226 lines
7.6 KiB
Plaintext
226 lines
7.6 KiB
Plaintext
---
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title: "Vorhofkatheter-Statistik"
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author: "Daniel Kraus"
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date: '2018-12-29'
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output:
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slidy_presentation: default
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ioslides_presentation: default
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beamer_presentation: default
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = FALSE, warning = FALSE)
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library(tidyverse)
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raw_data = read_csv('vhk.csv') %>%
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mutate(Year = lubridate::year(Date))
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cath_by_year = raw_data %>% count(Year)
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first_year = min(raw_data$Year)
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last_year = max(raw_data$Year)
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max_y_break = ((max(cath_by_year$n) %/% 10) + 1) * 10
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```
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## Katheterimplantationen pro Jahr
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```{r cath_by_year }
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cath_by_year %>%
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ggplot(aes(x = Year, y = n)) +
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geom_col() +
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scale_y_continuous(breaks = seq(from = 0, to = max_y_break, by = 10)) +
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scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
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labs(x = NULL, y = "Anzahl Katheter")
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```
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<!--
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## Katheterimplantationen im Jahresverlauf
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```{r cath_by_month}
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raw_data %>% mutate(Month = lubridate::month(Date)) %>%
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group_by(Year) %>%
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count(Month) %>%
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ggplot(aes(x = Month, y = n, group = Year, alpha = Year)) +
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geom_point() +
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geom_line()
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```
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-->
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## Katheterexplantationen pro Jahr
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```{r expl_by_year}
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raw_data %>% mutate(ExplYear = lubridate::year(RemovalDate)) %>%
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# group_by(InsertionSite, Side) %>%
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count(ExplYear) %>%
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ggplot(aes(x = ExplYear, y = n)) +
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geom_col() +
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# facet_grid(rows = vars(InsertionSite), cols = vars(Side)) +
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scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
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labs(x = NULL, y = "Explantationen")
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```
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## Explantationen pro Implantation pro Jahr
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```{r expl_by_cath_by_year}
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raw_data %>% mutate(ImplYear = lubridate::year(Date), ExplYear = lubridate::year(RemovalDate)) %>%
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group_by(ImplYear) %>%
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summarise(ExplByImpl = sum(!is.na(ExplYear)) / n()) %>%
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ggplot(aes(x = ImplYear, y = ExplByImpl)) +
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geom_col() +
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scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
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labs(x = NULL, y = "Explantationen pro Implantation")
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```
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## Verweildauern der Katheter
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```{r durations, message=FALSE}
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raw_data %>% mutate(Year = lubridate::year(Date), Duration = RemovalDate - Date) %>%
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group_by(Year) %>%
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summarize(MedianDuration = median(Duration, na.rm = TRUE)) %>%
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ggplot(aes(x = Year, y = MedianDuration)) +
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geom_col() +
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scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
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labs(x = NULL, y = "Mediane Katheter-Verweildauer [Tage]")
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```
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## Gründe der Katheterexplantation
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### Variante A: Absolute Zahlen
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```{r removal_reasons, message=FALSE}
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raw_data %>% filter(!is.na(RemovalDate), !is.na(RemovalReason)) %>%
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mutate(ExplYear = lubridate::year(RemovalDate) %% 100) %>%
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group_by(ExplYear) %>%
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count(RemovalReason) %>%
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ggplot(aes(x = ExplYear, y = n)) +
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geom_point() + geom_line() +
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scale_x_continuous(breaks = scales::pretty_breaks()) +
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scale_y_continuous(breaks = scales::pretty_breaks()) +
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facet_wrap(vars(RemovalReason)) +
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labs(x = NULL, y = "Anzahl entfernter Katheter")
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```
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### Variante B: auf die Zahl der in dem Jahr gelegten Katheter bezogen
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```{r removal_reasons_normalized, message=FALSE}
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# Zur Berechnung dieses Index muß man zunächst für jeden explantierten Katheter
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# berechnen, wie viele Katheter im *ex*plantationsjahr *im*plantiert wurden.
