["Adjust to","Age"] <- 25 dat_mg1 <- get_pred1s(out="sev.or.inte", bio="VCAM", age=25, serotype="Others", dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg2 <- get_pred1s(out="sev.or.inte", bio="SDC", age=25, serotype="Others", dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg3 <- get_pred1s(out="sev.or.inte", bio="Ang", age=25, serotype="Others", dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg4 <- get_pred1s(out="sev.or.inte", bio="IL8", age=25, serotype="Others", dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg5 <- get_pred1s(out="sev.or.inte", bio="IP10", age=25, serotype="Others", dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg6 <- get_pred1s(out="sev.or.inte", bio="IL1RA", age=25, serotype="Others", dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg7 <- get_pred1s(out="sev.or.inte", bio="CD163", age=25, serotype="Others", dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg8 <- get_pred1s(out="sev.or.inte", bio="TREM", age=25, serotype="Others", dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg9 <- get_pred1s(out="sev.or.inte", bio="Fer", age=25, serotype="Others", dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg10 <- get_pred1s(out="sev.or.inte", bio="CRP", age=25, serotype="Others", dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_p2 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(serotype = "Others") # Merge data for children and adults vis1 <- rbind(dat_p1, dat_p2) %>% mutate(outcome = "outcome 1") # Merge data for plots tmp0 <- dat1 %>% filter(!is.na(Serotype)) %>% arrange(Serotype) %>% select(sev.or.inte) tmp <- data.frame(sev.or.inte = rep(tmp0$sev.or.inte, 20)) tmp_p1 <- vis1 %>% arrange(biomarker, age) %>% mutate(value1 = 2^value, serotype = factor(serotype, levels=c("DENV-1", "Others")), age = factor(age, levels=c("Children", "Adults"))) %>% bind_cols(., tmp) vline <- tmp_p1 %>% filter(yhat==0) %>% select(biomarker, value, value1) %>% rename(vline=value, vline1=value1) dat_p <- left_join(tmp_p1, vline, by="biomarker") # Alternative data to limit to 5th - 95th of the values dat_alt <- dat_p %>% group_by(biomarker, model) %>% mutate(up = quantile(value, .95), lo = quantile(value, .05)) %>% ungroup() %>% mutate(is.outlier = value<lo | value>up | value<0, value = ifelse(is.outlier, NA, value), value1 = ifelse(is.outlier, NA, value1), yhat = ifelse(is.outlier, NA, yhat), lower = ifelse(is.outlier, NA, lower), upper = ifelse(is.outlier, NA, upper)) dat_1_5 <- dat_alt %>% filter(biomarker %in% c("VCAM", "SDC", "Ang", "IL8", "IP10")) %>% mutate(biomarker = factor(biomarker, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10"))) dat_6_10 <- dat_alt %>% filter(biomarker %in% c("IL1RA", "CD163", "TREM", "Fer", "CRP")) %>% mutate(biomarker = factor(biomarker, levels=c("IL1RA", "CD163", "TREM", "Fer", "CRP"))) # Modify facets' scales #require(facetscales) xVCAM <- c(1,4,15,60,250,1000,4000) xSDC <- c(1400,2000,2800,4000,5600) xAng <- c(50,100,250,500,1000,2000) xIL8 <- c(5,7,10,14,20,28,40) xIP10 <- c(25,100,400,1600,6400) xIL1RA <- c(1000,2000,4000,8000,16000) xCD163 <- c(75,150,300,600) xTREM <- c(35,50,70,100,140,200) xFer <- c(50,100,200,400,800) xCRP <- c(2.5,5,10,20,40,80) scales_x <- list( `VCAM` = scale_x_continuous(breaks = log2(xVCAM), labels = xVCAM), `SDC` = scale_x_continuous(breaks = log2(xSDC), labels = xSDC), `Ang` = scale_x_continuous(breaks = log2(xAng), labels = xAng), `IL8` = scale_x_continuous(breaks = log2(xIL8), labels = xIL8), `IP10` = scale_x_continuous(breaks = log2(xIP10), labels = xIP10), `IL1RA` = scale_x_continuous(breaks = log2(xIL1RA), labels = xIL1RA), `CD163` = scale_x_continuous(breaks = log2(xCD163), labels = xCD163), `TREM` = scale_x_continuous(breaks = log2(xTREM), labels = xTREM), `Fer` = scale_x_continuous(breaks = log2(xFer), labels = xFer), `CRP` = scale_x_continuous(breaks = log2(xCRP), labels = xCRP) ) ybreak1 <- c(.125, .25, .5, 1, 2, 4, 8) ybreak2 <- c(.125, .25, .5, 1, 2, 4) scales_y1 <- list( `Children` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1), `Adults` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1) ) scales_y2 <- list( `Children` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2), `Adults` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2) ) # Set facets' names age <- c(`Children` = "Children", `Adults` = "Adults") biomarkers <- c(`VCAM` = "VCAM-1 (ng/ml)", `SDC`= "SDC-1 (pg/ml)", `Ang` = "Ang-2 (pg/ml)", `IL8` = "IL-8 (pg/ml)", `IP10` = "IP-10 (pg/ml)", `IL1RA` = "IL-1RA (pg/ml)", `CD163` = "sCD163 (ng/ml)", `TREM` = "sTREM-1 (pg/ml)", `Fer` = "Ferritin (ng/ml)", `CRP` = "CRP (mg/l)") # Plot for the first 5 biomarkers p.1.5 <- ggplot(dat_1_5, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=serotype), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=serotype), size=.5, alpha=.7) + geom_rug(data=filter(dat_1_5, age=="Children" & sev.or.inte==0 & serotype=="DENV-1"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, age=="Children" & sev.or.inte==1 & serotype=="DENV-1"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, age=="Adults" & sev.or.inte==0 & serotype=="Others"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_1_5, age=="Adults" & sev.or.inte==1 & serotype=="Others"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("DENV-1","Others")) + scale_fill_manual(values=c("red","blue"), labels=c("DENV-1","Others")) + ylab("Odds ratio") + theme(legend.position="top", legend.title=element_blank(), axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(age), scales=list(x=scales_x, y=scales_y1), labeller = as_labeller(c(age, biomarkers))) # Plot for the last 5 biomarkers p.6.10 <- ggplot(dat_6_10, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=serotype), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=serotype), size=.5, alpha=.7) + geom_rug(data=filter(dat_6_10, age=="Children" & sev.or.inte==0 & serotype=="DENV-1"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, age=="Children" & sev.or.inte==1 & serotype=="DENV-1"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, age=="Adults" & sev.or.inte==0 & serotype=="Others"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_6_10, age=="Adults" & sev.or.inte==1 & serotype=="Others"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("DENV-1","Others")) + scale_fill_manual(values=c("red","blue"), labels=c("DENV-1","Others")) + ylab("Odds ratio") + theme(legend.position="none", axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(age), scales=list(x=scales_x, y=scales_y2), labeller = as_labeller(c(age, biomarkers))) # Merge plots p51 <- gridExtra::grid.arrange(p.1.5, p.6.10, nrow=2, heights=c(1,.86)) ``` <!-- --> ``` r #ggsave(filename="A5_fig1.pdf", p3, dpi=300, width=8.5, height=6.2) ``` ########################################################################################### #### Appendix 5—figure 2. Results from global model for severe/moderate dengue with the interaction with serotype. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # For DENV-1 dd$limits["Adjust to","Serotype"] <- "DENV-1" ## For children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred2s(out="sev.or.inte", bio="VCAM", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m2 <- get_pred2s(out="sev.or.inte", bio="SDC", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m3 <- get_pred2s(out="sev.or.inte", bio="Ang", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m4 <- get_pred2s(out="sev.or.inte", bio="IL8", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m5 <- get_pred2s(out="sev.or.inte", bio="IP10", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m6 <- get_pred2s(out="sev.or.inte", bio="IL1RA", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m7 <- get_pred2s(out="sev.or.inte", bio="CD163", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m8 <- get_pred2s(out="sev.or.inte", bio="TREM", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m9 <- get_pred2s(out="sev.or.inte", bio="Fer", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") dat_m10 <- get_pred2s(out="sev.or.inte", bio="CRP", age=10, serotype="DENV-1", dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Children") ## For adults dd$limits["Adjust to","Age"] <- 25 dat_mg1 <- get_pred2s(out="sev.or.inte", bio="VCAM", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg2 <- get_pred2s(out="sev.or.inte", bio="SDC", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg3 <- get_pred2s(out="sev.or.inte", bio="Ang", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg4 <- get_pred2s(out="sev.or.inte", bio="IL8", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg5 <- get_pred2s(out="sev.or.inte", bio="IP10", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg6 <- get_pred2s(out="sev.or.inte", bio="IL1RA", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg7 <- get_pred2s(out="sev.or.inte", bio="CD163", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg8 <- get_pred2s(out="sev.or.inte", bio="TREM", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg9 <- get_pred2s(out="sev.or.inte", bio="Fer", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_mg10 <- get_pred2s(out="sev.or.inte", bio="CRP", age=25, serotype="DENV-1", dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='DENV-1')) %>% mutate(age="Adults") dat_p1 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(serotype = "DENV-1") # For others dd$limits["Adjust to","Serotype"] <- "Others" ## For children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred2s(out="sev.or.inte", bio="VCAM", age=10, serotype="Others", dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m2 <- get_pred2s(out="sev.or.inte", bio="SDC", age=10, serotype="Others", dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m3 <- get_pred2s(out="sev.or.inte", bio="Ang", age=10, serotype="Others", dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m4 <- get_pred2s(out="sev.or.inte", bio="IL8", age=10, serotype="Others", dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m5 <- get_pred2s(out="sev.or.inte", bio="IP10", age=10, serotype="Others", dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m6 <- get_pred2s(out="sev.or.inte", bio="IL1RA", age=10, serotype="Others", dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m7 <- get_pred2s(out="sev.or.inte", bio="CD163", age=10, serotype="Others", dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m8 <- get_pred2s(out="sev.or.inte", bio="TREM", age=10, serotype="Others", dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m9 <- get_pred2s(out="sev.or.inte", bio="Fer", age=10, serotype="Others", dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") dat_m10 <- get_pred2s(out="sev.or.inte", bio="CRP", age=10, serotype="Others", dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Children") ## For adults dd$limits["Adjust to","Age"] <- 25 dat_mg1 <- get_pred2s(out="sev.or.inte", bio="VCAM", age=25, serotype="Others", dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg2 <- get_pred2s(out="sev.or.inte", bio="SDC", age=25, serotype="Others", dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg3 <- get_pred2s(out="sev.or.inte", bio="Ang", age=25, serotype="Others", dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg4 <- get_pred2s(out="sev.or.inte", bio="IL8", age=25, serotype="Others", dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg5 <- get_pred2s(out="sev.or.inte", bio="IP10", age=25, serotype="Others", dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg6 <- get_pred2s(out="sev.or.inte", bio="IL1RA", age=25, serotype="Others", dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg7 <- get_pred2s(out="sev.or.inte", bio="CD163", age=25, serotype="Others", dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg8 <- get_pred2s(out="sev.or.inte", bio="TREM", age=25, serotype="Others", dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg9 <- get_pred2s(out="sev.or.inte", bio="Fer", age=25, serotype="Others", dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_mg10 <- get_pred2s(out="sev.or.inte", bio="CRP", age=25, serotype="Others", dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$Serotype=='Others')) %>% mutate(age="Adults") dat_p2 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(serotype = "Others") # Merge data for children and adults vis1 <- rbind(dat_p1, dat_p2) %>% mutate(outcome = "outcome 1") # Merge data for plots tmp0 <- dat1 %>% filter(!is.na(Serotype)) %>% arrange(Serotype) %>% select(sev.or.inte) tmp <- data.frame(sev.or.inte = rep(tmp0$sev.or.inte, 20)) tmp_p1 <- vis1 %>% arrange(biomarker, age) %>% mutate(value1 = 2^value, serotype = factor(serotype, levels=c("DENV-1", "Others")), age = factor(age, levels=c("Children", "Adults"))) %>% bind_cols(., tmp) vline <- tmp_p1 %>% filter(yhat==0) %>% select(biomarker, value, value1) %>% rename(vline=value, vline1=value1) dat_p <- left_join(tmp_p1, vline, by="biomarker") # Alternative data to limit to 5th - 95th of the values dat_alt <- dat_p %>% group_by(biomarker, model) %>% mutate(up = quantile(value, .95), lo = quantile(value, .05)) %>% ungroup() %>% mutate(is.outlier = value<lo | value>up | value<0, value = ifelse(is.outlier, NA, value), value1 = ifelse(is.outlier, NA, value1), yhat = ifelse(is.outlier, NA, yhat), lower = ifelse(is.outlier, NA, lower), upper = ifelse(is.outlier, NA, upper)) dat_1_5 <- dat_alt %>% filter(biomarker %in% c("VCAM", "SDC", "Ang", "IL8", "IP10")) %>% mutate(biomarker = factor(biomarker, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10"))) dat_6_10 <- dat_alt %>% filter(biomarker %in% c("IL1RA", "CD163", "TREM", "Fer", "CRP")) %>% mutate(biomarker = factor(biomarker, levels=c("IL1RA", "CD163", "TREM", "Fer", "CRP"))) # Modify facets' scales #devtools::install_github("zeehio/facetscales") library(facetscales) xVCAM <- c(1,4,15,60,250,1000,4000) xSDC <- c(1400,2000,2800,4000,5600) xAng <- c(50,100,250,500,1000,2000) xIL8 <- c(5,7,10,14,20,28,40) xIP10 <- c(25,100,400,1600,6400) xIL1RA <- c(1000,2000,4000,8000,16000) xCD163 <- c(75,150,300,600) xTREM <- c(35,50,70,100,140,200) xFer <- c(50,100,200,400,800) xCRP <- c(2.5,5,10,20,40,80) scales_x <- list( `VCAM` = scale_x_continuous(breaks = log2(xVCAM), labels = xVCAM), `SDC` = scale_x_continuous(breaks = log2(xSDC), labels = xSDC), `Ang` = scale_x_continuous(breaks = log2(xAng), labels = xAng), `IL8` = scale_x_continuous(breaks = log2(xIL8), labels = xIL8), `IP10` = scale_x_continuous(breaks = log2(xIP10), labels = xIP10), `IL1RA` = scale_x_continuous(breaks = log2(xIL1RA), labels = xIL1RA), `CD163` = scale_x_continuous(breaks = log2(xCD163), labels = xCD163), `TREM` = scale_x_continuous(breaks = log2(xTREM), labels = xTREM), `Fer` = scale_x_continuous(breaks = log2(xFer), labels = xFer), `CRP` = scale_x_continuous(breaks = log2(xCRP), labels = xCRP) ) ybreak1 <- c(.125, .25, .5, 1, 2, 4, 8) ybreak2 <- c(.125, .25, .5, 1, 2, 4) scales_y1 <- list( `Children` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1), `Adults` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1) ) scales_y2 <- list( `Children` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2), `Adults` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2) ) # Set facets' names age <- c(`Children` = "Children", `Adults` = "Adults") biomarkers <- c(`VCAM` = "VCAM-1 (ng/ml)", `SDC`= "SDC-1 (pg/ml)", `Ang` = "Ang-2 (pg/ml)", `IL8` = "IL-8 (pg/ml)", `IP10` = "IP-10 (pg/ml)", `IL1RA` = "IL-1RA (pg/ml)", `CD163` = "sCD163 (ng/ml)", `TREM` = "sTREM-1 (pg/ml)", `Fer` = "Ferritin (ng/ml)", `CRP` = "CRP (mg/l)") # Plot for the first 5 biomarkers p.1.5 <- ggplot(dat_1_5, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=serotype), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=serotype), size=.5, alpha=.7) + geom_rug(data=filter(dat_1_5, age=="Children" & sev.or.inte==0 & serotype=="DENV-1"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, age=="Children" & sev.or.inte==1 & serotype=="DENV-1"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, age=="Adults" & sev.or.inte==0 & serotype=="Others"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_1_5, age=="Adults" & sev.or.inte==1 & serotype=="Others"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("DENV-1","Others")) + scale_fill_manual(values=c("red","blue"), labels=c("DENV-1","Others")) + ylab("Odds ratio") + theme(legend.position="top", legend.title=element_blank(), axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(age), scales=list(x=scales_x, y=scales_y1), labeller = as_labeller(c(age, biomarkers))) # Plot for the last 5 biomarkers p.6.10 <- ggplot(dat_6_10, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=serotype), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=serotype), size=.5, alpha=.7) + geom_rug(data=filter(dat_6_10, age=="Children" & sev.or.inte==0 & serotype=="DENV-1"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, age=="Children" & sev.or.inte==1 & serotype=="DENV-1"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, age=="Adults" & sev.or.inte==0 & serotype=="Others"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_6_10, age=="Adults" & sev.or.inte==1 & serotype=="Others"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("DENV-1","Others")) + scale_fill_manual(values=c("red","blue"), labels=c("DENV-1","Others")) + ylab("Odds ratio") + theme(legend.position="none", axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(age), scales=list(x=scales_x, y=scales_y2), labeller = as_labeller(c(age, biomarkers))) # Merge plots p52 <- gridExtra::grid.arrange(p.1.5, p.6.10, nrow=2, heights=c(1,.86)) ``` <!-- --> ``` r #ggsave(filename="A5_fig2.pdf", p52, dpi=300, width=8.5, height=6.2) ``` ########################################################################################### #### Appendix 5—table 1. Results from single models for severe/moderate dengue with the interaction with serotype. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # Get ORs and 95% CIs tmp1 <- data.frame( sort1 = rep(c(1:10), 2), sort2 = c(rep(2,10), rep(3,10)), bio = rep(c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP"), 2), ref1 = c(ref0-1, ref0), ref2 = c(ref0, ref0+1) ) %>% arrange(sort1, sort2) %>% group_by(sort1, sort2) %>% do(cbind(., # DENV-1 - children or1c = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, serotype=1, dat=dat1), lo1c = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, serotype=1, dat=dat1), up1c = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, serotype=1, dat=dat1), # Others - children or1a = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, serotype=2, dat=dat1), lo1a = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, serotype=2, dat=dat1), up1a = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, serotype=2, dat=dat1), # DENV-1 - adults or2c = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, serotype=1, dat=dat1), lo2c = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, serotype=1, dat=dat1), up2c = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, serotype=1, dat=dat1), # Others - adults or2a = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, serotype=2, dat=dat1), lo2a = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, serotype=2, dat=dat1), up2a = get_est1s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, serotype=2, dat=dat1))) %>% ungroup() for (i in 6:17) {tmp1[[i]] <- sprintf("%.2f", round(tmp1[[i]],2))} # Get p-values tmp2 <- data.frame( sort1 = c(1:10), sort2 = rep(1,10), bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") ) %>% group_by(sort1) %>% do(cbind(., p.s1 = get_est1s(out="sev.or.inte", bio=.$bio, est="p", dat=dat1), p.s1_int = get_est1s(out="sev.or.inte", bio=.$bio, est="p int", dat=dat1), p.s1_int_age = get_est1s(out="sev.or.inte", bio=.$bio, est="p int age", dat=dat1), p.s1_int_serotype = get_est1s(out="sev.or.inte", bio=.$bio, est="p int serotype", dat=dat1))) %>% ungroup() for (i in 4:7) {tmp2[[i]] <- ifelse(tmp2[[i]]<0.001, "<0.001", sprintf("%.3f", round(tmp2[[i]],3)))} # Combine results res1 <- bind_rows(tmp1, tmp2) %>% arrange(sort1, sort2) %>% mutate(bio = ifelse(is.na(ref1), as.character(bio), ifelse(bio!="Vir", paste(" -", round(2^ref2,0), "vs", round(2^ref1,0), sep=" "), paste(" -", round(10^ref2,0), "vs", round(10^ref1,0), sep=" "))), or.sc1 = ifelse(is.na(lo1c), NA, paste(or1c, " (", lo1c, "-", up1c, ")", sep="")), or.sa1 = ifelse(is.na(lo1a), NA, paste(or1a, " (", lo1a, "-", up1a, ")", sep="")), or.sc2 = ifelse(is.na(lo2c), NA, paste(or2c, " (", lo2c, "-", up2c, ")", sep="")), or.sa2 = ifelse(is.na(lo2a), NA, paste(or2a, " (", lo2a, "-", up2a, ")", sep=""))) %>% select(bio, or.sc1, or.sa1, or.sc2, or.sa2, p.s1, p.s1_int, p.s1_int_age, p.s1_int_serotype) names(res1) <- c("", "OR (children - DENV-1)", "OR (children - others)", "OR (adults - DENV-1)", "OR (adults - others)", "P overall", "P overall interaction", "P interaction with age", "P interaction with serotype") # Report results knitr::kable(res1) ``` | | OR (children - DENV-1) | OR (children - others) | OR (adults - DENV-1) | OR (adults - others) | P overall | P overall interaction | P interaction with age | P interaction with serotype | |:-----------------|:-----------------------|:-----------------------|:---------------------|:---------------------|:----------|:----------------------|:-----------------------|:----------------------------| | VCAM | | | | | 0.008 | 0.822 | 0.729 | 0.565 | | \- 1636 vs 818 | 1.22 (1.02-1.46) | 1.14 (0.95-1.37) | 1.42 (1.13-1.77) | 1.32 (1.10-1.58) | | | | | | \- 3272 vs 1636 | 1.28 (0.99-1.66) | 1.18 (0.91-1.54) | 1.57 (1.15-2.14) | 1.45 (1.13-1.86) | | | | | | SDC | | | | | <0.001 | 0.087 | 0.326 | 0.352 | | \- 2519 vs 1260 | 2.35 (0.89-6.25) | 2.56 (0.97-6.74) | 2.85 (0.80-10.12) | 3.10 (1.11-8.64) | | | | | | \- 5039 vs 2519 | 2.41 (1.47-3.96) | 1.22 (0.69-2.13) | 6.39 (2.81-14.53) | 3.23 (1.73-6.01) | | | | | | Ang | | | | | <0.001 | 0.935 | 0.923 | 0.702 | | \- 1204 vs 602 | 1.67 (1.35-2.05) | 1.51 (1.21-1.88) | 1.64 (1.28-2.10) | 1.48 (1.20-1.83) | | | | | | \- 2409 vs 1204 | 2.46 (1.60-3.79) | 1.97 (1.25-3.11) | 2.40 (1.44-4.02) | 1.92 (1.28-2.89) | | | | | | IL8 | | | | | <0.001 | <0.001 | <0.001 | 0.104 | | \- 14 vs 7 | 1.52 (1.00-2.31) | 1.12 (0.76-1.65) | 2.83 (1.57-5.12) | 2.09 (1.34-3.26) | | | | | | \- 28 vs 14 | 1.22 (0.90-1.65) | 0.84 (0.60-1.17) | 3.04 (1.88-4.94) | 2.10 (1.45-3.04) | | | | | | IP10 | | | | | <0.001 | 0.975 | 0.950 | 0.681 | | \- 3093 vs 1546 | 1.54 (1.28-1.84) | 1.39 (1.13-1.70) | 1.64 (1.28-2.10) | 1.48 (1.20-1.83) | | | | | | \- 6186 vs 3093 | 1.84 (1.39-2.43) | 1.60 (1.17-2.20) | 2.00 (1.37-2.92) | 1.75 (1.27-2.40) | | | | | | IL1RA | | | | | <0.001 | 0.577 | 0.280 | 0.805 | | \- 6434 vs 3217 | 1.68 (1.29-2.18) | 1.51 (1.20-1.90) | 1.96 (1.34-2.86) | 1.76 (1.29-2.38) | | | | | | \- 12868 vs 6434 | 1.87 (1.43-2.44) | 1.77 (1.28-2.46) | 1.73 (1.23-2.44) | 1.64 (1.18-2.29) | | | | | | CD163 | | | | | 0.002 | 0.983 | 0.831 | 0.932 | | \- 295 vs 147 | 1.50 (1.03-2.20) | 1.60 (1.03-2.48) | 1.59 (1.02-2.47) | 1.69 (1.18-2.42) | | | | | | \- 589 vs 295 | 1.35 (0.94-1.94) | 1.48 (0.99-2.21) | 1.50 (0.92-2.43) | 1.64 (1.04-2.59) | | | | | | TREM | | | | | 0.146 | 0.979 | 0.998 | 0.597 | | \- 85 vs 42 | 1.55 (0.94-2.57) | 2.03 (1.22-3.39) | 1.68 (0.92-3.07) | 2.20 (1.21-4.02) | | | | | | \- 169 vs 85 | 1.08 (0.80-1.45) | 1.16 (0.87-1.54) | 1.08 (0.68-1.71) | 1.16 (0.83-1.60) | | | | | | Fer | | | | | 0.112 | 0.139 | 0.177 | 0.711 | | \- 243 vs 122 | 1.18 (0.97-1.44) | 1.12 (0.91-1.38) | 1.14 (0.88-1.49) | 1.08 (0.88-1.32) | | | | | | \- 487 vs 243 | 1.28 (0.96-1.71) | 1.32 (0.91-1.92) | 0.87 (0.55-1.38) | 0.90 (0.64-1.27) | | | | | | CRP | | | | | <0.001 | 0.080 | 0.029 | 0.755 | | \- 28 vs 14 | 1.23 (1.06-1.43) | 1.36 (1.12-1.66) | 1.21 (0.94-1.57) | 1.34 (1.07-1.69) | | | | | | \- 56 vs 28 | 1.06 (0.85-1.33) | 1.24 (0.97-1.58) | 1.25 (0.93-1.68) | 1.46 (1.13-1.87) | | | | | ########################################################################################### #### Appendix 5—table 2. Results from global model for severe/moderate dengue with the interaction with serotype. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # Get ORs and 95% CIs tmp1 <- data.frame( sort1 = rep(c(1:10), 2), sort2 = c(rep(2,10), rep(3,10)), bio = rep(c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP"), 2), ref1 = c(ref0-1, ref0), ref2 = c(ref0, ref0+1) ) %>% arrange(sort1, sort2) %>% group_by(sort1, sort2) %>% do(cbind(., # DENV-1 - children or1c = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, serotype=1, dat=dat1), lo1c = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, serotype=1, dat=dat1), up1c = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, serotype=1, dat=dat1), # Others - children or1a = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, serotype=2, dat=dat1), lo1a = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, serotype=2, dat=dat1), up1a = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, serotype=2, dat=dat1), # DENV-1 - adults or2c = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, serotype=1, dat=dat1), lo2c = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, serotype=1, dat=dat1), up2c = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, serotype=1, dat=dat1), # Others - adults or2a = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, serotype=2, dat=dat1), lo2a = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, serotype=2, dat=dat1), up2a = get_est2s(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, serotype=2, dat=dat1))) %>% ungroup() for (i in 6:17) {tmp1[[i]] <- sprintf("%.2f", round(tmp1[[i]],2))} # Get p-values tmp2 <- data.frame( sort1 = c(1:10), sort2 = rep(1,10), bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") ) %>% group_by(sort1) %>% do(cbind(., p.s2 = get_est2s(out="sev.or.inte", bio=.$bio, est="p", dat=dat1), p.s2_int = get_est2s(out="sev.or.inte", bio=.$bio, est="p int", dat=dat1), p.s2_int_age = get_est2s(out="sev.or.inte", bio=.$bio, est="p int age", dat=dat1), p.s2_int_serotype = get_est2s(out="sev.or.inte", bio=.$bio, est="p int serotype", dat=dat1))) %>% ungroup() for (i in 4:7) {tmp2[[i]] <- ifelse(tmp2[[i]]<0.001, "<0.001", sprintf("%.3f", round(tmp2[[i]],3)))} # Combine results res2 <- bind_rows(tmp1, tmp2) %>% arrange(sort1, sort2) %>% mutate(bio = ifelse(is.na(ref1), as.character(bio), ifelse(bio!