Last updated: 2022-03-16

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Knit directory: codemapper/

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Unstaged changes:
    Modified:   R/clinical_codes.R
    Modified:   R/lookups_and_mappings.R
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    Modified:   analysis/read3_icd10_mapping.Rmd

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File Version Author Date Message
Rmd b425304 rmgpanw 2022-03-15 add code and tests to reformat read2 to icd10 mapping table
Rmd a81c1e7 rmgpanw 2022-03-14 add notes; add filter_cols function plus tests (all passing)
html e5c5381 rmgpanw 2022-03-10 update mainly icd10-related codes.
Rmd ae02335 Chuin Ying Ung 2022-02-22 update notes for read3_icd10
Rmd dfbc621 Chuin Ying Ung 2022-02-22 add mapping notes 
library(tidyverse)
library(reactable)
library(readxl)
library(crosstalk)
library(targets)
library(codemapper)
library(flextable)

all_lkps_maps <- tar_read(all_lkps_maps_raw) %>% 
  purrr::map(codemapper:::rm_footer_rows_all_lkps_maps_df) %>%
  purrr::map(~ tibble::rowid_to_column(.data = .x,
                                       var = ".rowid"))

read_ctv3_icd10 <- all_lkps_maps$read_ctv3_icd10
icd10_lkp <- all_lkps_maps$icd10_lkp
# utility functions
append_read_icd10_descriptions <- function(df,
                                            read_type = "read3") {
  match.arg(read_type,
            c("read2", "read3"))
  
  # get read and icd10 descriptions
  read_df <- df$read_code %>%
    lookup_codes(code_type = read_type, 
                 unrecognised_codes = "warning") %>%
    select(read_code = code,
           description_read3 = description)
  
  icd10_df <- df$icd10_code %>%
    lookup_codes(code_type = "icd10",
                 unrecognised_codes = "warning") %>%
    select(icd10_code = code,
           description_icd10 = description)
  
  # append descriptions
  list(df,
       read_df,
       icd10_df) %>%
    reduce(full_join) %>%
    select(contains("read"),
           contains("icd10"),
           everything())
}

Key points

Unrecognised ICD10 codes

[1] TRUE

All Read 3 codes in the read_ctv3_icd10 mapping table are present in the read_ctv3_lkp lookup table.

# identify unrecognised ICD10 codes
unrecognised_icd10_codes <- subset(read_ctv3_icd10$icd10_code,
       !read_ctv3_icd10$icd10_code %in% icd10_lkp$ALT_CODE) %>% 
  unique()

There are 919 ICD10 codes in the read_ctv3_icd10 mapping table that are not present in the icd10_lkp table (ALT_CODE format).

[1] TRUE

These are all either ‘asterisk’ or ‘dagger’ codes, which have been appended with ‘A’ or ‘D’ respectively.

[1] TRUE

After removing ‘A’ or ‘D’ from the ICD10 codes in mapping table read_ctv3_icd10, all ICD10 codes are also present in lookup table icd10_lkp.

Mapping status and refine flag

The Read 3 to ICD10 mapping table has a number of columns with additional information:

read_ctv3_icd10 %>% 
  head() %>% 
  select(-.rowid) %>% 
  knitr::kable(caption = "First few rows of the Read 3 to ICD10 mapping table")
First few rows of the Read 3 to ICD10 mapping table
read_code icd10_code mapping_status refine_flag add_code_flag element_num block_num
123.. Z831 D C M 0 0
123.. Z830 A C M 0 0
1231. Z831 G C M 0 0
1232. Z831 G C M 0 0
1233. Z831 G C M 0 0
1244. Z848 G C M 0 0

These are described by UK Biobank resource 592. The following tables show how refine_flag status varies by mapping_status. Mappings labelled with mapping_status ‘E’, ‘D’ and ‘G’ have the lowest proportion labelled with refine_flag ‘M’ (i.e. it is mandatory to check mappings labelled with refine_flag ‘M’ against the default)