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impl_per_year = raw_data %>% mutate(ImplYear = lubridate::year(Date)) %>% count(ImplYear)
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raw_data %>%
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select(Date, RemovalDate, RemovalReason) %>%
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mutate(ImplYear = lubridate::year(Date) %% 100, ExplYear = lubridate::year(RemovalDate)) %>%
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left_join(impl_per_year, by = c("ExplYear" = "ImplYear")) %>% # creates column "n"
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filter(!is.na(RemovalDate), !is.na(RemovalReason)) %>%
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group_by(ExplYear) %>%
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add_count(RemovalReason) %>% # creates column "nn"
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ungroup() %>%
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select(ExplYear, RemovalReason, n, nn) %>%
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mutate(i = nn/n) %>%
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group_by(ExplYear, RemovalReason) %>%
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# summarize(i = sum(i)) %>%
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distinct() %>%
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ggplot(aes(x = ExplYear, y = i)) +
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geom_point() + geom_line() +
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scale_x_continuous(breaks = scales::pretty_breaks()) +
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scale_y_continuous(breaks = scales::pretty_breaks()) +
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facet_wrap(vars(RemovalReason)) +
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labs(x = NULL, y = "Anzahl entfernter Katheter / gelegter Katheter")
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```
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## Alter der Patienten
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```{r patient_age}
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raw_data %>%
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ggplot(aes(group = Year, x = Year, y = Age)) +
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geom_boxplot() +
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coord_cartesian(ylim = c(20, 100)) +
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scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
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scale_y_continuous(breaks = seq(from = 20, to = 100, by = 10)) +
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labs(x = NULL, y = "Jahre")
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```
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## Geschlecht der Patienten
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```{r patient_sex}
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raw_data %>% group_by(Year) %>% summarise(PercentFemale = sum(Sex == "weiblich") / n()) %>%
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ggplot(aes(x = Year, y = PercentFemale)) +
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geom_col() +
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scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
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coord_cartesian(ylim = c(0, 1)) +
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scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
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labs(x = NULL, y = "Anteil Frauen")
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```
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## Katheterlokalisation
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Ist da ein Trend hin zu immer mehr Kathetern von links?!
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```{r insertion_site}
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raw_data %>% mutate(Side = factor(Side, levels = c("rechts", "links"))) %>%
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ggplot(aes(x = Year)) +
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facet_grid(InsertionSite ~ Side) +
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geom_bar() +
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labs(x = NULL, y = "Anzahl Katheter")
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```
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## Anteil der Arztrollen
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Um 2014 herum haben einige die Facharztprüfung abgelegt, ist das der Grund für die Auffälligkeit 2015/2016?
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```{r percent_residents}
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raw_data %>% group_by(Year) %>%
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summarize(Assistenzarzt = sum(SurgeonRole == "Assistenzarzt") / n(),
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Facharzt = sum(SurgeonRole == "Facharzt") / n(),
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Oberarzt = sum(SurgeonRole == "Oberarzt") / n()) %>%
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gather(key = Role, value = Percent, Assistenzarzt, Facharzt, Oberarzt) %>%
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ggplot(aes(x = Year, y = Percent)) +
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scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
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scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
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facet_grid(Role ~ .) +
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geom_col() +
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labs(x = NULL, y = "Anteil in den gelegten Kathetern")
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```
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## Hitparade der Durchleuchtungsdauern
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```{r greatest_fluoroscopy}
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raw_data %>%
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group_by(Surgeon) %>%
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summarize(FluoroscopyIndex = median(InsertionFluoroscopyDuration, na.rm = TRUE)) %>%
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arrange(desc(FluoroscopyIndex)) %>%
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top_n(-10, FluoroscopyIndex) %>%
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mutate(Surgeon = factor(Surgeon, levels = Surgeon)) %>%
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ggplot(aes(x = Surgeon, y = FluoroscopyIndex)) +
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geom_col() +
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coord_flip() +
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labs(x = NULL, y = "Median der Durchleuchtungsdauer [s]")
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```
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## Individuelle Durchleuchtungsdauern
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Nur Operateure der letzten 4 Jahre
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```{r individual_fluoroscopy, message=FALSE}
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raw_data %>% filter(Year > lubridate::year(lubridate::today()) - 4, !is.na(InsertionFluoroscopyDuration)) %>%
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mutate(Year = Year %% 100) %>%
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group_by(Surgeon, Year) %>%
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summarize(FluoroscopyIndex = median(InsertionFluoroscopyDuration, na.rm = TRUE)) %>%
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ungroup() %>%
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# mutate(Surgeon = factor(Surgeon, levels = Surgeon)) %>%
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ggplot(aes(x = Year, y = FluoroscopyIndex)) +
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geom_point() +
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geom_line() +
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facet_wrap(vars(Surgeon)) +
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labs(x = NULL, y = "Median der Durchleuchtungsdauer [s]")
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```
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## Hitparade der Implanteure
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```{r greatest_surgeons}
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raw_data %>% count(Surgeon) %>% arrange(n) %>% top_n(10, n) %>% mutate(Surgeon = factor(Surgeon, levels = Surgeon)) %>%
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ggplot(aes(x = Surgeon, y = n)) +
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geom_col() +
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coord_flip() +
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labs(x = NULL, y = "Gesamtzahl Katheter")
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```
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## Hitparade der Assistenten
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Einsame Spitze... Romana Ziegler!
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```{r greatest_assistants}
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raw_data %>% count(Assistant) %>% arrange(n) %>% top_n(10, n) %>% mutate(Assistant = factor(Assistant, levels = Assistant)) %>%
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ggplot(aes(x = Assistant, y = n)) +
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geom_col() +
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coord_flip() +
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labs(x = NULL, y = "Gesamtzahl Katheter")
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```
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