daniel/VHK
1
0
Fork 0
Code Issues Pull Requests Activity

Liegedauern infizierter Katheter.

Browse Source
This commit is contained in:
daniel 2019-01-01 20:10:05 +01:00
parent 95842e5085
commit 00670d9dc1
1 changed files with 43 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

70
vhk.Rmd
View File

@ -13,13 +13,11 @@ knitr::opts_chunk$set(echo = FALSE, warning = FALSE)
library(tidyverse)
library(lubridate)
raw_data = read_csv('vhk.csv') %>%
mutate(Year = year(Date))
raw_data = read_csv('vhk.csv') %>% mutate(ImplYear = year(Date)) %>% mutate(ExplYear = year(RemovalDate))
cath_by_year = raw_data %>% count(Year)
first_year = min(raw_data$Year)
last_year = max(raw_data$Year)
max_y_break = ((max(cath_by_year$n) %/% 10) + 1) * 10
first_year = min(raw_data$ImplYear)
last_year = max(raw_data$ImplYear)
max_y_break = ((max((raw_data %>% count(ImplYear))$n) %/% 10) + 1) * 10
reference_year = year(today()) - (today() < make_date(year(today()), 1, 31))
@ -27,8 +25,9 @@ reference_year = year(today()) - (today() < make_date(year(today()), 1, 31))
## Katheterimplantationen pro Jahr
```{r cath_by_year }
cath_by_year %>%
ggplot(aes(x = Year, y = n)) +
raw_data %>%
count(ImplYear) %>%
ggplot(aes(x = ImplYear, y = n)) +
geom_col() +
scale_y_continuous(breaks = seq(from = 0, to = max_y_break, by = 10)) +
scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
@ -37,7 +36,7 @@ cath_by_year %>%
## Katheterimplantationen pro Operateur im Jahr `r reference_year`
```{r}
raw_data %>% mutate(Year = year(Date)) %>% filter(Year == reference_year) %>%
raw_data %>% filter(Year == reference_year) %>%
count(Surgeon) %>%
arrange(n) %>%
mutate(Surgeon = factor(Surgeon, levels = Surgeon)) %>%
@ -64,7 +63,7 @@ raw_data %>% mutate(Month = month(Date)) %>%
## Katheterexplantationen pro Jahr
```{r expl_by_year}
raw_data %>% mutate(ExplYear = year(RemovalDate)) %>%
raw_data %>%
# group_by(InsertionSite, Side) %>%
count(ExplYear) %>%
ggplot(aes(x = ExplYear, y = n)) +
@ -76,7 +75,7 @@ raw_data %>% mutate(ExplYear = year(RemovalDate)) %>%
## Explantationen pro Implantation pro Jahr
```{r expl_by_cath_by_year}
raw_data %>% mutate(ImplYear = year(Date), ExplYear = year(RemovalDate)) %>%
raw_data %>%
group_by(ImplYear) %>%
summarise(ExplByImpl = sum(!is.na(ExplYear)) / n()) %>%
ggplot(aes(x = ImplYear, y = ExplByImpl)) +
@ -87,10 +86,10 @@ raw_data %>% mutate(ImplYear = year(Date), ExplYear = year(RemovalDate)) %>%
## Verweildauern der Katheter
```{r durations, message=FALSE}
raw_data %>% mutate(Year = year(Date), Duration = RemovalDate - Date) %>%
group_by(Year) %>%
raw_data %>% mutate(Duration = RemovalDate - Date) %>%
group_by(ImplYear) %>%
summarize(MedianDuration = median(Duration, na.rm = TRUE)) %>%
ggplot(aes(x = Year, y = MedianDuration)) +
ggplot(aes(x = ImplYear, y = MedianDuration)) +
geom_col() +
scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
labs(x = NULL, y = "Mediane Katheter-Verweildauer [Tage]")
@ -100,7 +99,7 @@ raw_data %>% mutate(Year = year(Date), Duration = RemovalDate - Date) %>%
### Variante A: Absolute Zahlen
```{r removal_reasons, message=FALSE}
raw_data %>% filter(!is.na(RemovalDate), !is.na(RemovalReason)) %>%
mutate(ExplYear = year(RemovalDate) %% 100) %>%
mutate(ExplYear = ExplYear %% 100) %>%
group_by(ExplYear) %>%
count(RemovalReason) %>%
ggplot(aes(x = ExplYear, y = n)) +
@ -118,8 +117,8 @@ raw_data %>% filter(!is.na(RemovalDate), !is.na(RemovalReason)) %>%
impl_per_year = raw_data %>% mutate(ImplYear = year(Date)) %>% count(ImplYear)
raw_data %>%
select(Date, RemovalDate, RemovalReason) %>%
mutate(ImplYear = year(Date) %% 100, ExplYear = year(RemovalDate)) %>%
select(ImplYear, ExplYear, RemovalDate, RemovalReason) %>%