Counts:

refine_flag_by_mapping_status <- read_ctv3_icd10 %>% 
  distinct(mapping_status) %>% 
  filter(!is.na(mapping_status)) %>% 
  pull(mapping_status) %>% 
  set_names() %>% 
  map(~ {
    read_ctv3_icd10 %>% 
        filter(mapping_status == .x) %>% 
        group_by(refine_flag) %>% 
      summarise(n_unique_read3 = length(unique(read_code)))
    }) %>% 
  bind_rows(.id = "mapping_status") %>% 
  mutate(refine_flag = paste0("refine_flag_", refine_flag)) %>% 
  pivot_wider(names_from = refine_flag,
              values_from = n_unique_read3) %>% 
  mutate(Total_unique_read3 = rowSums(across(refine_flag_C:refine_flag_P)))

knitr::kable(refine_flag_by_mapping_status)
mapping_status refine_flag_C refine_flag_M refine_flag_P Total_unique_read3
D 27626 708 1950 30284
A 2891 8671 103 11665
G 31066 1916 1192 34174
R 5859 3598 268 9725
E 3660 41 3157 6858

Percentages:

refine_flag_by_mapping_status_pct <- refine_flag_by_mapping_status %>% 
  mutate(across(refine_flag_C:refine_flag_P,
                ~ round(.x / Total_unique_read3 * 100, digits = 3)))

knitr::kable(refine_flag_by_mapping_status_pct)
mapping_status refine_flag_C refine_flag_M refine_flag_P Total_unique_read3
D 91.223 2.338 6.439 30284
A 24.784 74.333 0.883 11665
G 90.905 5.607 3.488 34174
R 60.247 36.997 2.756 9725
E 53.368 0.598 46.034 6858

Mappings are categorised under the following refine_flag labels:

refine_flag Description
C Completely refined
M Mandatory to refine further
P Possible but not mandatory to refine further
The refine_flag denotes whether or not the target code is sufficiently detailed to be acceptable. 3-character ICD codes are usually not acceptable, for example. Covers addition of 4th and 5th digit extensions in ICD, 4th character in OPCS-4.

+=====================================================================================================================================================================================================================================================+ +—————————————————————————————————————————————————————————————————————————————————–+

# get all possible combinations of `mapping_status`, `refine_flag` and
# `add_code_flag`
mapping_metadata_vars <- c("mapping_status",
  "refine_flag",
  "add_code_flag")

mapping_metadata_vars_combinations <- mapping_metadata_vars %>% 
  set_names() %>% 
  
  # get unique values for each variable
  map(~ read_ctv3_icd10 %>% 
        filter(!is.na(.data[[.x]])) %>% 
        pull(.data[[.x]]) %>% 
               unique()) %>% 
  
  # generate all possible combinations
  crossing(!!!.) %>% 
  
  # perform filtering join for only combinations that are actually present in
  # mapping table
  semi_join(read_ctv3_icd10,
            by = mapping_metadata_vars)

# convert this to a named list (where names describe the combination)
mapping_metadata_vars_combinations_list <- mapping_metadata_vars_combinations %>% 
  unite(col = "combination_label",
        everything(),
        remove = FALSE) %>% 
  mutate(combination_label = paste0("mapping_status/refine_flag/add_code_flag: ",
                                    combination_label))

mapping_metadata_vars_combinations_list <-
  split(
    mapping_metadata_vars_combinations_list,
    mapping_metadata_vars_combinations_list$combination_label
  ) %>%
  map(~ select(.x, -combination_label))