="Vir", paste(" -", round(2^ref2,0), "vs", round(2^ref1,0), sep=" "), paste(" -", round(10^ref2,0), "vs", round(10^ref1,0), sep=" "))), or.sc1 = ifelse(is.na(lo1c), NA, paste(or1c, " (", lo1c, "-", up1c, ")", sep="")), or.sa1 = ifelse(is.na(lo1a), NA, paste(or1a, " (", lo1a, "-", up1a, ")", sep="")), or.sc2 = ifelse(is.na(lo2c), NA, paste(or2c, " (", lo2c, "-", up2c, ")", sep="")), or.sa2 = ifelse(is.na(lo2a), NA, paste(or2a, " (", lo2a, "-", up2a, ")", sep=""))) %>% select(bio, or.sc1, or.sa1, or.sc2, or.sa2, p.s2, p.s2_int, p.s2_int_age, p.s2_int_serotype) names(res2) <- c("", "OR (children - DENV-1)", "OR (children - others)", "OR (adults - DENV-1)", "OR (adults - others)", "P overall", "P overall interaction", "P interaction with age", "P interaction with serotype") # Report results knitr::kable(res2) ``` | | OR (children - DENV-1) | OR (children - others) | OR (adults - DENV-1) | OR (adults - others) | P overall | P overall interaction | P interaction with age | P interaction with serotype | |:-----------------|:-----------------------|:-----------------------|:---------------------|:---------------------|:----------|:----------------------|:-----------------------|:----------------------------| | VCAM | | | | | 0.449 | 0.258 | 0.248 | 0.327 | | \- 1636 vs 818 | 0.84 (0.62-1.13) | 0.88 (0.66-1.17) | 1.18 (0.81-1.72) | 1.24 (0.93-1.65) | | | | | | \- 3272 vs 1636 | 0.75 (0.50-1.12) | 0.85 (0.58-1.25) | 1.14 (0.70-1.85) | 1.29 (0.88-1.90) | | | | | | SDC | | | | | 0.027 | 0.788 | 0.821 | 0.316 | | \- 2519 vs 1260 | 3.21 (0.79-12.94) | 1.38 (0.38-4.94) | 5.98 (1.00-35.72) | 2.57 (0.66-10.01) | | | | | | \- 5039 vs 2519 | 2.82 (1.21-6.57) | 1.45 (0.62-3.40) | 3.70 (1.08-12.72) | 1.90 (0.80-4.52) | | | | | | Ang | | | | | 0.067 | 0.102 | 0.043 | 0.472 | | \- 1204 vs 602 | 1.65 (1.10-2.47) | 1.79 (1.18-2.72) | 0.89 (0.55-1.44) | 0.97 (0.67-1.40) | | | | | | \- 2409 vs 1204 | 2.22 (1.21-4.05) | 1.73 (0.94-3.17) | 1.25 (0.63-2.48) | 0.97 (0.58-1.62) | | | | | | IL8 | | | | | <0.001 | <0.001 | <0.001 | 0.591 | | \- 14 vs 7 | 0.86 (0.48-1.51) | 0.73 (0.44-1.24) | 2.14 (1.01-4.53) | 1.84 (1.05-3.20) | | | | | | \- 28 vs 14 | 0.68 (0.41-1.13) | 0.49 (0.29-0.82) | 2.60 (1.27-5.32) | 1.88 (1.20-2.96) | | | | | | IP10 | | | | | 0.068 | 0.875 | 0.715 | 0.888 | | \- 3093 vs 1546 | 0.98 (0.69-1.40) | 0.91 (0.65-1.26) | 0.86 (0.53-1.40) | 0.80 (0.53-1.19) | | | | | | \- 6186 vs 3093 | 1.27 (0.77-2.10) | 1.10 (0.70-1.74) | 1.04 (0.54-2.00) | 0.90 (0.53-1.53) | | | | | | IL1RA | | | | | <0.001 | 0.333 | 0.230 | 0.711 | | \- 6434 vs 3217 | 2.63 (1.65-4.20) | 2.11 (1.32-3.39) | 2.50 (1.28-4.88) | 2.01 (1.15-3.51) | | | | | | \- 12868 vs 6434 | 2.38 (1.45-3.91) | 1.90 (1.19-3.04) | 1.65 (0.88-3.09) | 1.32 (0.78-2.23) | | | | | | CD163 | | | | | 0.340 | 0.661 | 0.455 | 0.769 | | \- 295 vs 147 | 1.20 (0.65-2.20) | 1.54 (0.83-2.87) | 1.24 (0.68-2.27) | 1.61 (0.92-2.80) | | | | | | \- 589 vs 295 | 1.20 (0.77-1.87) | 1.20 (0.75-1.94) | 1.49 (0.84-2.67) | 1.50 (0.85-2.61) | | | | | | TREM | | | | | 0.441 | 0.289 | 0.306 | 0.071 | | \- 85 vs 42 | 0.90 (0.48-1.67) | 1.23 (0.66-2.28) | 0.84 (0.36-1.94) | 1.15 (0.54-2.42) | | | | | | \- 169 vs 85 | 0.56 (0.33-0.96) | 1.18 (0.79-1.77) | 0.34 (0.15-0.76) | 0.71 (0.43-1.19) | | | | | | Fer | | | | | 0.067 | 0.033 | 0.013 | 0.331 | | \- 243 vs 122 | 1.36 (1.00-1.85) | 1.25 (0.92-1.69) | 0.92 (0.62-1.36) | 0.84 (0.63-1.13) | | | | | | \- 487 vs 243 | 1.08 (0.72-1.63) | 1.59 (0.97-2.61) | 0.55 (0.29-1.05) | 0.81 (0.51-1.29) | | | | | | CRP | | | | | 0.156 | 0.103 | 0.241 | 0.136 | | \- 28 vs 14 | 0.95 (0.78-1.16) | 1.25 (0.98-1.60) | 0.94 (0.67-1.32) | 1.24 (0.90-1.70) | | | | | | \- 56 vs 28 | 0.80 (0.60-1.07) | 1.07 (0.80-1.44) | 1.13 (0.75-1.70) | 1.51 (1.09-2.09) | | | | | ########################################################################################### #### Appendix 6—figure 1. Results from models for severe dengue endpoint. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # For children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred1(out="sev.only", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m2 <- get_pred1(out="sev.only", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m3 <- get_pred1(out="sev.only", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m4 <- get_pred1(out="sev.only", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m5 <- get_pred1(out="sev.only", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m6 <- get_pred1(out="sev.only", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m7 <- get_pred1(out="sev.only", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m8 <- get_pred1(out="sev.only", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m9 <- get_pred1(out="sev.only", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m10 <- get_pred1(out="sev.only", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg1 <- get_pred2(out="sev.only", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg2 <- get_pred2(out="sev.only", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg3 <- get_pred2(out="sev.only", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg4 <- get_pred2(out="sev.only", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg5 <- get_pred2(out="sev.only", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg6 <- get_pred2(out="sev.only", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg7 <- get_pred2(out="sev.only", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg8 <- get_pred2(out="sev.only", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg9 <- get_pred2(out="sev.only", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg10 <- get_pred2(out="sev.only", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_p1 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(age = "10 years") # For adults dd$limits["Adjust to","Age"] <- 25 dat_m1 <- get_pred1(out="sev.only", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m2 <- get_pred1(out="sev.only", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m3 <- get_pred1(out="sev.only", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m4 <- get_pred1(out="sev.only", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m5 <- get_pred1(out="sev.only", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m6 <- get_pred1(out="sev.only", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m7 <- get_pred1(out="sev.only", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m8 <- get_pred1(out="sev.only", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m9 <- get_pred1(out="sev.only", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m10 <- get_pred1(out="sev.only", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg1 <- get_pred2(out="sev.only", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg2 <- get_pred2(out="sev.only", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg3 <- get_pred2(out="sev.only", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg4 <- get_pred2(out="sev.only", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg5 <- get_pred2(out="sev.only", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg6 <- get_pred2(out="sev.only", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg7 <- get_pred2(out="sev.only", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg8 <- get_pred2(out="sev.only", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg9 <- get_pred2(out="sev.only", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg10 <- get_pred2(out="sev.only", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_p2 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(age = "25 years") # Merge results for children and adults vis2 <- rbind(dat_p1, dat_p2) %>% mutate(yhat = ifelse(yhat>log(32), NA, ifelse(yhat<log(1/32), NA, yhat)), lower = ifelse(lower>log(32), log(32), ifelse(lower<log(1/32), log(1/32), lower)), upper = ifelse(upper>log(32), log(32), ifelse(upper<log(1/32), log(1/32), upper)), outcome = "outcome 2") # Merge data for plots tmp0 <- dat %>% arrange(age15) %>% select(sev.only) tmp <- data.frame(sev.only = rep(tmp0$sev.only, 20)) tmp_p1 <- vis2 %>% mutate(value1 = 2^value, age = factor(age, levels=c("10 years", "25 years")), gr = ifelse(model=="Single model" & age=="10 years", 1, ifelse(model=="Single model" & age=="25 years", 2, ifelse(model=="Global model" & age=="10 years", 3, 4))), gr = factor(gr, levels=c(1:4), labels=c("Single children", "Single adults", "Global children", "Global adults")), model = factor(model, levels=c("Single model", "Global model"))) %>% bind_cols(., tmp) vline <- tmp_p1 %>% filter(model=="Single model") %>% filter(yhat==0) %>% select(biomarker, value, value1) %>% rename(vline=value, vline1=value1) dat_p <- left_join(tmp_p1, vline, by="biomarker") # Alternative data to limit to 5th - 95th of the values dat_alt <- dat_p %>% group_by(biomarker, model) %>% mutate(up = quantile(value, .95), lo = quantile(value, .05)) %>% ungroup() %>% mutate(is.outlier = value<lo | value>up | value<0, value = ifelse(is.outlier, NA, value), value1 = ifelse(is.outlier, NA, value1), yhat = ifelse(is.outlier, NA, yhat), lower = ifelse(is.outlier, NA, lower), upper = ifelse(is.outlier, NA, upper)) dat_1_5 <- dat_alt %>% filter(biomarker %in% c("VCAM", "SDC", "Ang", "IL8", "IP10")) %>% mutate(biomarker = factor(biomarker, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10"))) dat_6_10 <- dat_alt %>% filter(biomarker %in% c("IL1RA", "CD163", "TREM", "Fer", "CRP")) %>% mutate(biomarker = factor(biomarker, levels=c("IL1RA", "CD163", "TREM", "Fer", "CRP"))) # Modify facets' scales #library(facetscales) xVCAM <- c(1,4,15,60,250,1000,4000) xSDC <- c(1400,2000,2800,4000,5600) xAng <- c(50,100,250,500,1000,2000) xIL8 <- c(5,7,10,14,20,28,40) xIP10 <- c(25,100,400,1600,6400) xIL1RA <- c(1000,2000,4000,8000,16000) xCD163 <- c(75,150,300,600) xTREM <- c(35,50,70,100,140,200) xFer <- c(50,100,200,400,800) xCRP <- c(2.5,5,10,20,40,80) scales_x <- list( `VCAM` = scale_x_continuous(breaks = log2(xVCAM), labels = xVCAM), `SDC` = scale_x_continuous(breaks = log2(xSDC), labels = xSDC), `Ang` = scale_x_continuous(breaks = log2(xAng), labels = xAng), `IL8` = scale_x_continuous(breaks = log2(xIL8), labels = xIL8), `IP10` = scale_x_continuous(breaks = log2(xIP10), labels = xIP10), `IL1RA` = scale_x_continuous(breaks = log2(xIL1RA), labels = xIL1RA), `CD163` = scale_x_continuous(breaks = log2(xCD163), labels = xCD163), `TREM` = scale_x_continuous(breaks = log2(xTREM), labels = xTREM), `Fer` = scale_x_continuous(breaks = log2(xFer), labels = xFer), `CRP` = scale_x_continuous(breaks = log2(xCRP), labels = xCRP) ) ybreak1 <- c(.0625,.125, .25, .5, 1, 2, 4, 8, 16) ybreak2 <- c(.0625,.125, .25, .5, 1, 2, 4, 8, 16) scales_y1 <- list( `Single model` = scale_y_continuous(limits = c(log(.0624), log(16.01)), breaks = log(ybreak1), labels = ybreak1), `Global model` = scale_y_continuous(limits = c(log(.0624), log(16.01)), breaks = log(ybreak1), labels = ybreak1) ) scales_y2 <- list( `Single model` = scale_y_continuous(limits = c(log(.0624), log(16.01)), breaks = log(ybreak2), labels = ybreak2), `Global model` = scale_y_continuous(limits = c(log(.0624), log(16.01)), breaks = log(ybreak2), labels = ybreak2) ) # Set facets' names models <- c(`Single model` = "Single model", `Global model` = "Global model") biomarkers <- c(`VCAM` = "VCAM-1 (ng/ml)", `SDC`= "SDC-1 (pg/ml)", `Ang` = "Ang-2 (pg/ml)", `IL8` = "IL-8 (pg/ml)", `IP10` = "IP-10 (pg/ml)", `IL1RA` = "IL-1RA (pg/ml)", `CD163` = "sCD163 (ng/ml)", `TREM` = "sTREM-1 (pg/ml)", `Fer` = "Ferritin (ng/ml)", `CRP` = "CRP (mg/l)") # Plot for the first 5 biomarkers p.1.5 <- ggplot(dat_1_5, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_1_5, model=="Single model" & sev.only==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Single model" & sev.only==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Global model" & sev.only==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_1_5, model=="Global model" & sev.only==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="top", legend.title=element_blank(), axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y1), labeller = as_labeller(c(models, biomarkers))) # Plot for the last 5 biomarkers p.6.10 <- ggplot(dat_6_10, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_6_10, model=="Single model" & sev.only==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, model=="Single model" & sev.only==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, model=="Global model" & sev.only==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_6_10, model=="Global model" & sev.only==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="none", axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y2), labeller = as_labeller(c(models, biomarkers))) # Merge plots p61 <- gridExtra::grid.arrange(p.1.5, p.6.10, nrow=2, heights=c(1,.85)) ``` <!-- --> ``` r #ggsave(filename="A6_fig1.pdf", p61, width=8.5, height=6.2) ``` ########################################################################################### #### Appendix 6—figure 2. Results from models for severe dengue or dengue with warning signs endpoint. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # For children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred1(out="sev.or.ws", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m2 <- get_pred1(out="sev.or.ws", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m3 <- get_pred1(out="sev.or.ws", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m4 <- get_pred1(out="sev.or.ws", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m5 <- get_pred1(out="sev.or.ws", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m6 <- get_pred1(out="sev.or.ws", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m7 <- get_pred1(out="sev.or.ws", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m8 <- get_pred1(out="sev.or.ws", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m9 <- get_pred1(out="sev.or.ws", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m10 <- get_pred1(out="sev.or.ws", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg1 <- get_pred2(out="sev.or.ws", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg2 <- get_pred2(out="sev.or.ws", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg3 <- get_pred2(out="sev.or.ws", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg4 <- get_pred2(out="sev.or.ws", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg5 <- get_pred2(out="sev.or.ws", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg6 <- get_pred2(out="sev.or.ws", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg7 <- get_pred2(out="sev.or.ws", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg8 <- get_pred2(out="sev.or.ws", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg9 <- get_pred2(out="sev.or.ws", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg10 <- get_pred2(out="sev.or.ws", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_p1 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(age = "10 years") # For adults dd$limits["Adjust to","Age"] <- 25 dat_m1 <- get_pred1(out="sev.or.ws", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m2 <- get_pred1(out="sev.or.ws", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m3 <- get_pred1(out="sev.or.ws", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m4 <- get_pred1(out="sev.or.ws", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m5 <- get_pred1(out="sev.or.ws", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m6 <- get_pred1(out="sev.or.ws", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m7 <- get_pred1(out="sev.or.ws", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m8 <- get_pred1(out="sev.or.ws", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m9 <- get_pred1(out="sev.or.ws", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m10 <- get_pred1(out="sev.or.ws", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg1 <- get_pred2(out="sev.or.ws", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg2 <- get_pred2(out="sev.or.ws", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg3 <- get_pred2(out="sev.or.ws", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg4 <- get_pred2(out="sev.or.ws", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg5 <- get_pred2(out="sev.or.ws", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg6 <- get_pred2(out="sev.or.ws", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg7 <- get_pred2(out="sev.or.ws", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg8 <- get_pred2(out="sev.or.ws", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg9 <- get_pred2(out="sev.or.ws", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg10 <- get_pred2(out="sev.or.ws", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_p2 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(age = "25 years") # Merge data for children and adults vis3 <- rbind(dat_p1, dat_p2) %>% mutate(outcome = "outcome 3") # Merge data for plots tmp0 <- dat %>% arrange(age15) %>% select(sev.or.ws) tmp <- data.frame(sev.or.ws = rep(tmp0$sev.or.ws, 20)) tmp_p1 <- vis3 %>% mutate(value1 = 2^value, age = factor(age, levels=c("10 years", "25 years")), gr = ifelse(model=="Single model" & age=="10 years", 1, ifelse(model=="Single model" & age=="25 years", 2, ifelse(model=="Global model" & age=="10 years", 3, 4))), gr = factor(gr, levels=c(1:4), labels=c("Single children", "Single adults", "Global children", "Global adults")), model = factor(model, levels=c("Single model", "Global model"))) %>% bind_cols(., tmp) vline <- tmp_p1 %>% filter(model=="Single model") %>% filter(yhat==0) %>% select(biomarker, value, value1) %>% rename(vline=value, vline1=value1) dat_p <- left_join(tmp_p1, vline, by="biomarker") # Alternative data to limit to 5th - 95th of the values dat_alt <- dat_p %>% group_by(biomarker, model) %>% mutate(up = quantile(value, .95), lo = quantile(value, .05)) %>% ungroup() %>% mutate(is.outlier = value<lo | value>up | value<0, value = ifelse(is.outlier, NA, value), value1 = ifelse(is.outlier, NA, value1), yhat = ifelse(is.outlier, NA, yhat), lower = ifelse(is.outlier, NA, lower), upper = ifelse(is.outlier, NA, upper)) dat_1_5 <- dat_alt %>% filter(biomarker %in% c("VCAM", "SDC", "Ang", "IL8", "IP10")) %>% mutate(biomarker = factor(biomarker, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10"))) dat_6_10 <- dat_alt %>% filter(biomarker %in% c("IL1RA", "CD163", "TREM", "Fer", "CRP")) %>% mutate(biomarker = factor(biomarker, levels=c("IL1RA", "CD163", "TREM", "Fer", "CRP"))) # Modify facets' scales #library(facetscales) xVCAM <- c(1,4,15,60,250,1000,4000) xSDC <- c(1400,2000,2800,4000,5600) xAng <- c(50,100,250,500,1000,2000) xIL8 <- c(5,7,10,14,20,28,40) xIP10 <- c(25,100,400,1600,6400) xIL1RA <- c(1000,2000,4000,8000,16000) xCD163 <- c(75,150,300,600) xTREM <- c(35,50,70,100,140,200) xFer <- c(50,100,200,400,800) xCRP <- c(2.5,5,10,20,40,80) scales_x <- list( `VCAM` = scale_x_continuous(breaks = log2(xVCAM), labels = xVCAM), `SDC` = scale_x_continuous(breaks = log2(xSDC), labels = xSDC), `Ang` = scale_x_continuous(breaks = log2(xAng), labels = xAng), `IL8` = scale_x_continuous(breaks = log2(xIL8), labels = xIL8), `IP10` = scale_x_continuous(breaks = log2(xIP10), labels = xIP10), `IL1RA` = scale_x_continuous(breaks = log2(xIL1RA), labels = xIL1RA), `CD163` = scale_x_continuous(breaks = log2(xCD163), labels = xCD163), `TREM` = scale_x_continuous(breaks = log2(xTREM), labels = xTREM), `Fer` = scale_x_continuous(breaks = log2(xFer), labels = xFer), `CRP` = scale_x_continuous(breaks = log2(xCRP), labels = xCRP) ) ybreak1 <- c(.25, .5, 1, 2, 4) ybreak2 <- c(.25, .5, 1, 2, 4) scales_y1 <- list( `Single model` = scale_y_continuous(limits = c(log(.24), log(4.01)), breaks = log(ybreak1), labels = ybreak1), `Global model` = scale_y_continuous(limits = c(log(.24), log(4.01)), breaks = log(ybreak1), labels = ybreak1) ) scales_y2 <- list( `Single model` = scale_y_continuous(limits = c(log(.24), log(4.01)), breaks = log(ybreak2), labels = ybreak2), `Global model` = scale_y_continuous(limits = c(log(.24), log(4.01)), breaks = log(ybreak2), labels = ybreak2) ) # Set facets' names models <- c(`Single model` = "Single model", `Global model` = "Global model") biomarkers <- c(`VCAM` = "VCAM-1 (ng/ml)", `SDC`= "SDC-1 (pg/ml)", `Ang` = "Ang-2 (pg/ml)", `IL8` = "IL-8 (pg/ml)", `IP10` = "IP-10 (pg/ml)", `IL1RA` = "IL-1RA (pg/ml)", `CD163` = "sCD163 (ng/ml)", `TREM` = "sTREM-1 (pg/ml)", `Fer` = "Ferritin (ng/ml)", `CRP` = "CRP (mg/l)") # Plot for the first 5 biomarkers p.1.5 <- ggplot(dat_1_5, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_1_5, model=="Single model" & sev.or.ws==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Single model" & sev.or.ws==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Global model" & sev.or.ws==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_1_5, model=="Global model" & sev.or.ws==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="top", legend.title=element_blank(), axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y1), labeller = as_labeller(c(models, biomarkers))) # Plot for the last 5 biomarkers p.6.10 <- ggplot(dat_6_10, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_6_10, model=="Single model" & sev.or.ws==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, model=="Single model" & sev.or.ws==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, model=="Global model" & sev.or.ws==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_6_10, model=="Global model" & sev.or.ws==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="none", axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y2), labeller = as_labeller(c(models, biomarkers))) # Merge plots p62 <- gridExtra::grid.arrange(p.1.5, p.6.10, nrow=2, heights=c(1,.85)) ``` <!-- --> ``` r #ggsave(filename="A6_fig2.pdf", p62, width=8.5, height=6.2) ``` ########################################################################################### #### Appendix 6—figure 3. Results from models for hospitalization endpoint. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # For children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred1(out="hospital", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m2 <- get_pred1(out="hospital", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m3 <- get_pred1(out="hospital", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m4 <- get_pred1(out="hospital", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m5 <- get_pred1(out="hospital", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m6 <- get_pred1(out="hospital", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m7 <- get_pred1(out="hospital", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m8 <- get_pred1(out="hospital", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m9 <- get_pred1(out="hospital", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m10 <- get_pred1(out="hospital", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg1 <- get_pred2(out="hospital", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg2 <- get_pred2(out="hospital", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg3 <- get_pred2(out="hospital", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg4 <- get_pred2(out="hospital", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg5 <- get_pred2(out="hospital", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg6 <- get_pred2(out="hospital", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg7 <- get_pred2(out="hospital", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg8 <- get_pred2(out="hospital", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg9 <- get_pred2(out="hospital", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg10 <- get_pred2(out="hospital", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_p1 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(age = "10 years") # For adults dd$limits["Adjust to","Age"] <- 25 dat_m1 <- get_pred1(out="hospital", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m2 <- get_pred1(out="hospital", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m3 <- get_pred1(out="hospital", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m4 <- get_pred1(out="hospital", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m5 <- get_pred1(out="hospital", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m6 <- get_pred1(out="hospital", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m7 <- get_pred1(out="hospital", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m8 <- get_pred1(out="hospital", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m9 <- get_pred1(out="hospital", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m10 <- get_pred1(out="hospital", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg1 <- get_pred2(out="hospital", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg2 <- get_pred2(out="hospital", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg3 <- get_pred2(out="hospital", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg4 <- get_pred2(out="hospital", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg5 <- get_pred2(out="hospital", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg6 <- get_pred2(out="hospital", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg7 <- get_pred2(out="hospital", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg8 <- get_pred2(out="hospital", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg9 <- get_pred2(out="hospital", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg10 <- get_pred2(out="hospital", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_p2 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %>% mutate(age = "25 years") # Merge data for children and adults vis4 <- rbind(dat_p1, dat_p2) %>% mutate(outcome = "outcome 4") # Merge data for plots tmp0 <- dat %>% arrange(age15) %>% select(hospital) tmp <- data.frame(hospital = rep(tmp0$hospital, 20)) tmp_p1 <- vis4 %>% mutate(value1 = 2^value, age = factor(age, levels=c("10 years", "25 years")), gr = ifelse(model=="Single model" & age=="10 years", 1, ifelse(model=="Single model" & age=="25 years", 2, ifelse(model=="Global model" & age=="10 years", 3, 4))), gr = factor(gr, levels=c(1:4), labels=c("Single children", "Single adults", "Global children", "Global adults")), model = factor(model, levels=c("Single model", "Global model"))) %>% bind_cols(., tmp) vline <- tmp_p1 %>% filter(model=="Single model") %>% filter(yhat==0) %>% select(biomarker, value, value1) %>% rename(vline=value, vline1=value1) dat_p <- left_join(tmp_p1, vline, by="biomarker") # Alternative data to limit to 5th - 95th of the values dat_alt <- dat_p %>% group_by(biomarker, model) %>% mutate(up = quantile(value, .95), lo = quantile(value, .05)) %>% ungroup() %>% mutate(is.outlier = value<lo | value>up | value<0, value = ifelse(is.outlier, NA, value), value1 = ifelse(is.outlier, NA, value1), yhat = ifelse(is.outlier, NA, yhat), lower = ifelse(is.outlier, NA, lower), upper = ifelse(is.outlier, NA, upper)) dat_1_5 <- dat_alt %>% filter(biomarker %in% c("VCAM", "SDC", "Ang", "IL8", "IP10")) %>% mutate(biomarker = factor(biomarker, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10"))) dat_6_10 <- dat_alt %>% filter(biomarker %in% c("IL1RA", "CD163", "TREM", "Fer", "CRP")) %>% mutate(biomarker = factor(biomarker, levels=c("IL1RA", "CD163", "TREM", "Fer", "CRP"))) # Modify facets' scales #library(facetscales) xVCAM <- c(1,4,15,60,250,1000,4000) xSDC <- c(1400,2000,2800,4000,5600) xAng <- c(50,100,250,500,1000,2000) xIL8 <- c(5,7,10,14,20,28,40) xIP10 <- c(25,100,400,1600,6400) xIL1RA <- c(1000,2000,4000,8000,16000) xCD163 <- c(75,150,300,600) xTREM <- c(35,50,70,100,140,200) xFer <- c(50,100,200,400,800) xCRP <- c(2.5,5,10,20,40,80) scales_x <- list( `VCAM` = scale_x_continuous(breaks = log2(xVCAM), labels = xVCAM), `SDC` = scale_x_continuous(breaks = log2(xSDC), labels = xSDC), `Ang` = scale_x_continuous(breaks = log2(xAng), labels = xAng), `IL8` = scale_x_continuous(breaks = log2(xIL8), labels = xIL8), `IP10` = scale_x_continuous(breaks = log2(xIP10), labels = xIP10), `IL1RA` = scale_x_continuous(breaks = log2(xIL1RA), labels = xIL1RA), `CD163` = scale_x_continuous(breaks = log2(xCD163), labels = xCD163), `TREM` = scale_x_continuous(breaks = log2(xTREM), labels = xTREM), `Fer` = scale_x_continuous(breaks = log2(xFer), labels = xFer), `CRP` = scale_x_continuous(breaks = log2(xCRP), labels = xCRP) ) ybreak1 <- c(.125, .25, .5, 1, 2, 4, 8) ybreak2 <- c(.125, .25, .5, 1, 2, 4, 8) scales_y1 <- list( `Single model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1), `Global model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1) ) scales_y2 <- list( `Single model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak2), labels = ybreak2), `Global model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak2), labels = ybreak2) ) # Set facets' names models <- c(`Single model` = "Single model", `Global model` = "Global model") biomarkers <- c(`VCAM` = "VCAM-1 (ng/ml)", `SDC`= "SDC-1 (pg/ml)", `Ang` = "Ang-2 (pg/ml)", `IL8` = "IL-8 (pg/ml)", `IP10` = "IP-10 (pg/ml)", `IL1RA` = "IL-1RA (pg/ml)", `CD163` = "sCD163 (ng/ml)", `TREM` = "sTREM-1 (pg/ml)", `Fer` = "Ferritin (ng/ml)", `CRP` = "CRP (mg/l)") # Plot for the first 5 biomarkers p.1.5 <- ggplot(dat_1_5, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_1_5, model=="Single model" & hospital==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Single model" & hospital==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Global model" & hospital==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_1_5, model=="Global model" & hospital==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="top", legend.title=element_blank(), axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y1), labeller = as_labeller(c(models, biomarkers))) # Plot for the last 5 biomarkers p.6.10 <- ggplot(dat_6_10, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_6_10, model=="Single model" & hospital==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, model=="Single model" & hospital==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_6_10, model=="Global model" & hospital==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_6_10, model=="Global model" & hospital==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="none", axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y2), labeller = as_labeller(c(models, biomarkers))) # Merge plots p63 <- gridExtra::grid.arrange(p.1.5, p.6.10, nrow=2, heights=c(1,.85)) ``` <!