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