Here are some example codes for each refine flag

# df of example mappings
mapping_metadata_vars_combinations_list_example_codes <-
  mapping_metadata_vars_combinations_list %>%
  map( ~ right_join(.x,
                    read_ctv3_icd10,
                    by = mapping_metadata_vars) %>%
         head(1)) %>%
  bind_rows() %>%
  pull(read_code)

mapping_metadata_vars_combinations_list_examples <-
  read_ctv3_icd10 %>%
  filter(read_code %in% mapping_metadata_vars_combinations_list_example_codes) %>%
  codemapper:::reformat_read_ctv3_icd10() %>%
  select(-.rowid) %>%
  append_read_icd10_descriptions() %>%
  bind_rows()

# crosstalk reactable table
mapping_metadata_vars_combinations_list_examples_crosstalk <-
  SharedData$new(mapping_metadata_vars_combinations_list_examples)

bscols(
  widths = c(3, 9),
  list(
    crosstalk::filter_select(
      "read_code",
      "Read code",
      mapping_metadata_vars_combinations_list_examples_crosstalk,
      ~ read_code
    ),
    crosstalk::filter_checkbox(
      "mapping_status",
      "Mapping status",
      mapping_metadata_vars_combinations_list_examples_crosstalk,
      ~ mapping_status
    ),
    crosstalk::filter_checkbox(
      "refine_flag",
      "Refine flag",
      mapping_metadata_vars_combinations_list_examples_crosstalk,
      ~ refine_flag
    ),
    crosstalk::filter_checkbox(
      "add_code_flag",
      "Add code flag",
      mapping_metadata_vars_combinations_list_examples_crosstalk,
      ~ add_code_flag
    )
  ),
  reactable(
    mapping_metadata_vars_combinations_list_examples_crosstalk,
    filterable = TRUE,
    searchable = TRUE,
    showPageSizeOptions = TRUE,
    pageSizeOptions = c(5, 25, 50, 100),
    defaultPageSize = 5,
    resizable = TRUE,
    paginationType = 'jump'
  )
)

Examples of non-specific Read 3 to sex-specific ICD10 code mappings

The Read 3 code XaIP9 maps ‘sebaceous cyst’ to various ICD10 codes, including some which are sex-specific. A similar issue arises for Read 3 M262.

# df of example mappings
non_specific_read3_to_sex_specific_icd10_examples <-
  read_ctv3_icd10 %>%
  filter(read_code %in% c('XaIP9', 'M262.')) %>%
  codemapper:::reformat_read_ctv3_icd10() %>%
  select(-.rowid) %>%
  append_read_icd10_descriptions()

# crosstalk reactable table
non_specific_read3_to_sex_specific_icd10_examples_crosstalk <-
  SharedData$new(non_specific_read3_to_sex_specific_icd10_examples)

bscols(
  widths = c(3, 9),
  list(
    crosstalk::filter_checkbox(
      "read_code",
      "Read code",
      non_specific_read3_to_sex_specific_icd10_examples_crosstalk,
      ~ read_code
    ),
    crosstalk::filter_checkbox(
      "mapping_status",
      "Mapping status",
      non_specific_read3_to_sex_specific_icd10_examples_crosstalk,
      ~ mapping_status
    ),
    crosstalk::filter_checkbox(
      "refine_flag",
      "Refine flag",
      non_specific_read3_to_sex_specific_icd10_examples_crosstalk,
      ~ refine_flag
    ),
    crosstalk::filter_checkbox(
      "add_code_flag",
      "Add code flag",
      non_specific_read3_to_sex_specific_icd10_examples_crosstalk,
      ~ add_code_flag
    )
  ),
  reactable(
    non_specific_read3_to_sex_specific_icd10_examples_crosstalk,
    filterable = TRUE,
    searchable = TRUE,
    showPageSizeOptions = TRUE,
    pageSizeOptions = c(5, 25, 50, 100),
    defaultPageSize = 5,
    resizable = TRUE,
    paginationType = 'jump'
  )
)

Example: type 2 diabetes

The description for Read 3 code ‘C10..’ is ‘Diabetes mellitus’. This is an example of an unspecific Read 3 code that can potentially map to multiple specific ICD10 codes. Note that the default mapping (flagged as ‘D’ under mapping_status) is the most appropriate.