-- --> ``` r #ggsave(filename="A6_fig3.pdf", p63, width=8.5, height=6.2) ``` ########################################################################################### #### Appendix 6—table 1. Results from models for severe dengue endpoint. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # Get ORs and 95% CIs tmp1 <- data.frame( sort1 = rep(c(1:10), 2), sort2 = c(rep(2,10), rep(3,10)), bio = rep(c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP"), 2), ref1 = c(ref0-1, ref0), ref2 = c(ref0, ref0+1) ) %>% arrange(sort1, sort2) %>% group_by(sort1, sort2) %>% do(cbind(., # Children - single models or1c = get_est1(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo1c = get_est1(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up1c = get_est1(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Children - global model or2c = get_est2(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo2c = get_est2(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up2c = get_est2(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Adults - single models or1a = get_est1(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo1a = get_est1(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up1a = get_est1(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1), # Adults - global model or2a = get_est2(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo2a = get_est2(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up2a = get_est2(out="sev.only", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1))) %>% ungroup() for (i in 6:17) {tmp1[[i]] <- sprintf("%.2f", round(tmp1[[i]],2))} # Get p-values tmp2 <- data.frame( sort1 = c(1:10), sort2 = rep(1,10), bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") ) %>% group_by(sort1) %>% do(cbind(., p.s2 = get_est1(out="sev.only", bio=.$bio, est="p", dat=dat1), p.s2_int = get_est1(out="sev.only", bio=.$bio, est="p int", dat=dat1), p.g2 = get_est2(out="sev.only", bio=.$bio, est="p", dat=dat1), p.g2_int = get_est2(out="sev.only", bio=.$bio, est="p int", dat=dat1))) %>% ungroup() for (i in 4:7) {tmp2[[i]] <- ifelse(tmp2[[i]]<0.001, "<0.001", sprintf("%.3f", round(tmp2[[i]],3)))} # Combine results res2 <- tmp1 %>% group_by(bio) %>% slice(1) %>% left_join(., select(tmp2, -c(sort1, sort2)), by="bio") %>% arrange(sort1) %>% mutate(bio = as.character(bio), or.sc2 = ifelse(is.na(lo1c), NA, paste(or1c, " (", lo1c, "-", up1c, ")", sep="")), # s: single model; c: children or.gc2 = ifelse(is.na(lo2c), NA, paste(or2c, " (", lo2c, "-", up2c, ")", sep="")), # g: global model or.sa2 = ifelse(is.na(lo1a), NA, paste(or1a, " (", lo1a, "-", up1a, ")", sep="")), # a: adults or.ga2 = ifelse(is.na(lo2a), NA, paste(or2a, " (", lo2a, "-", up2a, ")", sep=""))) %>% select(bio, or.sc2, or.sa2, p.s2, p.s2_int, or.gc2, or.ga2, p.g2, p.g2_int) names(res2) <- c("", "OR (single - children)", "OR (single - adults)", "P overall (single)", "P interaction (single)", "OR (global - children)", "OR (global - adults)", "P overall (global)", "P interaction (global)") # Report results knitr::kable(res2) ``` | | OR (single - children) | OR (single - adults) | P overall (single) | P interaction (single) | OR (global - children) | OR (global - adults) | P overall (global) | P interaction (global) | |:------|:-----------------------|:---------------------|:-------------------|:-----------------------|:-----------------------|:---------------------|:-------------------|:-----------------------| | VCAM | 1.28 (0.77-2.12) | 2.13 (0.88-5.15) | 0.236 | 0.202 | 1.20 (0.35-4.12) | 3.13 (0.19-50.77) | 0.723 | 0.491 | | SDC | 1.55 (0.41-5.96) | 3.26 (0.69-15.52) | 0.307 | 0.438 | 1.16 (0.12-11.07) | 0.87 (0.03-27.93) | 0.987 | 0.884 | | Ang | 1.42 (0.70-2.88) | 1.79 (0.97-3.31) | 0.148 | 0.580 | 1.20 (0.33-4.31) | 1.02 (0.33-3.19) | 0.961 | 0.830 | | IL8 | 1.04 (0.47-2.33) | 2.15 (0.67-6.88) | 0.420 | 0.338 | 0.81 (0.28-2.31) | 2.51 (0.16-38.43) | 0.741 | 0.446 | | IP10 | 1.32 (0.75-2.33) | 1.64 (0.58-4.62) | 0.443 | 0.708 | 0.76 (0.17-3.30) | 0.22 (0.01-7.12) | 0.693 | 0.473 | | IL1RA | 1.84 (0.81-4.16) | 1.78 (0.80-3.94) | 0.098 | 0.956 | 2.22 (0.51-9.64) | 3.35 (0.45-24.87) | 0.386 | 0.696 | | CD163 | 1.43 (0.52-3.94) | 2.43 (0.27-21.70) | 0.617 | 0.645 | 0.98 (0.27-3.59) | 2.06 (0.15-27.47) | 0.855 | 0.590 | | TREM | 1.20 (0.44-3.28) | 1.29 (0.55-3.05) | 0.793 | 0.914 | 0.93 (0.30-2.88) | 0.38 (0.02-8.76) | 0.827 | 0.541 | | Fer | 1.13 (0.59-2.16) | 1.58 (0.59-4.23) | 0.659 | 0.501 | 1.12 (0.55-2.29) | 1.34 (0.25-7.02) | 0.926 | 0.819 | | CRP | 1.16 (0.71-1.88) | 1.77 (0.56-5.62) | 0.620 | 0.418 | 0.97 (0.58-1.63) | 1.55 (0.33-7.32) | 0.773 | 0.500 | ########################################################################################### #### Appendix 6—table 2. Results from models for severe dengue or dengue with warning signs endpoint. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # Get ORs and 95% CIs tmp1 <- data.frame( sort1 = rep(c(1:10), 2), sort2 = c(rep(2,10), rep(3,10)), bio = rep(c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP"), 2), ref1 = c(ref0-1, ref0), ref2 = c(ref0, ref0+1) ) %>% arrange(sort1, sort2) %>% group_by(sort1, sort2) %>% do(cbind(., # Children - single models or1c = get_est1(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo1c = get_est1(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up1c = get_est1(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Children - global model or2c = get_est2(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo2c = get_est2(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up2c = get_est2(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Adults - single models or1a = get_est1(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo1a = get_est1(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up1a = get_est1(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1), # Adults - global model or2a = get_est2(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo2a = get_est2(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up2a = get_est2(out="sev.or.ws", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1))) %>% ungroup() for (i in 6:17) {tmp1[[i]] <- sprintf("%.2f", round(tmp1[[i]],2))} # Get p-values tmp2 <- data.frame( sort1 = c(1:10), sort2 = rep(1,10), bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") ) %>% group_by(sort1) %>% do(cbind(., p.s3 = get_est1(out="sev.or.ws", bio=.$bio, est="p", dat=dat1), p.s3_int = get_est1(out="sev.or.ws", bio=.$bio, est="p int", dat=dat1), p.g3 = get_est2(out="sev.or.ws", bio=.$bio, est="p", dat=dat1), p.g3_int = get_est2(out="sev.or.ws", bio=.$bio, est="p int", dat=dat1))) %>% ungroup() for (i in 4:7) {tmp2[[i]] <- ifelse(tmp2[[i]]<0.001, "<0.001", sprintf("%.3f", round(tmp2[[i]],3)))} # Combine results res3 <- bind_rows(tmp1, tmp2) %>% arrange(sort1, sort2) %>% mutate(bio = ifelse(!is.na(ref1), paste(" -", round(2^ref2,0), "vs", round(2^ref1,0), sep=" "), as.character(bio)), or.sc3 = ifelse(is.na(lo1c), NA, paste(or1c, " (", lo1c, "-", up1c, ")", sep="")), # s: single model; c: children or.gc3 = ifelse(is.na(lo2c), NA, paste(or2c, " (", lo2c, "-", up2c, ")", sep="")), # g: global model or.sa3 = ifelse(is.na(lo1a), NA, paste(or1a, " (", lo1a, "-", up1a, ")", sep="")), # a: adults or.ga3 = ifelse(is.na(lo2a), NA, paste(or2a, " (", lo2a, "-", up2a, ")", sep=""))) %>% select(bio, or.sc3, or.sa3, p.s3, p.s3_int, or.gc3, or.ga3, p.g3, p.g3_int) names(res3) <- c("", "OR (single - children)", "OR (single - adults)", "P overall (single)", "P interaction (single)", "OR (global - children)", "OR (global - adults)", "P overall (global)", "P interaction (global)") # Report results knitr::kable(res3) ``` | | OR (single - children) | OR (single - adults) | P overall (single) | P interaction (single) | OR (global - children) | OR (global - adults) | P overall (global) | P interaction (global) | |:-----------------|:-----------------------|:---------------------|:-------------------|:-----------------------|:-----------------------|:---------------------|:-------------------|:-----------------------| | VCAM | | | 0.025 | 0.374 | | | 0.469 | 0.763 | | \- 1636 vs 818 | 1.06 (0.93-1.22) | 1.11 (0.92-1.33) | | | 0.82 (0.66-1.02) | 0.92 (0.66-1.30) | | | | \- 3272 vs 1636 | 1.06 (0.87-1.29) | 1.13 (0.88-1.46) | | | 0.74 (0.55-1.01) | 0.88 (0.56-1.40) | | | | SDC | | | 0.032 | 0.363 | | | 0.116 | 0.773 | | \- 2519 vs 1260 | 1.34 (0.86-2.09) | 1.15 (0.56-2.33) | | | 1.57 (0.89-2.75) | 1.89 (0.71-5.03) | | | | \- 5039 vs 2519 | 1.40 (0.90-2.17) | 2.00 (0.79-5.08) | | | 1.78 (1.00-3.18) | 2.88 (0.71-11.69) | | | | Ang | | | 0.008 | 0.637 | | | 0.009 | 0.011 | | \- 1204 vs 602 | 1.23 (1.05-1.44) | 1.12 (0.93-1.36) | | | 1.37 (1.07-1.75) | 0.95 (0.69-1.30) | | | | \- 2409 vs 1204 | 1.34 (0.95-1.88) | 1.22 (0.82-1.80) | | | 1.42 (0.91-2.21) | 0.85 (0.48-1.51) | | | | IL8 | | | 0.040 | 0.020 | | | 0.053 | 0.030 | | \- 14 vs 7 | 1.05 (0.87-1.27) | 0.97 (0.67-1.41) | | | 0.96 (0.77-1.21) | 0.94 (0.62-1.42) | | | | \- 28 vs 14 | 1.01 (0.85-1.19) | 1.45 (0.94-2.22) | | | 0.78 (0.62-0.99) | 1.48 (0.89-2.44) | | | | IP10 | | | <0.001 | 0.176 | | | 0.059 | 0.537 | | \- 3093 vs 1546 | 1.26 (1.09-1.44) | 1.44 (1.16-1.80) | | | 1.16 (0.87-1.55) | 1.30 (0.81-2.09) | | | | \- 6186 vs 3093 | 1.39 (1.13-1.71) | 1.75 (1.26-2.44) | | | 1.33 (0.89-1.99) | 1.49 (0.78-2.87) | | | | IL1RA | | | 0.005 | 0.381 | | | 0.425 | 0.955 | | \- 6434 vs 3217 | 1.17 (1.06-1.30) | 1.15 (0.97-1.36) | | | 1.24 (1.01-1.52) | 1.14 (0.80-1.63) | | | | \- 12868 vs 6434 | 1.37 (1.06-1.77) | 1.70 (1.13-2.55) | | | 1.38 (0.93-2.05) | 1.45 (0.80-2.64) | | | | CD163 | | | 0.854 | 0.719 | | | 0.193 | 0.419 | | \- 295 vs 147 | 0.96 (0.84-1.08) | 1.03 (0.85-1.25) | | | 0.85 (0.71-1.03) | 1.12 (0.81-1.55) | | | | \- 589 vs 295 | 0.91 (0.69-1.22) | 1.03 (0.61-1.73) | | | 0.71 (0.50-1.00) | 0.80 (0.44-1.47) | | | | TREM | | | 0.221 | 0.472 | | | 0.002 | 0.132 | | \- 85 vs 42 | 0.75 (0.56-1.00) | 0.69 (0.41-1.16) | | | 0.48 (0.32-0.73) | 0.64 (0.36-1.16) | | | | \- 169 vs 85 | 1.04 (0.84-1.28) | 0.78 (0.57-1.07) | | | 0.87 (0.69-1.10) | 0.62 (0.39-1.00) | | | | Fer | | | 0.034 | 0.258 | | | 0.024 | 0.075 | | \- 243 vs 122 | 1.13 (1.02-1.26) | 1.01 (0.85-1.19) | | | 1.17 (1.01-1.35) | 0.96 (0.76-1.23) | | | | \- 487 vs 243 | 0.96 (0.77-1.20) | 0.76 (0.53-1.08) | | | 0.94 (0.71-1.25) | 0.67 (0.42-1.07) | | | | CRP | | | 0.747 | 0.622 | | | 0.662 | 0.448 | | \- 28 vs 14 | 1.03 (0.96-1.12) | 0.97 (0.79-1.20) | | | 0.99 (0.89-1.10) | 0.84 (0.59-1.20) | | | | \- 56 vs 28 | 1.02 (0.87-1.19) | 1.19 (0.92-1.54) | | | 0.96 (0.79-1.17) | 1.06 (0.75-1.49) | | | ########################################################################################### #### Appendix 6—table 3. Results from models for hospitalization endpoint. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # Get ORs and 95% CIs tmp1 <- data.frame( sort1 = rep(c(1:10), 2), sort2 = c(rep(2,10), rep(3,10)), bio = rep(c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP"), 2), ref1 = c(ref0-1, ref0), ref2 = c(ref0, ref0+1) ) %>% arrange(sort1, sort2) %>% group_by(sort1, sort2) %>% do(cbind(., # Children - single models or1c = get_est1(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo1c = get_est1(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up1c = get_est1(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Children - global model or2c = get_est2(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo2c = get_est2(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up2c = get_est2(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Adults - single models or1a = get_est1(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo1a = get_est1(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up1a = get_est1(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1), # Adults - global model or2a = get_est2(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo2a = get_est2(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up2a = get_est2(out="hospital", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1))) %>% ungroup() for (i in 6:17) {tmp1[[i]] <- sprintf("%.2f", round(tmp1[[i]],2))} # Get p-values tmp2 <- data.frame( sort1 = c(1:10), sort2 = rep(1,10), bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") ) %>% group_by(sort1) %>% do(cbind(., p.s4 = get_est1(out="hospital", bio=.