R Notebook

Clean table 3

library(tidyverse)
library(readxl)
library(broom)
library(gt)

PSFI_df_malnutrition <- read_xlsx("PSFI_final_malnutrition3.xlsx")

# CREATE NEW AGE GROUP CATEGORY
PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    age_group1 = case_when(
      age_months < 12 ~ 0L,
      age_months >= 12 & age_months < 24 ~ 1L,
      age_years >=2 & age_years <5 ~ 2L,
      TRUE ~ NA_integer_
    )
  )

PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    popc_change = popc_dc - popc_admit,

    popc_dc1 = case_when(
      popc_dc >= 3 & popc_change >= 1 ~ 1L,
      !is.na(popc_dc) & !is.na(popc_admit) ~ 0L,
      TRUE ~ NA_integer_
    ),

    popc_dc1 = factor(
      popc_dc1,
      levels = c(0, 1),
      labels = c(
        "No new moderate disability",
        "New moderate disability"
      )
    )
  )

analysis_df <- PSFI_df_malnutrition %>%
  filter(
    case_control == 1,
    age_years < 5
  )

# SET NUTRITIONAL STATUS REFERENCE CATEGORY
analysis_df <- analysis_df %>%
  mutate(
    malnutrition_who5 = relevel(
      factor(malnutrition_who5),
      ref = "No malnutrition"
    )
  )

analysis_df <- analysis_df %>%
  mutate(
    picu_admit1 = factor(
      picu_admit,
      levels = c("No","Yes"),
      labels = c("No PICU admission","PICU admission")
    )
  )

sapply(
  analysis_df[, c("malnutrition_who5", "age_group1", "sex1", "hiv_pos1", "comorbidity", "picu_admit1")],
  class
)

malnutrition_who5        age_group1              sex1          hiv_pos1       comorbidity       picu_admit1 
         "factor"         "integer"       "character"       "character"         "numeric"          "factor" 

analysis_df <- analysis_df %>%
  mutate(
    comorbidity = factor(
      comorbidity,
      levels = c(0, 1),
      labels = c("No", "Yes")
    )
  )

mort_crude <- glm(
  mort_inhosp ~ malnutrition_who5,
  family = binomial,
  data = analysis_df
)

mort_adj <- glm(
  mort_inhosp ~ malnutrition_who5 +
    age_group1 + sex1 + hiv_pos1 + comorbidity,
  family = binomial,
  data = analysis_df
)

popc_crude <- glm(
  popc_dc1 ~ malnutrition_who5,
  family = binomial,
  data = analysis_df
)

popc_adj <- glm(
  popc_dc1 ~ malnutrition_who5 +
    age_group1 + sex1 + hiv_pos1 +
  comorbidity,
  family = binomial,
  data = analysis_df
)

picu_crude <- glm(
  picu_admit1 ~ malnutrition_who5,
  family = binomial,
  data = analysis_df
)

picu_adj <- glm(
  picu_admit1 ~ malnutrition_who5 +
    age_group1 + sex1 + hiv_pos1 +
   comorbidity,
  family = binomial,
  data = analysis_df
)

analysis_df_survivors <- analysis_df %>%
  filter(mort_inhosp == 0, case_control == 1, age_years <5) 

los_crude <- lm(
  los ~ malnutrition_who5,
  data = analysis_df_survivors
)

los_adj <- lm(
  los ~ malnutrition_who5 +
    age_group1 + sex1 + hiv_pos1  + comorbidity,
  data = analysis_df_survivors
)

library(broom)
library(dplyr)
library(tidyr)
make_table <- function(crude, adjusted){

  c1 <- broom::tidy(
    crude,
    exponentiate = TRUE,
    conf.int = TRUE
  ) %>%
    filter(grepl("^malnutrition_who5", term)) %>%
    transmute(
      Category = sub("^malnutrition_who5", "", term),
      OR = sprintf("%.2f (%.2f–%.2f)",
                   estimate,
                   conf.low,
                   conf.high),
      P = ifelse(
        p.value < 0.001,
        "<0.001",
        sprintf("%.3f", p.value)
      )
    )

  c2 <- broom::tidy(
    adjusted,
    exponentiate = TRUE,
    conf.int = TRUE
  ) %>%
    filter(grepl("^malnutrition_who5", term)) %>%
    transmute(
      Category = sub("^malnutrition_who5", "", term),
      aOR = sprintf("%.2f (%.2f–%.2f)",
                    estimate,
                    conf.low,
                    conf.high),
      aP = ifelse(
        p.value < 0.001,
        "<0.001",
        sprintf("%.3f", p.value)
      )
    )

  full_join(c1, c2, by = "Category")
}

make_table_lm <- function(crude, adjusted){

  c1 <- broom::tidy(crude, conf.int = TRUE) %>%
    filter(grepl("^malnutrition_who5", term)) %>%
    transmute(
      Category = sub("^malnutrition_who5", "", term),
      LOS_Beta = sprintf("%.2f (%.2f–%.2f)",
                         estimate,
                         conf.low,
                         conf.high),
      LOS_P = ifelse(
        p.value < 0.001,
        "<0.001",
        sprintf("%.3f", p.value)
      )
    )

  c2 <- broom::tidy(adjusted, conf.int = TRUE) %>%
    filter(grepl("^malnutrition_who5", term)) %>%
    transmute(
      Category = sub("^malnutrition_who5", "", term),
      LOS_aBeta = sprintf("%.2f (%.2f–%.2f)",
                          estimate,
                          conf.low,
                          conf.high),
      LOS_aP = ifelse(
        p.value < 0.001,
        "<0.001",
        sprintf("%.3f", p.value)
      )
    )

