---
title: "Physical activity before stroke, small vessel disease and cognitive decline after stroke"
format: 
  revealjs:
    footer: "Andreas"
    slide-number: c/t
    show-slide-number: all
    toc: true
    toc-title: "Overview"
    logo: dsc_tekst.png
    theme: serif
    css: style.css
editor: visual
---

## Hypothesis

```{r}
library(tidyverse)
# remotes::install_github("agdamsbo/REDCapRITS")

library(gtsummary)
library(patchwork)
library(REDCapRITS)

# REDCapR::redcap_instruments(redcap_uri = "https://redcap.au.dk/api/", token = keyring::key_get("enigma_api_key"))$data

d_list <- REDCapRITS::read_redcap_tables(
  uri = "https://redcap.au.dk/api/",
  token = keyring::key_get("enigma_api_key"),
  fields = c("record_id", "incl_by" , "incl_date"),
  forms = c(
    "klassifikation_af_primre_stroke",
    "baseline_stroke",
    "registrering",
    "baseline_nihss",
    "mrs",
    "uddannelsesniveau",
    "hjde_vgt_og_blodtryk",
    "rygeanamnese",
    "rbans",
    "iqcode",
    "eos"
  )
) 
```

```{r message=FALSE}
d <- REDCapRITS::redcap_wider(d_list)
```

> Higher level of PA before acute ischemic stroke (AIS) is associated with decreased risk of cognitive decline after stroke independently of stroke severity and cSVD burden.

## Data {.smaller}

-   Physical Activity Scale in the Elderly (**PASE**) questionnaire
-   Modified Rankin Scale (**mRS**)
-   Level of education
-   Informant Questionnaire on Cognitive Decline in the Elderly, short version (**IQCODE**)
-   Repeatable Battery for the Assessment of Neuropsychological Status (**RBANS**), Scandinavian version
-   Wellbeing and depressive symptoms
-   Cerebral Small vessel disease burden (**cSVD score**)

## Education {.smaller}

```{r education}
# dput(levels(factor(d$education_inclusion_long)))

d |>
  select(kon, education_inclusion_long, ) |>
 mutate(
    education_inclusion_long = factor(
      education_inclusion_long,
      levels = c(
        "Folkeskole (9-10 år)",
        "Gymnasiale/erhvervsfaglige uddannelser (11-12 år)",
        "Erhvervsakademiuddannelser (13-14 år)",
        "Professionsbachelor/universitetsbachelor (15-17 år)",
        "Kandidatuddannelser (18-19 år)",
        "PhD ( > 20 år)"
      )
    )) |> 
    tbl_summary(
      by = "kon",
      missing = "ifany",
      missing_text = "Not classified",
      label = list(education_inclusion_long ~ "Level of education")
    ) |>
      add_overall(
      )
```

## SVD scoring

-   Number of microbleeds (SWI)
-   Superficiel siderose (SWI)
-   Lacunes (3D FLAIR)
-   White matter hyperintensities (3D FLAIR)
-   Enlarged Perivascular Space (3D FLAIR)
-   Atrophy (3D T1)

## SVD score {.scrollable}

```{r}
REDCapTidieR::read_redcap(
  redcap_uri = "https://redcap.au.dk/api/",
  token = keyring::key_get("enigma_api_key"),
  forms = c("svd_score")) |> REDCapTidieR::bind_tibbles()

svd_score |> filter(svd_initials=="AGD",redcap_event=="inclusion") |> select(svd_score) |> na.omit() |> mutate(svd_score = as.numeric(svd_score)) |> tbl_summary()
```

## IQCODE og RBANS {.smaller}

```{r}
d |> 
  select(kon,
         mrs_score_inclusion_long,
         iq_score,
         rbans_e_is_3_months_long,
         rbans_e_is_12_months_long
         # ,
         # svd_score
         ) |>
  mutate(mrs_score_inclusion_long=factor(mrs_score_inclusion_long,labels = c("0","1","2"))) |> 
  tbl_summary(
    by = "kon",
    missing = "no",
    label = list(
      mrs_score_inclusion_long~"mRS at inclusion",
      iq_score~"IQCODE score",
      rbans_e_is_3_months_long~"RBANS 3 months",
      rbans_e_is_12_months_long~"RBANS 12 months"#,
      # svd_score~"SVD score at inclusion"
    )
  ) |>
  add_overall()
```

## RBANS plot

```{r}
source("/Users/au301842/ENIGMAtrial_R/src/plot_index.R")

rbans <- d_list$rbans |> 
  dplyr::select(c("record_id",
           "redcap_event_name",
           ends_with(c("_is","_lo","_up","_per")))) |> 
  na.omit()|> 
  dplyr::mutate(redcap_event=factor(redcap_event_name, 
                                  levels = c("3_months","12_months"),
                                  labels = c("3 months","12 months"))) |> 
  dplyr::tibble()

rbans |> 
  dplyr::filter(record_id %in% record_id[duplicated(record_id)]) |> # Only patients with both 3 and 12 month
  dplyr::filter(record_id %in% sample(record_id,5)) |> # 5 random patients
  # filter(record_id %in% 28:32) |> # Only specified number
  plot_index(id="redcap_event_name",facet.by = "record_id")
```