$bio, est="p", dat=dat1), p.s4_int = get_est1(out="hospital", bio=.$bio, est="p int", dat=dat1), p.g4 = get_est2(out="hospital", bio=.$bio, est="p", dat=dat1), p.g4_int = get_est2(out="hospital", bio=.$bio, est="p int", dat=dat1))) %>% ungroup() for (i in 4:7) {tmp2[[i]] <- ifelse(tmp2[[i]]<0.001, "<0.001", sprintf("%.3f", round(tmp2[[i]],3)))} # Combine results res4 <- bind_rows(tmp1, tmp2) %>% arrange(sort1, sort2) %>% mutate(bio = ifelse(!is.na(ref1), paste(" -", round(2^ref2,0), "vs", round(2^ref1,0), sep=" "), as.character(bio)), or.sc4 = ifelse(is.na(lo1c), NA, paste(or1c, " (", lo1c, "-", up1c, ")", sep="")), # s: single model; c: children or.gc4 = ifelse(is.na(lo2c), NA, paste(or2c, " (", lo2c, "-", up2c, ")", sep="")), # g: global model or.sa4 = ifelse(is.na(lo1a), NA, paste(or1a, " (", lo1a, "-", up1a, ")", sep="")), # a: adults or.ga4 = ifelse(is.na(lo2a), NA, paste(or2a, " (", lo2a, "-", up2a, ")", sep=""))) %>% select(bio, or.sc4, or.sa4, p.s4, p.s4_int, or.gc4, or.ga4, p.g4, p.g4_int) names(res4) <- c("", "OR (single - children)", "OR (single - adults)", "P overall (single)", "P interaction (single)", "OR (global - children)", "OR (global - adults)", "P overall (global)", "P interaction (global)") # Report results knitr::kable(res4) ``` | | OR (single - children) | OR (single - adults) | P overall (single) | P interaction (single) | OR (global - children) | OR (global - adults) | P overall (global) | P interaction (global) | |:-----------------|:-----------------------|:---------------------|:-------------------|:-----------------------|:-----------------------|:---------------------|:-------------------|:-----------------------| | VCAM | | | <0.001 | 0.009 | | | 0.092 | 0.137 | | \- 1636 vs 818 | 1.28 (1.11-1.49) | 1.28 (1.03-1.60) | | | 1.16 (0.90-1.48) | 1.28 (0.87-1.90) | | | | \- 3272 vs 1636 | 1.42 (1.14-1.76) | 1.46 (1.08-1.97) | | | 1.16 (0.81-1.64) | 1.41 (0.84-2.37) | | | | SDC | | | <0.001 | 0.187 | | | 0.006 | 0.406 | | \- 2519 vs 1260 | 1.82 (1.01-3.28) | 0.79 (0.31-2.05) | | | 1.70 (0.84-3.41) | 0.62 (0.16-2.49) | | | | \- 5039 vs 2519 | 2.22 (1.36-3.63) | 1.81 (0.65-5.07) | | | 3.70 (1.90-7.22) | 1.66 (0.39-7.07) | | | | Ang | | | 0.012 | 0.337 | | | 0.497 | 0.789 | | \- 1204 vs 602 | 1.27 (1.07-1.52) | 1.21 (0.91-1.62) | | | 1.27 (0.92-1.77) | 1.16 (0.76-1.77) | | | | \- 2409 vs 1204 | 1.58 (1.08-2.32) | 1.61 (0.86-3.04) | | | 1.63 (0.90-2.93) | 1.45 (0.70-3.01) | | | | IL8 | | | 0.007 | 0.002 | | | 0.024 | 0.021 | | \- 14 vs 7 | 1.14 (0.90-1.44) | 0.73 (0.47-1.15) | | | 0.94 (0.66-1.33) | 0.63 (0.36-1.13) | | | | \- 28 vs 14 | 0.98 (0.81-1.18) | 1.32 (0.85-2.05) | | | 0.70 (0.50-0.97) | 1.59 (0.81-3.12) | | | | IP10 | | | 0.002 | 0.242 | | | 0.005 | 0.212 | | \- 3093 vs 1546 | 1.32 (1.13-1.54) | 1.10 (0.86-1.40) | | | 0.80 (0.56-1.14) | 0.77 (0.45-1.30) | | | | \- 6186 vs 3093 | 1.50 (1.18-1.90) | 1.17 (0.81-1.70) | | | 0.84 (0.51-1.40) | 0.66 (0.32-1.35) | | | | IL1RA | | | <0.001 | 0.685 | | | <0.001 | 0.389 | | \- 6434 vs 3217 | 1.31 (1.17-1.46) | 1.13 (0.94-1.36) | | | 2.05 (1.57-2.66) | 1.18 (0.73-1.89) | | | | \- 12868 vs 6434 | 1.41 (1.11-1.80) | 1.17 (0.79-1.72) | | | 2.25 (1.39-3.64) | 1.19 (0.62-2.28) | | | | CD163 | | | 0.208 | 0.722 | | | 0.007 | 0.419 | | \- 295 vs 147 | 0.90 (0.79-1.04) | 0.98 (0.76-1.27) | | | 0.69 (0.53-0.89) | 0.96 (0.62-1.50) | | | | \- 589 vs 295 | 0.69 (0.47-1.00) | 0.91 (0.46-1.82) | | | 0.49 (0.30-0.80) | 0.83 (0.36-1.91) | | | | TREM | | | 0.635 | 0.371 | | | 0.011 | 0.053 | | \- 85 vs 42 | 0.93 (0.67-1.29) | 1.35 (0.71-2.58) | | | 0.53 (0.34-0.81) | 1.74 (0.67-4.52) | | | | \- 169 vs 85 | 0.89 (0.71-1.13) | 0.83 (0.54-1.28) | | | 0.55 (0.36-0.83) | 0.57 (0.26-1.29) | | | | Fer | | | <0.001 | 0.011 | | | 0.129 | 0.117 | | \- 243 vs 122 | 1.22 (1.08-1.38) | 0.92 (0.72-1.18) | | | 1.23 (1.02-1.49) | 0.91 (0.68-1.21) | | | | \- 487 vs 243 | 1.40 (1.10-1.79) | 0.99 (0.68-1.46) | | | 1.25 (0.89-1.75) | 0.93 (0.54-1.59) | | | | CRP | | | 0.379 | 0.190 | | | 0.139 | 0.053 | | \- 28 vs 14 | 0.97 (0.89-1.06) | 1.01 (0.83-1.24) | | | 0.97 (0.86-1.11) | 0.95 (0.71-1.27) | | | | \- 56 vs 28 | 0.95 (0.78-1.15) | 1.35 (1.01-1.81) | | | 0.89 (0.69-1.15) | 1.37 (0.88-2.13) | | | ########################################################################################### #### Appendix 7—table 1. Model selection frequencies for children. ``` r # EPV -------------------------------------------------------------- pred <- c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") # Bootstrap results ------------------------------------------------ ## The following codes were modified from Heinze G. et al (https://doi.org/10.1002/bimj.201700067) bootnum <- 1000 load("bootstrap_results.RData") # Load bootstrap results # Model frequency group_cols <- c(colnames(data.frame(boot_varc))[1:length(pred)]) boot_modfreq <- data.frame(boot_varc) %>% mutate(time = 1) %>% group_by_(.dots = group_cols) %>% summarise(aic_median = round(median(aic),1), aic_025 = round(quantile(aic, 0.025),1), aic_975 = round(quantile(aic, 0.975),1), count = sum(time)) %>% ungroup() %>% mutate(percent = count/bootnum * 100) %>% arrange(desc(count)) %>% slice(1:20) %>% as.data.frame(.) out <- boot_modfreq for (i in 1:(ncol(out)-5)) { out[,i] <- ifelse(out[,i]==0, NA, "+") } knitr::kable(out) ``` | VCAM | SDC | Ang | IL8 | IP10 | IL1RA | CD163 | TREM | Fer | CRP | aic\_median | aic\_025 | aic\_975 | count | percent | |:-----|:----|:----|:----|:-----|:------|:------|:-----|:----|:----|------------:|---------:|---------:|------:|--------:| | | \+ | \+ | \+ | \+ | \+ | | | \+ | | 457.9 | 413.0 | 500.7 | 134 | 13.4 | | | \+ | \+ | \+ | \+ | \+ | \+ | | \+ | | 453.5 | 415.8 | 495.6 | 100 | 10.0 | | | | \+ | \+ | \+ | \+ | \+ | | \+ | | 459.7 | 401.8 | 500.6 | 55 | 5.5 | | | | \+ | \+ | \+ | \+ | | | \+ | | 459.2 | 420.8 | 490.4 | 54 | 5.4 | | \+ | \+ | \+ | \+ | | \+ | \+ | | \+ | | 462.1 | 420.2 | 507.5 | 48 | 4.8 | | \+ | \+ | \+ | \+ | \+ | \+ | | | \+ | | 450.2 | 422.3 | 492.1 | 47 | 4.7 | | \+ | \+ | \+ | \+ | \+ | \+ | \+ | | \+ | | 460.6 | 418.4 | 502.0 | 46 | 4.6 | | \+ | \+ | \+ | \+ | | \+ | | | \+ | | 457.6 | 417.7 | 497.6 | 40 | 4.0 | | | \+ | \+ | \+ | \+ | \+ | | | \+ | \+ | 451.9 | 413.7 | 493.5 | 39 | 3.9 | | | \+ | \+ | \+ | \+ | \+ | \+ | \+ | \+ | | 453.2 | 394.6 | 491.9 | 36 | 3.6 | | | \+ | \+ | \+ | \+ | \+ | \+ | | \+ | \+ | 452.8 | 418.2 | 484.7 | 28 | 2.8 | | | \+ | \+ | \+ | \+ | \+ | | \+ | \+ | | 441.9 | 407.8 | 486.8 | 23 | 2.3 | | \+ | | \+ | \+ | \+ | \+ | | | \+ | | 461.4 | 434.5 | 490.8 | 23 | 2.3 | | \+ | | \+ | \+ | | \+ | | | \+ | | 466.8 | 427.4 | 481.0 | 17 | 1.7 | | | \+ | \+ | \+ | \+ | \+ | | \+ | \+ | \+ | 449.9 | 431.1 | 482.4 | 15 | 1.5 | | \+ | \+ | \+ | \+ | | \+ | | | \+ | \+ | 449.3 | 423.0 | 486.2 | 14 | 1.4 | | | | \+ | \+ | \+ | \+ | | | \+ | \+ | 449.1 | 420.8 | 477.8 | 13 | 1.3 | | \+ | | \+ | \+ | \+ | \+ | \+ | | \+ | | 474.9 | 442.2 | 488.7 | 12 | 1.2 | | \+ | \+ | \+ | \+ | \+ | \+ | | \+ | \+ | | 451.0 | 416.7 | 471.9 | 12 | 1.2 | | | | \+ | \+ | \+ | \+ | \+ | | \+ | \+ | 453.9 | 435.2 | 492.2 | 11 | 1.1 | ########################################################################################### #### Appendix 7—table 2. Model stability for children. ``` r # EPV -------------------------------------------------------------- pred <- c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP") # Estimate full model ---------------------------------------------- full_mod <- glm(sev.or.inte ~ VCAM + SDC + Ang + IL8 + IP10 + IL1RA + CD163 + TREM + Fer + CRP, family=binomial, data=dat1c, x=T, y=T) full_est <- coef(full_mod) full_se <- coef(summary(full_mod))[, "Std. Error"] pred_name <- names(full_est)[-1] # Selected model (for bootstrap results) --------------------------- sel_m <- dredge(full_mod, rank="AIC") sel_var <- matrix(0, ncol = length(pred), nrow = 1, dimnames = list(NULL, pred)) sel_var_tmp <- attr(get.models(sel_m, 1)[[1]]$terms, "term.labels") for (j in 1:ncol(sel_var)) { sel_var[1,j] <- ifelse(names(sel_var[1,j]) %in% sel_var_tmp, 1, 0) } formula <- paste("sev.or.inte~", paste(names(sel_var[1,][sel_var[1,]==1]), collapse = "+")) sel_mod <- glm(formula, data = dat1, family = binomial, x = T, y = T) sel_est <- coef(sel_mod[1])[c("(Intercept)", pred_name)] sel_est[is.na(sel_est)] <- 0 names(sel_est) <- c("(Intercept)", pred_name) sel_se <- coef(summary(sel_mod))[, "Std. Error"][c("(Intercept)", pred_name)] sel_se[is.na(sel_se)] <- 0 # Bootstrap results ------------------------------------------------ ## The following codes were modified from Heinze G. et al (https://doi.org/10.1002/bimj.201700067) bootnum <- 1000 load("bootstrap_results.RData") # Load bootstrap results boot_01 <- (boot_estc != 0) * 1 boot_inclusion <- apply(boot_01, 2, function(x) sum(x) / length(x) * 100) ## Overview of estimates and measures sqe <- (t(boot_estc) - full_est) ^ 2 rmsd <- apply(sqe, 1, function(x) sqrt(mean(x))) rmsdratio <- rmsd / full_se boot_mean <- apply(boot_estc, 2, mean) boot_meanratio <- boot_mean / full_est boot_relbias <- (boot_meanratio / (boot_inclusion / 100) - 1) * 100 boot_median <- apply(boot_estc, 2, median) boot_025per <- apply(boot_estc, 2, function(x) quantile(x, 0.025)) boot_975per <- apply(boot_estc, 2, function(x) quantile(x, 0.975)) overview <- round(cbind(full_est, full_se, boot_inclusion, sel_est, sel_se, rmsdratio, boot_relbias, boot_median, boot_025per, boot_975per), 4) overview <- overview[order(overview[,"boot_inclusion"], decreasing=T),] knitr::kable(overview) ``` | | full\_est | full\_se | boot\_inclusion | sel\_est | sel\_se | rmsdratio | boot\_relbias | boot\_median | boot\_025per | boot\_975per | |:------------|----------:|---------:|----------------:|---------:|--------:|----------:|--------------:|-------------:|-------------:|-------------:| | (Intercept) | -20.6801 | 3.0499 | 100.0 | -15.7908 | 1.8360 | 1.1330 | -1.4929 | -20.3233 | -26.6990 | -14.0696 | | IL1RA | 0.7604 | 0.1443 | 100.0 | 0.5344 | 0.0892 | 1.2352 | 1.6338 | 0.7651 | 0.4408 | 1.1236 | | Ang | 0.5203 | 0.1542 | 98.2 | 0.1500 | 0.0830 | 1.1710 | 7.6528 | 0.5416 | 0.2268 | 0.8804 | | IL8 | -0.4250 | 0.1338 | 97.2 | -0.0238 | 0.0851 | 1.0597 | 4.5484 | -0.4341 | -0.6995 | 0.0000 | | Fer | 0.2951 | 0.1044 | 93.5 | 0.0226 | 0.0612 | 1.2233 | 11.1579 | 0.3118 | 0.0000 | 0.5358 | | IP10 | -0.2145 | 0.1079 | 75.9 | -0.1243 | 0.0586 | 1.3272 | 32.1767 | -0.2398 | -0.4676 | 0.0000 | | SDC | 0.5079 | 0.2487 | 73.5 | 0.7102 | 0.1543 | 1.2692 | 18.5555 | 0.4923 | 0.0000 | 1.0035 | | CD163 | 0.1778 | 0.1433 | 45.8 | 0.0000 | 0.0000 | 1.1935 | 69.8797 | 0.0000 | 0.0000 | 0.4926 | | VCAM | -0.0500 | 0.0541 | 35.2 | 0.0000 | 0.0000 | 1.1525 | 125.6348 | 0.0000 | -0.1763 | 0.0000 | | TREM | -0.0689 | 0.1375 | 20.0 | 0.0000 | 0.0000 | 0.9176 | 159.0696 | 0.0000 | -0.3584 | 0.2162 | | CRP | 0.0438 | 0.0712 | 19.2 | 0.0000 | 0.0000 | 0.8804 | 173.2343 | 0.0000 | 0.0000 | 0.1754 | ########################################################################################### #### Appendix 7—table 3. Model selection frequencies for adults. ``` r # EPV -------------------------------------------------------------- pred <- c("VCAM", "SDC", "Ang", "IL8", "ns1(IP10)", "IL1RA", "CD163", "TREM", "Fer", "CRP") # Bootstrap results ------------------------------------------------ ## The following codes were modified from Heinze G. et al (https://doi.org/10.1002/bimj.201700067) bootnum <- 1000 load("bootstrap_results.RData") # Load bootstrap results ## Model frequency group_cols <- c(colnames(data.frame(boot_vara))[1:length(pred)]) boot_modfreq <- data.frame(boot_vara) %>% mutate(time = 1) %>% group_by_(.dots = group_cols) %>% summarise(aic_median = round(median(aic),1), aic_025 = round(quantile(aic, 0.025),1), aic_975 = round(quantile(aic, 0.975),1), count = sum(time)) %>% ungroup() %>% mutate(percent = count/bootnum * 100) %>% arrange(desc(count)) %>% slice(1:20) %>% as.data.frame(.) out <- boot_modfreq for (i in 1:(ncol(out)-5)) { out[,i] <- ifelse(out[,i]==0, NA, "+") } knitr::kable(out) ``` | VCAM | SDC | Ang | IL8 | ns1.IP10. | IL1RA | CD163 | TREM | Fer | CRP | aic\_median | aic\_025 | aic\_975 | count | percent | |:-----|:----|:----|:----|:----------|:------|:------|:-----|:----|:----|------------:|---------:|---------:|------:|--------:| | | \+ | | \+ | \+ | \+ | \+ | \+ | \+ | | 407.3 | 381.3 | 442.8 | 79 | 7.9 | | \+ | \+ | | \+ | \+ | \+ | \+ | \+ | \+ | | 415.0 | 374.8 | 444.2 | 55 | 5.5 | | \+ | \+ | | \+ | \+ | \+ | | \+ | \+ | | 418.0 | 380.9 | 457.7 | 36 | 3.6 | | | \+ | | \+ | | \+ | | \+ | \+ | | 420.5 | 389.1 | 451.4 | 33 | 3.3 | | | \+ | | \+ | | | | \+ | \+ | \+ | 415.6 | 371.4 | 445.2 | 30 | 3.0 | | | \+ | \+ | \+ | \+ | \+ | \+ | \+ | \+ | | 406.1 | 374.3 | 434.3 | 29 | 2.9 | | | \+ | | \+ | | | \+ | \+ | \+ | \+ | 410.3 | 375.1 | 434.6 | 26 | 2.6 | | | \+ | | \+ | | \+ | | | \+ | | 428.9 | 390.0 | 460.7 | 25 | 2.5 | | | \+ | | \+ | | \+ | | \+ | \+ | \+ | 403.8 | 388.6 | 438.9 | 24 | 2.4 | | | \+ | | \+ | \+ | | \+ | \+ | \+ | \+ | 421.0 | 384.4 | 443.8 | 20 | 2.0 | | \+ | \+ | | \+ | | \+ | \+ | \+ | \+ | | 405.8 | 384.2 | 432.4 | 20 | 2.0 | | \+ | \+ | | \+ | \+ | \+ | \+ | \+ | \+ | \+ | 404.9 | 374.6 | 445.0 | 20 | 2.0 | | \+ | \+ | | \+ | \+ | \+ | \+ | | \+ | | 403.1 | 384.9 | 426.5 | 19 | 1.9 | | | \+ | | \+ | \+ | | \+ | \+ | \+ | | 424.0 | 401.4 | 450.9 | 17 | 1.7 | | | \+ | | \+ | | | | | \+ | \+ | 436.1 | 409.6 | 469.2 | 16 | 1.6 | | | \+ | | \+ | | | \+ | | \+ | | 422.7 | 388.3 | 455.4 | 16 | 1.6 | | | \+ | | \+ | | | \+ | \+ | \+ | | 416.3 | 396.7 | 465.2 | 16 | 1.6 | | | \+ | | \+ | \+ | | \+ | | \+ | | 417.4 | 388.5 | 451.2 | 16 | 1.6 | | | \+ | | \+ | \+ | \+ | | \+ | \+ | | 417.9 | 396.0 | 457.2 | 16 | 1.6 | | | \+ | | \+ | | \+ | \+ | \+ | \+ | | 412.1 | 389.1 | 435.4 | 15 | 1.5 | ########################################################################################### #### Appendix 7—table 4. Model stability for adults. ``` r # EPV -------------------------------------------------------------- pred <- c("VCAM", "SDC", "Ang", "IL8", "ns1(IP10)", "IL1RA", "CD163", "TREM", "Fer", "CRP") # Estimate full model ---------------------------------------------- full_mod <- glm(sev.or.inte ~ VCAM + SDC + Ang + IL8 + ns1(IP10) + IL1RA + CD163 + TREM + Fer + CRP, family=binomial, data=dat1a, x=T, y=T) full_est <- coef(full_mod) full_se <- coef(summary(full_mod))[, "Std. Error"] pred_name <- names(full_est)[-1] # Selected model (for bootstrap results) --------------------------- sel_m <- dredge(full_mod, rank="AIC") sel_var <- matrix(0, ncol = length(pred), nrow = 1, dimnames = list(NULL, pred)) sel_var_tmp <- attr(get.models(sel_m, 1)[[1]]$terms, "term.labels") for (j in 1:ncol(sel_var)) { sel_var[1,j] <- ifelse(names(sel_var[1,j]) %in% sel_var_tmp, 1, 0) } formula <- paste("sev.or.inte~", paste(names(sel_var[1,][sel_var[1,]==1]), collapse = "+")) sel_mod <- glm(formula, data = dat1, family = binomial, x = T, y = T) sel_est <- coef(sel_mod[1])[c("(Intercept)", pred_name)] sel_est[is.na(sel_est)] <- 0 names(sel_est) <- c("(Intercept)", pred_name) sel_se <- coef(summary(sel_mod))[, "Std. Error"][c("(Intercept)", pred_name)] sel_se[is.na(sel_se)] <- 0 # Bootstrap results ------------------------------------------------ ## The following codes were modified from Heinze G. et al (https://doi.org/10.1002/bimj.201700067) bootnum <- 1000 load("bootstrap_results.RData") # Load bootstrap results boot_01 <- (boot_esta != 0) * 1 boot_inclusion <- apply(boot_01, 2, function(x) sum(x) / length(x) * 100) ## Overview of estimates and measures sqe <- (t(boot_esta) - full_est) ^ 2 rmsd <- apply(sqe, 1, function(x) sqrt(mean(x))) rmsdratio <- rmsd / full_se boot_mean <- apply(boot_esta, 2, mean) boot_meanratio <- boot_mean / full_est boot_relbias <- (boot_meanratio / (boot_inclusion / 100) - 1) * 100 boot_median <- apply(boot_esta, 2, median) boot_025per <- apply(boot_esta, 2, function(x) quantile(x, 0.025)) boot_975per <- apply(boot_esta, 2, function(x) quantile(x, 0.975)) overview <- round(cbind(full_est, full_se, boot_inclusion, sel_est, sel_se, rmsdratio, boot_relbias, boot_median, boot_025per, boot_975per), 4) overview <- overview[order(overview[,"boot_inclusion"], decreasing=T),] knitr::kable(overview) ``` | | full\_est | full\_se | boot\_inclusion | sel\_est | sel\_se | rmsdratio | boot\_relbias | boot\_median | boot\_025per | boot\_975per | |:------------|----------:|---------:|----------------:|---------:|--------:|----------:|--------------:|-------------:|-------------:|-------------:| | (Intercept) | -16.6085 | 3.2632 | 100.0 | -16.1181 | 2.0300 | 1.3263 | 2.0586 | -16.7262 | -26.4755 | -9.8095 | | SDC | 1.1524 | 0.2708 | 99.2 | 0.8260 | 0.1575 | 1.2745 | 3.0018 | 1.1767 | 0.5450 | 1.8615 | | IL8 | 0.5473 | 0.1427 | 98.9 | 0.0378 | 0.0842 | 1.2973 | 5.8880 | 0.5739 | 0.2490 | 0.9520 | | Fer | -0.2785 | 0.0916 | 94.6 | -0.0083 | 0.0619 | 1.2479 | 7.9330 | -0.2845 | -0.5012 | 0.0000 | | TREM | -0.2961 | 0.1499 | 66.5 | -0.2171 | 0.0914 | 1.4191 | 28.1687 | -0.2807 | -0.6448 | 0.0000 | | IL1RA | 0.2582 | 0.1583 | 62.3 | 0.5217 | 0.0961 | 1.4788 | 54.9829 | 0.2494 | 0.0000 | 0.7255 | | ns1(IP10)1 | -1.4427 | 1.0592 | 59.8 | -1.0181 | 0.4709 | 1.6064 | 36.8995 | -0.2108 | -5.2628 | 0.7003 | | ns1(IP10)2 | -0.1027 | 0.5763 | 59.8 | 0.1119 | 0.3226 | 1.2060 | 43.1473 | 0.0000 | -1.7021 | 1.2589 | | CD163 | 0.2056 | 0.1287 | 59.1 | 0.1343 | 0.0869 | 1.3333 | 51.3932 | 0.2148 | 0.0000 | 0.4989 | | CRP | 0.0863 | 0.0990 | 36.3 | 0.0000 | 0.0000 | 1.1203 | 129.8186 | 0.0000 | 0.0000 | 0.3048 | | VCAM | 0.0660 | 0.0685 | 35.4 | 0.0000 | 0.0000 | 1.3808 | 98.3111 | 0.0000 | -0.0923 | 0.2714 | | Ang | -0.0246 | 0.1193 | 21.8 | 0.0000 | 0.0000 | 1.0047 | 62.8642 | 0.0000 | -0.2792 | 0.3008 | ########################################################################################### #### Appendix 8—figure 1. Results from models for severe/moderate dengue including viremia as a potential biomarker. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # For children dd$limits["Adjust to","Age"] <- 10 dat_m1 <- get_pred1(out="sev.or.inte", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m2 <- get_pred1(out="sev.or.inte", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m3 <- get_pred1(out="sev.or.inte", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m4 <- get_pred1(out="sev.or.inte", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m5 <- get_pred1(out="sev.or.inte", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m6 <- get_pred1(out="sev.or.inte", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m7 <- get_pred1(out="sev.or.inte", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m8 <- get_pred1(out="sev.or.inte", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m9 <- get_pred1(out="sev.or.inte", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m10 <- get_pred1(out="sev.or.inte", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_m11 <- get_pred1(out="sev.or.inte", bio="Vir", age=10, dat=dat1) %>% rename(value=Vir) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg1 <- get_pred2v(out="sev.or.inte", bio="VCAM", age=10, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg2 <- get_pred2v(out="sev.or.inte", bio="SDC", age=10, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg3 <- get_pred2v(out="sev.or.inte", bio="Ang", age=10, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg4 <- get_pred2v(out="sev.or.inte", bio="IL8", age=10, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg5 <- get_pred2v(out="sev.or.inte", bio="IP10", age=10, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg6 <- get_pred2v(out="sev.or.inte", bio="IL1RA", age=10, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg7 <- get_pred2v(out="sev.or.inte", bio="CD163", age=10, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg8 <- get_pred2v(out="sev.or.inte", bio="TREM", age=10, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg9 <- get_pred2v(out="sev.or.inte", bio="Fer", age=10, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg10 <- get_pred2v(out="sev.or.inte", bio="CRP", age=10, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_mg11 <- get_pred2v(out="sev.or.inte", bio="Vir", age=10, dat=dat1) %>% rename(value=Vir) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="Under 15")) dat_p1 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10, dat_m11,dat_mg11) %>% mutate(age = "10 years") # For adults dd$limits["Adjust to","Age"] <- 25 dat_m1 <- get_pred1(out="sev.or.inte", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m2 <- get_pred1(out="sev.or.inte", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m3 <- get_pred1(out="sev.or.inte", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m4 <- get_pred1(out="sev.or.inte", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m5 <- get_pred1(out="sev.or.inte", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m6 <- get_pred1(out="sev.or.inte", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m7 <- get_pred1(out="sev.or.inte", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m8 <- get_pred1(out="sev.or.inte", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m9 <- get_pred1(out="sev.or.inte", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m10 <- get_pred1(out="sev.or.inte", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_m11 <- get_pred1(out="sev.or.inte", bio="Vir", age=25, dat=dat1) %>% rename(value=Vir) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg1 <- get_pred2v(out="sev.or.inte", bio="VCAM", age=25, dat=dat1) %>% rename(value=VCAM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg2 <- get_pred2v(out="sev.or.inte", bio="SDC", age=25, dat=dat1) %>% rename(value=SDC) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg3 <- get_pred2v(out="sev.or.inte", bio="Ang", age=25, dat=dat1) %>% rename(value=Ang) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg4 <- get_pred2v(out="sev.or.inte", bio="IL8", age=25, dat=dat1) %>% rename(value=IL8) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg5 <- get_pred2v(out="sev.or.inte", bio="IP10", age=25, dat=dat1) %>% rename(value=IP10) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg6 <- get_pred2v(out="sev.or.inte", bio="IL1RA", age=25, dat=dat1) %>% rename(value=IL1RA) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg7 <- get_pred2v(out="sev.or.inte", bio="CD163", age=25, dat=dat1) %>% rename(value=CD163) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg8 <- get_pred2v(out="sev.or.inte", bio="TREM", age=25, dat=dat1) %>% rename(value=TREM) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg9 <- get_pred2v(out="sev.or.inte", bio="Fer", age=25, dat=dat1) %>% rename(value=Fer) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg10 <- get_pred2v(out="sev.or.inte", bio="CRP", age=25, dat=dat1) %>% rename(value=CRP) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_mg11 <- get_pred2v(out="sev.or.inte", bio="Vir", age=25, dat=dat1) %>% rename(value=Vir) %>% select(value, yhat, lower, upper, biomarker, model) %>% slice(which(dat1$age15=="15 and above")) dat_p2 <- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10, dat_m11,dat_mg11) %>% mutate(age = "25 years") # Merge data for children and adults vis1 <- rbind(dat_p1, dat_p2) %>% mutate(outcome = "outcome 1") # Merge data for plots tmp0 <- dat %>% arrange(age15) %>% select(sev.or.inte) tmp <- data.frame(sev.or.inte = rep(tmp0$sev.or.inte, 22)) tmp_p1 <- vis1 %>% arrange(biomarker, model) %>% mutate(value1 = 2^value, age = factor(age, levels=c("10 years", "25 years")), gr = ifelse(model=="Single model" & age=="10 years", 1, ifelse(model=="Single model" & age=="25 years", 2, ifelse(model=="Global model" & age=="10 years", 3, 4))), gr = factor(gr, levels=c(1:4), labels=c("Single children", "Single adults", "Global children", "Global adults")), model = factor(model, levels=c("Single model", "Global model"))) %>% bind_cols(., tmp) vline <- tmp_p1 %>% filter(model=="Single model") %>% filter(yhat==0) %>% select(biomarker, value, value1) %>% rename(vline=value, vline1=value1) dat_p <- left_join(tmp_p1, vline, by="biomarker") # Alternative data to limit to 5th - 95th of the values dat_alt <- dat_p %>% filter(value != 0) %>% group_by(biomarker, model) %>% mutate(up = quantile(value, .95), lo = quantile(value, .05)) %>% ungroup() %>% mutate(is.outlier = value<lo | value>up | value<0, value = ifelse(is.outlier, NA, value), value1 = ifelse(is.outlier, NA, value1), yhat = ifelse(is.outlier, NA, yhat), lower = ifelse(is.outlier, NA, lower), upper = ifelse(is.outlier, NA, upper)) dat_1_5 <- dat_alt %>% filter(biomarker %in% c("VCAM", "SDC", "Ang", "IL8", "IP10")) %>% mutate(biomarker = factor(biomarker, levels=c("VCAM", "SDC", "Ang", "IL8", "IP10"))) dat_6_11 <- dat_alt %>% filter(biomarker %in% c("IL1RA", "CD163", "TREM", "Fer", "CRP", "Vir")) %>% mutate(biomarker = factor(biomarker, levels=c("IL1RA", "CD163", "TREM", "Fer", "CRP", "Vir"))) # Modify facets' scales #library(facetscales) xVCAM <- c(1,4,15,60,250,1000,4000) xSDC <- c(1400,2000,2800,4000,5600) xAng <- c(50,100,250,500,1000,2000) xIL8 <- c(5,7,10,14,20,28,40) xIP10 <- c(25,100,400,1600,6400) xIL1RA <- c(1000,2000,4000,8000,16000) xCD163 <- c(75,150,300,600) xTREM <- c(35,50,70,100,140,200) xFer <- c(50,100,200,400,800) xCRP <- c(2.5,5,10,20,40,80) xVir <- c(0:10) scales_x <- list( `VCAM` = scale_x_continuous(breaks = log2(xVCAM), labels = xVCAM), `SDC` = scale_x_continuous(breaks = log2(xSDC), labels = xSDC), `Ang` = scale_x_continuous(breaks = log2(xAng), labels = xAng), `IL8` = scale_x_continuous(breaks = log2(xIL8), labels = xIL8), `IP10` = scale_x_continuous(breaks = log2(xIP10), labels = xIP10), `IL1RA` = scale_x_continuous(breaks = log2(xIL1RA), labels = xIL1RA), `CD163` = scale_x_continuous(breaks = log2(xCD163), labels = xCD163), `TREM` = scale_x_continuous(breaks = log2(xTREM), labels = xTREM), `Fer` = scale_x_continuous(breaks = log2(xFer), labels = xFer), `CRP` = scale_x_continuous(breaks = log2(xCRP), labels = xCRP), `Vir` = scale_x_continuous(breaks = xVir, labels = xVir) ) ybreak1 <- c(.125, .25, .5, 1, 2, 4, 8) ybreak2 <- c(.125, .25, .5, 1, 2, 4) scales_y1 <- list( `Single model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1), `Global model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1) ) scales_y2 <- list( `Single model` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2), `Global model` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2) ) # Set facets' names models <- c(`Single model` = "Single model", `Global model` = "Global model") biomarkers <- c(`VCAM` = "VCAM-1 (ng/ml)", `SDC`= "SDC-1 (pg/ml)", `Ang` = "Ang-2 (pg/ml)", `IL8` = "IL-8 (pg/ml)", `IP10` = "IP-10 (pg/ml)", `IL1RA` = "IL-1RA (pg/ml)", `CD163` = "sCD163 (ng/ml)", `TREM` = "sTREM-1 (pg/ml)", `Fer` = "Ferritin (ng/ml)", `CRP` = "CRP (mg/l)", `Vir` = "Log10 viremia") # Plot for the first 5 biomarkers p.1.5 <- ggplot(dat_1_5, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_1_5, model=="Single model" & sev.or.inte==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Single model" & sev.or.inte==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_1_5, model=="Global model" & sev.or.inte==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_1_5, model=="Global model" & sev.or.inte==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="top", legend.title=element_blank(), axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y1), labeller = as_labeller(c(models, biomarkers))) # Plot for the last 5 biomarkers p.6.11 <- ggplot(dat_6_11, aes(x=value)) + geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) + geom_vline(aes(xintercept=vline), color="black", linetype="dashed", alpha=.4) + geom_hline(yintercept=0, color="black", linetype="dashed", alpha=.4) + geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) + geom_rug(data=filter(dat_6_11, model=="Single model" & sev.or.inte==0 & age=="10 years"), sides="b", alpha=.2, color="red") + geom_rug(data=filter(dat_6_11, model=="Single model" & sev.or.inte==1 & age=="10 years"), sides="t", alpha=.2, color="red") + geom_rug(data=filter(dat_6_11, model=="Global model" & sev.or.inte==0 & age=="25 years"), sides="b", alpha=.2, color="blue") + geom_rug(data=filter(dat_6_11, model=="Global model" & sev.or.inte==1 & age=="25 years"), sides="t", alpha=.2, color="blue") + scale_color_manual(values=c("red","blue"), labels=c("Children","Adults")) + scale_fill_manual(values=c("red","blue"), labels=c("Children","Adults")) + ylab("Odds ratio") + theme(legend.position="none", axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) + facet_grid_sc(cols=vars(biomarker), rows=vars(model), scales=list(x=scales_x, y=scales_y2), labeller = as_labeller(c(models, biomarkers))) # Merge plots p81 <- gridExtra::grid.arrange(p.1.5, p.6.11, nrow=2, heights=c(1,.86)) ``` <!