  full_join(c1, c2, by = "Category")
}

mort_tbl <- make_table(mort_crude, mort_adj)
popc_tbl <- make_table(popc_crude, popc_adj)
picu_tbl <- make_table(picu_crude, picu_adj)
los_tbl  <- make_table_lm(los_crude, los_adj)

names(mort_tbl) <- c(
  "Category",
  "Mortality_OR","Mortality_P",
  "Mortality_aOR","Mortality_aP"
)

names(popc_tbl) <- c(
  "Category",
  "POPC_OR","POPC_P",
  "POPC_aOR","POPC_aP"
)

names(picu_tbl) <- c(
  "Category",
  "PICU_OR","PICU_P",
  "PICU_aOR","PICU_aP"
)

names(los_tbl) <- c(
  "Category",
  "LOS_Beta","LOS_P",
  "LOS_aBeta","LOS_aP"
)

table3 <-
  mort_tbl %>%
  left_join(popc_tbl, by = "Category") %>%
  left_join(picu_tbl, by = "Category") %>%
  left_join(los_tbl, by = "Category")

table3 <- bind_rows(
  tibble(
    Category = "No malnutrition",
    Mortality_OR = "Reference",
    Mortality_aOR = "Reference",
    POPC_OR = "Reference",
    POPC_aOR = "Reference",
    PICU_OR = "Reference",
    PICU_aOR = "Reference",
    LOS_Beta = "Reference",
    LOS_aBeta = "Reference"
  ),
  table3
)

library(gt)

table3 %>%
  gt() %>%
  tab_header(
    title = md("**Association between nutritional status and clinical outcomes**")
  ) %>%
  tab_spanner(
    label = "In-hospital mortality",
    columns = c(Mortality_OR, Mortality_P,
                Mortality_aOR, Mortality_aP)
  ) %>%
  tab_spanner(
    label = "New moderate disability",
    columns = c(POPC_OR, POPC_P,
                POPC_aOR, POPC_aP)
  ) %>%
  tab_spanner(
    label = "PICU admission",
    columns = c(PICU_OR, PICU_P,
                PICU_aOR, PICU_aP)
  ) %>%

    tab_spanner(
    label = "Length of stay (survivors only)",
    columns = c(LOS_Beta, LOS_P,
                LOS_aBeta, LOS_aP)
  )%>%

  cols_label(
    Category = "Nutritional status",

    Mortality_OR = "OR (95% CI)",
    Mortality_P = "P",
    Mortality_aOR = "aOR (95% CI)",
    Mortality_aP = "P",

    POPC_OR = "OR (95% CI)",
    POPC_P = "P",
    POPC_aOR = "aOR (95% CI)",
    POPC_aP = "P",

    PICU_OR = "OR (95% CI)",
    PICU_P = "P",
    PICU_aOR = "aOR (95% CI)",
    PICU_aP = "P",
    
    LOS_Beta = "β (95% CI)",
LOS_P = "P",
LOS_aBeta = "Adjusted β (95% CI)",
LOS_aP = "P"
  ) %>%
  tab_source_note(
    md("*Adjusted for age group, sex, HIV status, and comorbidity status.*")
  )

Association between nutritional status and clinical outcomes
Nutritional status	In-hospital mortality				New moderate disability				PICU admission				Length of stay (survivors only)
	OR (95% CI)	P	aOR (95% CI)	P	OR (95% CI)	P	aOR (95% CI)	P	OR (95% CI)	P	aOR (95% CI)	P	β (95% CI)	P	Adjusted β (95% CI)	P
No malnutrition	Reference	NA	Reference	NA	Reference	NA	Reference	NA	Reference	NA	Reference	NA	Reference	NA	Reference	NA
Moderate malnutrition	2.12 (1.25–3.54)	0.005	2.13 (1.24–3.63)	0.006	2.75 (1.72–4.38)	<0.001	2.79 (1.73–4.50)	<0.001	1.12 (0.67–1.85)	0.650	1.16 (0.68–1.93)	0.570	1.64 (-1.42–4.70)	0.292	1.77 (-1.31–4.85)	0.260
Obesity	0.88 (0.25–2.38)	0.823	0.85 (0.24–2.33)	0.776	1.13 (0.44–2.63)	0.782	1.12 (0.43–2.64)	0.797	1.24 (0.50–2.79)	0.625	1.19 (0.48–2.72)	0.685	-0.32 (-5.21–4.57)	0.898	-0.42 (-5.32–4.48)	0.867
Overweight	0.47 (0.11–1.37)	0.226	0.42 (0.10–1.23)	0.164	0.31 (0.07–0.88)	0.054	0.28 (0.07–0.80)	0.038	0.61 (0.22–1.41)	0.282	0.56 (0.20–1.30)	0.210	-5.02 (-9.21–-0.83)	0.019	-5.25 (-9.46–-1.04)	0.015
Severe malnutrition	3.29 (1.97–5.48)	<0.001	3.40 (2.00–5.74)	<0.001	2.92 (1.80–4.73)	<0.001	2.97 (1.82–4.84)	<0.001	0.97 (0.55–1.65)	0.922	0.97 (0.55–1.66)	0.923	1.46 (-1.93–4.84)	0.399	1.49 (-1.90–4.88)	0.389
Adjusted for age group, sex, HIV status, and comorbidity status.