mutate(ImplYear = ImplYear %% 100) %>%
left_join(impl_per_year, by = c("ExplYear" = "ImplYear")) %>% # creates column "n"
filter(!is.na(RemovalDate), !is.na(RemovalReason)) %>%
group_by(ExplYear) %>%
@ -138,6 +137,22 @@ raw_data %>%
labs(x = NULL, y = "Anzahl entfernter Katheter / gelegter Katheter")
```
## Wann treten Infektionen auf?
```{r infections, message=FALSE}
raw_data %>% filter(!is.na(RemovalDate), RemovalReason == "Infektion") %>%
mutate(Duration = RemovalDate - Date, Month = as.integer(Duration) %/% 30) %>%
ggplot(aes(x = Month)) +
geom_bar(width = 0.9) +
# raw_data %>% filter(!is.na(RemovalDate), RemovalReason == "Infektion") %>%
coord_cartesian(xlim = c(0, 56)) +
scale_x_continuous(breaks = seq(from = 0, to = 56, by = 4)) +
scale_y_continuous(breaks = seq(from = 0, to = 10, by = 1)) +
facet_grid(rows = vars(ImplYear)) +
labs(x = "Woche nach Implantation", y = "Anzahl wg. Infektion entfernter Katheter",
title = "Liegedauer infizierter Katheter nach Implantationsjahr")
```
<!--
## Explantationsgründe je Implanteur
```{r removal_reasons_by_surgeon, message=FALSE}
@ -157,7 +172,7 @@ raw_data %>% filter(!is.na(RemovalDate)) %>%
## Alter der Patienten bei Implantation
```{r patient_age}
raw_data %>%
ggplot(aes(group = Year, x = Year, y = Age)) +
ggplot(aes(group = ImplYear, x = ImplYear, y = Age)) +
geom_boxplot() +
coord_cartesian(ylim = c(20, 100)) +
scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
@ -167,8 +182,9 @@ raw_data %>%
## Geschlecht der Patienten bei Implantation
```{r patient_sex}
raw_data %>% group_by(Year) %>% summarise(PercentFemale = sum(Sex == "weiblich") / n()) %>%
ggplot(aes(x = Year, y = PercentFemale)) +
raw_data %>% group_by(ImplYear) %>%
summarise(PercentFemale = sum(Sex == "weiblich") / n()) %>%
ggplot(aes(x = ImplYear, y = PercentFemale)) +
geom_col() +
scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
coord_cartesian(ylim = c(0, 1)) +
@ -181,7 +197,7 @@ Ist da ein Trend hin zu immer mehr Kathetern von links?!
```{r insertion_site}
raw_data %>% mutate(Side = factor(Side, levels = c("rechts", "links"))) %>%
ggplot(aes(x = Year)) +
ggplot(aes(x = ImplYear)) +
facet_grid(InsertionSite ~ Side) +
geom_bar() +
labs(x = NULL, y = "Anzahl Katheter")
@ -191,12 +207,12 @@ raw_data %>% mutate(Side = factor(Side, levels = c("rechts", "links"))) %>%
Um 2014 herum haben einige die Facharztprüfung abgelegt, ist das der Grund für die Auffälligkeit 2015/2016?
```{r percent_residents}
raw_data %>% group_by(Year) %>%
raw_data %>% group_by(ImplYear) %>%
summarize(Assistenzarzt = sum(SurgeonRole == "Assistenzarzt") / n(),
Facharzt = sum(SurgeonRole == "Facharzt") / n(),
Oberarzt = sum(SurgeonRole == "Oberarzt") / n()) %>%
gather(key = Role, value = Percent, Assistenzarzt, Facharzt, Oberarzt) %>%
ggplot(aes(x = Year, y = Percent)) +
ggplot(aes(x = ImplYear, y = Percent)) +
scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
facet_grid(Role ~ .) +
@ -225,13 +241,13 @@ Nur Operateure der letzten 4 Jahre
to_year = year(today()) %% 100
from_year = to_year - 3
raw_data %>%
mutate(Year = Year %% 100) %>%
filter(Year >= from_year, !is.na(InsertionFluoroscopyDuration)) %>%
group_by(Surgeon, Year) %>%
mutate(ImplYear = ImplYear %% 100) %>%
filter(ImplYear >= from_year, !is.na(InsertionFluoroscopyDuration)) %>%
group_by(Surgeon, ImplYear) %>%
summarize(FluoroscopyIndex = median(InsertionFluoroscopyDuration, na.rm = TRUE)) %>%
ungroup() %>%
# mutate(Surgeon = factor(Surgeon, levels = Surgeon)) %>%
ggplot(aes(x = Year, y = FluoroscopyIndex)) +
ggplot(aes(x = ImplYear, y = FluoroscopyIndex)) +
geom_point() +
geom_line() +
scale_x_continuous(breaks = seq(from = from_year, to = to_year, by = 1 )) +