-- --> ``` r #ggsave(filename="A8_fig1.pdf", p81, dpi=300, width=8.5, height=6.2) ``` ########################################################################################### #### Appendix 8—table 1. Results from models for severe/moderate dengue including viremia as a potential biomarker. ``` r # Set datadist for 'lrm' function [rms] dd <- datadist(dat1); options(datadist="dd") # Set references to calculate results ref0v <- c(ref0, median(dat1$Vir)) # Get ORs and 95% CIs tmp1 <- data.frame( sort1 = rep(c(1:11), 2), sort2 = c(rep(2,11), rep(3,11)), bio = rep(c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP", "Vir"), 2), ref1 = c(ref0v-1, ref0v), ref2 = c(ref0v, ref0v+1) ) %>% arrange(sort1, sort2) %>% group_by(sort1, sort2) %>% do(cbind(., # Children - single models or1c = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo1c = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up1c = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Children - global model or2c = get_est2v(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=10, dat=dat1), lo2c = get_est2v(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=10, dat=dat1), up2c = get_est2v(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=10, dat=dat1), # Adults - single models or1a = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo1a = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up1a = get_est1(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1), # Adults - global model or2a = get_est2v(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="OR", age=25, dat=dat1), lo2a = get_est2v(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="loCI", age=25, dat=dat1), up2a = get_est2v(out="sev.or.inte", bio=.$bio, ref1=.$ref1, ref2=.$ref2, est="upCI", age=25, dat=dat1))) %>% ungroup() for (i in 6:17) {tmp1[[i]] <- sprintf("%.2f", round(tmp1[[i]],2))} # Get p-values tmp2 <- data.frame( sort1 = c(1:11), sort2 = rep(1,11), bio = c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP", "Vir") ) %>% group_by(sort1) %>% do(cbind(., p.s1 = get_est1(out="sev.or.inte", bio=.$bio, est="p", dat=dat1), p.s1_int = get_est1(out="sev.or.inte", bio=.$bio, est="p int", dat=dat1), p.g1 = get_est2v(out="sev.or.inte", bio=.$bio, est="p", dat=dat1), p.g1_int = get_est2v(out="sev.or.inte", bio=.$bio, est="p int", dat=dat1))) %>% ungroup() for (i in 4:7) {tmp2[[i]] <- ifelse(tmp2[[i]]<0.001, "<0.001", sprintf("%.3f", round(tmp2[[i]],3)))} # Combine results res1 <- bind_rows(tmp1, tmp2) %>% arrange(sort1, sort2) %>% mutate(bio = ifelse(is.na(ref1), as.character(bio), ifelse(bio!="Vir", paste(" -", round(2^ref2,0), "vs", round(2^ref1,0), sep=" "), paste(" -", round(ref2,1), "vs", round(ref1,1), sep=" "))), or.sc1 = ifelse(is.na(lo1c), NA, paste(or1c, " (", lo1c, "-", up1c, ")", sep="")), # s: single model; c: children or.gc1 = ifelse(is.na(lo2c), NA, paste(or2c, " (", lo2c, "-", up2c, ")", sep="")), # g: global model or.sa1 = ifelse(is.na(lo1a), NA, paste(or1a, " (", lo1a, "-", up1a, ")", sep="")), # a: adults or.ga1 = ifelse(is.na(lo2a), NA, paste(or2a, " (", lo2a, "-", up2a, ")", sep=""))) %>% select(bio, or.sc1, or.sa1, p.s1, p.s1_int, or.gc1, or.ga1, p.g1, p.g1_int) names(res1) <- c("", "OR (single - children)", "OR (single - adults)", "P overall (single)", "P interaction (single)", "OR (global - children)", "OR (global - adults)", "P overall (global)", "P interaction (global)") # Report results knitr::kable(res1) ``` | | OR (single - children) | OR (single - adults) | P overall (single) | P interaction (single) | OR (global - children) | OR (global - adults) | P overall (global) | P interaction (global) | |:-----------------|:-----------------------|:---------------------|:-------------------|:-----------------------|:-----------------------|:---------------------|:-------------------|:-----------------------| | VCAM | | | <0.001 | 0.715 | | | 0.286 | 0.136 | | \- 1636 vs 818 | 1.20 (1.04-1.38) | 1.35 (1.15-1.58) | | | 0.94 (0.76-1.17) | 1.34 (1.03-1.74) | | | | \- 3272 vs 1636 | 1.25 (1.02-1.53) | 1.48 (1.19-1.85) | | | 0.93 (0.69-1.24) | 1.45 (1.02-2.04) | | | | SDC | | | <0.001 | 0.088 | | | 0.005 | 0.645 | | \- 2519 vs 1260 | 2.67 (1.31-5.43) | 3.33 (1.32-8.42) | | | 2.07 (0.78-5.47) | 4.28 (1.27-14.43) | | | | \- 5039 vs 2519 | 1.71 (1.18-2.47) | 3.71 (2.09-6.58) | | | 1.71 (0.95-3.09) | 2.55 (1.17-5.57) | | | | Ang | | | <0.001 | 0.524 | | | 0.060 | 0.070 | | \- 1204 vs 602 | 1.64 (1.39-1.94) | 1.51 (1.26-1.82) | | | 1.62 (1.19-2.20) | 0.97 (0.71-1.34) | | | | \- 2409 vs 1204 | 2.21 (1.58-3.10) | 2.00 (1.40-2.85) | | | 1.92 (1.22-3.01) | 0.96 (0.61-1.49) | | | | IL8 | | | <0.001 | <0.001 | | | <0.001 | <0.001 | | \- 14 vs 7 | 1.42 (1.05-1.91) | 2.18 (1.47-3.24) | | | 0.89 (0.61-1.31) | 1.60 (0.99-2.59) | | | | \- 28 vs 14 | 0.99 (0.78-1.25) | 2.33 (1.63-3.33) | | | 0.52 (0.36-0.77) | 1.96 (1.28-3.02) | | | | IP10 | | | <0.001 | 0.984 | | | 0.150 | 0.500 | | \- 3093 vs 1546 | 1.46 (1.26-1.68) | 1.45 (1.21-1.73) | | | 0.93 (0.73-1.19) | 0.75 (0.53-1.06) | | | | \- 6186 vs 3093 | 1.68 (1.35-2.09) | 1.69 (1.29-2.22) | | | 1.07 (0.76-1.50) | 0.75 (0.47-1.20) | | | | IL1RA | | | <0.001 | 0.082 | | | <0.001 | 0.062 | | \- 6434 vs 3217 | 1.69 (1.42-2.03) | 1.48 (1.21-1.81) | | | 1.97 (1.42-2.73) | 1.40 (0.93-2.09) | | | | \- 12868 vs 6434 | 1.82 (1.46-2.27) | 1.70 (1.29-2.24) | | | 2.03 (1.41-2.92) | 1.38 (0.87-2.19) | | | | CD163 | | | <0.001 | 0.551 | | | 0.124 | 0.289 | | \- 295 vs 147 | 1.57 (1.14-2.15) | 1.49 (1.13-1.98) | | | 1.51 (0.94-2.42) | 1.30 (0.86-1.98) | | | | \- 589 vs 295 | 1.46 (1.10-1.93) | 1.61 (1.09-2.37) | | | 1.24 (0.88-1.73) | 1.44 (0.91-2.28) | | | | TREM | | | 0.059 | 0.997 | | | 0.745 | 0.594 | | \- 85 vs 42 | 1.87 (1.23-2.84) | 1.79 (1.10-2.93) | | | 1.16 (0.71-1.91) | 1.24 (0.65-2.36) | | | | \- 169 vs 85 | 1.12 (0.91-1.38) | 1.12 (0.82-1.53) | | | 0.92 (0.67-1.27) | 0.67 (0.41-1.10) | | | | Fer | | | 0.042 | 0.054 | | | 0.007 | 0.002 | | \- 243 vs 122 | 1.18 (1.01-1.38) | 1.06 (0.89-1.27) | | | 1.32 (1.04-1.67) | 0.77 (0.60-0.98) | | | | \- 487 vs 243 | 1.26 (1.00-1.58) | 0.90 (0.66-1.23) | | | 1.22 (0.89-1.68) | 0.64 (0.42-0.98) | | | | CRP | | | <0.001 | 0.031 | | | 0.185 | 0.113 | | \- 28 vs 14 | 1.26 (1.12-1.41) | 1.25 (1.03-1.52) | | | 1.06 (0.91-1.23) | 1.09 (0.84-1.43) | | | | \- 56 vs 28 | 1.13 (0.95-1.34) | 1.38 (1.11-1.71) | | | 0.89 (0.72-1.11) | 1.35 (1.01-1.81) | | | | Vir | | | <0.001 | 0.747 | | | 0.040 | 0.886 | | \- 7.5 vs 6.5 | 1.34 (1.18-1.53) | 1.33 (1.16-1.53) | | | 1.21 (1.03-1.42) | 1.25 (1.04-1.51) | | | | \- 8.5 vs 7.5 | 1.53 (1.25-1.87) | 1.48 (1.16-1.88) | | | 1.35 (1.05-1.74) | 1.43 (1.05-1.95) | | | ########################################################################################### #### Appendix 8—table 2. Best combinations of biomarkers associated with severe or moderate dengue for children. ``` r # EPV -------------------------------------------------------------- pred <- c("VCAM", "SDC", "Ang", "IL8", "IP10", "IL1RA", "CD163", "TREM", "Fer", "CRP", "Vir") # Estimate full model ---------------------------------------------- full_mod <- glm(sev.or.inte ~ VCAM + SDC + Ang + IL8 + IP10 + IL1RA + CD163 + TREM + Fer + CRP + Vir, family=binomial, data=dat1c, x=T, y=T) # Selected model --------------------------------------------------- sel_var <- matrix(0, ncol=length(pred)+1, nrow=5, dimnames=list(NULL, c(pred, "aic"))) for (i in 1:5) { if (i==1) { bs <- dredge(full_mod, rank="AIC") } else { bs <- dredge(full_mod, rank="AIC", m.lim=c(i,i)) } bs_var <- attr(get.models(bs, 1)[[1]]$terms, "term.labels") for (j in 1:(ncol(sel_var)-1)) {sel_var[i,j] <- ifelse(names(sel_var[i,j]) %in% bs_var, 1, 0)} formula <- paste("sev.or.inte~", paste(names(sel_var[i,][sel_var[i,]==1]), collapse = "+")) sel_mod <- glm(formula, data = dat1c, family = binomial, x = T, y = T) sel_var[i, ncol(sel_var)] <- AIC(sel_mod) } # Report results -------------------------------------------------- out1 <- as.data.frame(sel_var) %>% mutate(AIC = round(aic,1)) %>% select(-aic) for (i in 1:(ncol(out1)-1)) {out1[,i] <- ifelse(out1[,i]==0, NA, "+")} out2 <- as.data.frame(t(out1)) colnames(out2) <- c("Best of all combinations", "Best combination of 2 variables", "Best combination of 3 variables", "Best combination of 4 variables", "Best combination of 5 variables") rownames(out2) <- c("- VCAM-1", "- SDC-1", "- Ang-2", "- IL-8", "- IP-10", "- IL-1RA", "- sCD163", "- sTREM-1", "- Ferritin", "- CRP", "- Viremia", "AIC of the selected model") knitr::kable(out2) ``` | | Best of all combinations | Best combination of 2 variables | Best combination of 3 variables | Best combination of 4 variables | Best combination of 5 variables | |:--------------------------|:-------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------| | \- VCAM-1 | | | | | | | \- SDC-1 | \+ | | | | | | \- Ang-2 | \+ | | \+ | \+ | \+ | | \- IL-8 | \+ | | | | \+ | | \- IP-10 | \+ | | | \+ | \+ | | \- IL-1RA | \+ | \+ | \+ | \+ | \+ | | \- sCD163 | | | | | | | \- sTREM-1 | | | | | | | \- Ferritin | \+ | \+ | \+ | \+ | \+ | | \- CRP | | | | | | | \- Viremia | | | | | | | AIC of the selected model | 465.9 | 484.7 | 480.0 | 473.7 | 467.6 | ########################################################################################### #### Appendix 8—table 3. Best combinations of biomarkers associated with severe or moderate dengue for adults. ``` r # EPV -------------------------------------------------------------- pred <- c("VCAM", "SDC", "Ang", "IL8", "ns1(IP10)", "IL1RA", "CD163", "TREM", "Fer", "CRP", "Vir") # Estimate full model ---------------------------------------------- full_mod <- glm(sev.or.inte ~ VCAM + SDC + Ang + IL8 + ns1(IP10) + IL1RA + CD163 + TREM + Fer + CRP + Vir, family=binomial, data=dat1a, x=T, y=T) # Selected model --------------------------------------------------- sel_var <- matrix(0, ncol=length(pred)+1, nrow=5, dimnames=list(NULL, c(pred, "aic"))) for (i in 1:5) { if (i==1) { bs <- dredge(full_mod, rank="AIC") } else { bs <- dredge(full_mod, rank="AIC", m.lim=c(i,i)) } bs_var <- attr(get.models(bs, 1)[[1]]$terms, "term.labels") for (j in 1:(ncol(sel_var)-1)) {sel_var[i,j] <- ifelse(names(sel_var[i,j]) %in% bs_var, 1, 0)} formula <- paste("sev.or.inte~", paste(names(sel_var[i,][sel_var[i,]==1]), collapse = "+")) sel_mod <- glm(formula, data = dat1a, family = binomial, x = T, y = T) sel_var[i, ncol(sel_var)] <- AIC(sel_mod) } # Report results -------------------------------------------------- out1 <- as.data.frame(sel_var) %>% mutate(AIC = round(aic,1)) %>% select(-aic) for (i in 1:(ncol(out1)-1)) {out1[,i] <- ifelse(out1[,i]==0, NA, "+")} out2 <- as.data.frame(t(out1)) colnames(out2) <- c("Best of all combinations", "Best combination of 2 variables", "Best combination of 3 variables", "Best combination of 4 variables", "Best combination of 5 variables") rownames(out2) <- c("- VCAM-1", "- SDC-1", "- Ang-2", "- IL-8", "- IP-10", "- IL-1RA", "- sCD163", "- sTREM-1", "- Ferritin", "- CRP", "- Viremia", "AIC of the selected model") knitr::kable(out2) ``` | | Best of all combinations | Best combination of 2 variables | Best combination of 3 variables | Best combination of 4 variables | Best combination of 5 variables | |:--------------------------|:-------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------| | \- VCAM-1 | | | | | | | \- SDC-1 | \+ | \+ | \+ | \+ | \+ | | \- Ang-2 | | | | | | | \- IL-8 | \+ | \+ | \+ | \+ | \+ | | \- IP-10 | | | | | | | \- IL-1RA | | | | | | | \- sCD163 | \+ | | | | \+ | | \- sTREM-1 | | | | | | | \- Ferritin | \+ | | \+ | \+ | \+ | | \- CRP | | | | | | | \- Viremia | \+ | | | \+ | \+ | | AIC of the selected model | 426.4 | 441.1 | 434.2 | 428.5 | 426.4 | ########################################################################################### #### Appendix 9—table 1. Results of variable selection for children. ########################################################################################### #### Appendix 9—table 2. Results of variable selection for adults.