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MOVER dataset: EPIC complications

library(tidyverse)
theme_set(theme_bw())
source("01_read_clean.R")

The post-operative complications dataset includes 36985 patients who had 64214 surgeries.

The whole dataset includes 39685 patients who had 64354 surgeries, a discrepancy of 140 surgeries. Which is small considering the overall size. I wonder if these patients could have died without a recorded complication?

Number of surgeries that resulted in any complication

complications %>% 
  count(any_complication, name = "n_patients") %>% 
  knitr::kable()
any_complication n_patients
No 61387
Yes 2829

Number of complications per surgery

complications %>% 
  count(n_complications, name = "n_patients") %>% 
  knitr::kable()
n_complications n_patients
0 61387
1 350
2 1681
3 340
4 182
5 117
6 72
7 60
8 6
9 2
10 12
11 1
12 2
13 3
17 1

Types of complications

complications_all %>% 
  filter(comp_abbr != "None") %>% 
  #filter(comp_abbr != "Post-op AN") %>% 
  ggplot(aes(y = fct_rev(fct_infreq(comp_abbr)))) +
  geom_bar() +
  facet_wrap(~if_else(comp_abbr == "Post-op AN", "Big x axis scale", "Small x axis scale"),
             scales = "free", ncol = 1)

By far the most common complication is “AN Post-op Complications”, which I’ve shortened to “Post-op AN” for plotting/brevity purposes. It seems to cover everything from “None” to “Death”:

complications_all %>% 
  filter(comp_abbr == "Post-op AN") %>% 
  count(comp_abbr, complication, sort = TRUE) %>%
  slice(1:20) %>% 
  knitr::kable()
comp_abbr complication n
Post-op AN None 77776
Post-op AN Other 872
Post-op AN Cardiovascular 615
Post-op AN Respiratory 585
Post-op AN Airway 341
Post-op AN Unknown 200
Post-op AN Neurological 134
Post-op AN Metabolic 131
Post-op AN Administrative 130
Post-op AN Injury/Infection 125
Post-op AN Medication 94
Post-op AN Death 76
Post-op AN Regional 55
Post-op AN Chronic Pain 32