read_ctv3_icd10 %>%
  filter(read_code == 'C10..') %>%
  codemapper:::reformat_read_ctv3_icd10() %>%
  select(-.rowid) %>%
  append_read_icd10_descriptions() %>% 
  select(read_code:add_code_flag,
         -icd10_dagger_asterisk) %>% 
  flextable()

Compare also with the Read 2 to ICD10 mapping, which does not label mappings as ‘default’/‘alternative’/‘requires checking’ but simply maps to all ICD10 codes ‘E10-E14’:

all_lkps_maps$read_v2_icd10 %>%
  filter(read_code == 'C10..') %>%
  codemapper:::reformat_read_v2_icd10(icd10_lkp = all_lkps_maps$icd10_lkp) %>%
  select(-.rowid) %>%
  append_read_icd10_descriptions(read_type = "read2") %>% 
  select(-icd10_dagger_asterisk) %>% 
  flextable()

sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] flextable_0.6.10      codemapper_0.0.0.9000 ukbwranglr_0.0.0.9000
 [4] targets_0.8.0         crosstalk_1.1.1       readxl_1.3.1         
 [7] reactable_0.2.3       forcats_0.5.1         stringr_1.4.0        
[10] dplyr_1.0.7           purrr_0.3.4           readr_2.0.2          
[13] tidyr_1.1.4           tibble_3.1.4          ggplot2_3.3.5        
[16] tidyverse_1.3.1       workflowr_1.6.2      

loaded via a namespace (and not attached):
 [1] fs_1.5.0          bit64_4.0.5       lubridate_1.7.10  httr_1.4.2       
 [5] rprojroot_2.0.2   tools_4.1.0       backports_1.2.1   bslib_0.3.1      
 [9] utf8_1.2.2        R6_2.5.1          lazyeval_0.2.2    DBI_1.1.1        
[13] colorspace_2.0-2  withr_2.4.3       tidyselect_1.1.1  processx_3.5.2   
[17] bit_4.0.4         compiler_4.1.0    git2r_0.28.0      cli_3.0.1        
[21] rvest_1.0.1       xml2_1.3.2        officer_0.4.1     sass_0.4.0       
[25] scales_1.1.1      callr_3.7.0       systemfonts_1.0.4 digest_0.6.28    
[29] rmarkdown_2.11    base64enc_0.1-3   pkgconfig_2.0.3   htmltools_0.5.2  
[33] dbplyr_2.1.1      fastmap_1.1.0     highr_0.9         htmlwidgets_1.5.4
[37] rlang_0.4.11      RSQLite_2.2.9     rstudioapi_0.13   shiny_1.7.0      
[41] jquerylib_0.1.4   generics_0.1.0    jsonlite_1.7.2    zip_2.2.0        
[45] magrittr_2.0.1    Rcpp_1.0.7        munsell_0.5.0     fansi_0.5.0      
[49] gdtools_0.2.4     lifecycle_1.0.1   stringi_1.7.4     whisker_0.4      
[53] yaml_2.2.1        blob_1.2.2        grid_4.1.0        promises_1.2.0.1 
[57] crayon_1.4.1      haven_2.4.3       hms_1.1.1         knitr_1.34       
[61] ps_1.6.0          pillar_1.6.3      uuid_0.1-4        igraph_1.2.6     
[65] codetools_0.2-18  reprex_2.0.1      glue_1.4.2        evaluate_0.14    
[69] data.table_1.14.2 renv_0.13.2       modelr_0.1.8      vctrs_0.3.8      
[73] tzdb_0.1.2        httpuv_1.6.3      cellranger_1.1.0  gtable_0.3.0     
[77] reactR_0.4.4      assertthat_0.2.1  cachem_1.0.6      xfun_0.24        
[81] mime_0.12         xtable_1.8-4      broom_0.7.9       later_1.3.0      
[85] memoise_2.0.0     ellipsis_0.3.2