Read 2 to ICD10 mapping

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Last updated: 2022-03-16

Checks: 5 2

Knit directory: codemapper/

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Ignored files:
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    Ignored:    all_lkps_maps.db
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    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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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/read2_icd10_mapping.Rmd) and HTML (public/read2_icd10_mapping.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	b425304	rmgpanw	2022-03-15	add code and tests to reformat read2 to icd10 mapping table
Rmd	dfbc621	Chuin Ying Ung	2022-02-22	add mapping notes
Rmd	5b80a79	Chuin Ying Ung	2022-02-22	update 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_v2_icd10 <- all_lkps_maps$read_v2_icd10
icd10_lkp <- all_lkps_maps$icd10_lkp

# utility functions
append_read_icd10_descriptions <- function(df,
                                            read_type = "read2") {
  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

• Where a Read 2 code maps to multiple ICD10 codes, a range of ICD10 codes is presented in the icd10_code column. This needs reformatting (to allow programmatic mapping from Read 2 to ICD10) so that each ICD10 code appears on a separate row.

• The following additional reformatting operations are also required to ensure that the ICD10 codes in this mapping table match the ALT_CODE format in lookup table icd10_lkp:

  • Remove appended ‘D’/‘A’ characters from dagger/asterisk ICD10 codes, and create a separate indicator column for these instead.

  • Some undivided 3 character ICD10 codes appear inconsistently without an appended ‘X’. For example, ‘A64X’ appears as ‘A50-A64’, ‘A65X’ appears as ‘A65-A69’, ‘A70X’ as ‘A70-A74’, ‘A89X’ as ‘A80-A89’ and ‘A99X’ as ‘A92-A99’. The ‘X’ is re-appended to these codes during reformatting to match the ALT_CODE format in lookup table icd10_lkp.

  • ICD10 code “C836” (“Diffuse non-Hodgkin’s lymphoma - Undifferentiated (diffuse)”) has been removed from ICD10 and does not exist in the icd10_lkp table. It is therefore also removed from the reformatted read_v2_icd10 mapping table.

• It is often appropriate to map a single Read 2 code to multiple ICD10 codes, where more than one ICD10 code is required to define a single specific Read 2 cod (see examples below). However, it may not be desirable to map a single Read 2 code with a non-specific description to multiple specific ICD10 codes. For example, the Read 2 code for ‘Diabetes mellitus’, ‘C10..’, is mapped to ICD10 codes ‘E10-E14’, which include both type 1 and type 2 diabetes as well as all their potential complications (e.g. renal and ophthalmic complications). The map_codes function in codemapper excludes this category of mappings (flagged as icd10_code_def ‘2’ in the read_v2_icd10 mapping table) by default.

Unrecognised ICD10 codes

[1] TRUE

All Read 2 codes in the read_v2_icd10 mapping table are present in the read_v2_lkp lookup table.

# identify unrecognised ICD10 codes
unrecognised_icd10_codes <- subset(read_v2_icd10$icd10_code,
       !read_v2_icd10$icd10_code %in% all_lkps_maps$icd10_lkp$ALT_CODE) %>% 
  unique()

There are 1438 ICD10 codes in the read_v2_icd10 mapping table that are not present in the icd10_lkp table (ALT_CODE format). These are mostly flagged by values in column icd10_code_def, with two exceptions:

1. ICD10 code ‘C836’ has been removed from ICD10 and does not appear in the lookup icd10_lkptable.
2. Several undivided 3 character ICD10 codes appear inconsistently without an appended ‘X’ in the read_v2_icd10 mapping table.

Some examples, ‘C836’ and ‘A64’ (the latter should appear as ‘A64X’):

read_v2_icd10 %>% 
  filter(str_detect(icd10_code,
                    pattern = "A64|C836")) %>% 
  append_read_icd10_descriptions() %>% 
  select(-.rowid) %>% 
  flextable()

Mapping types: icd10_code_def

Column icd10_code_def signifies, for example, whether or not the Read 2 code matches to a single ICD-10 code, if the ICD-10 code is a parent code that links to several child codes, if the ICD-10 code is asterisk or dagger code, a dagger-asterisk combination, etc.

Below are some example codes for each value in icd10_code_def:

read_v2_icd10 %>%
  filter(!is.na(icd10_code_def)) %>%
  mutate(
    icd10_contains_dash = ifelse(str_detect(icd10_code,
                                        "-"),
                             yes = "dash",
                             no = NA_character_),
    icd10_contains_comma = ifelse(str_detect(icd10_code,
                                         ","),
                              yes = "comma",
                              no = NA_character_),
    icd10_contains_space = ifelse(str_detect(icd10_code,
                                         "\\s"),
                              yes = "space",
                              no = NA_character_),
    icd10_contains_plus = ifelse(str_detect(icd10_code,
                                        "\\+"),
                             yes = "plus",
                             no = NA_character_),
    icd10_contains_da_ending = ifelse(str_detect(icd10_code,
                                        "[D|A]$"),
                             yes = "DAending",
                             no = NA_character_)
  ) %>% 
  unite(col = "icd10_pattern", 
        starts_with("icd10_contains_"), 
        sep = "_", 
        remove = TRUE, 
        na.rm = TRUE) %>% 
  group_by(icd10_code_def,
           icd10_pattern) %>%
  slice(1L) %>%
  ungroup() %>% 
  append_read_icd10_descriptions() %>% 
  arrange(description_icd10) %>% 
  select(icd10_code_def,
         icd10_pattern,
         everything()) %>% 
  select(-.rowid) %>% 
  flextable::as_grouped_data("icd10_code_def") %>% 
  flextable() %>% 
  suppressWarnings()

Mappings categorised as icd10_code_def ‘1’, ‘5’ or ‘8’ are one-to-one, whereas the other categories are one-to-many. Read codes in category ‘2’ have non-specific descriptions and map to multiple specific ICD10 codes, which may be conflicting. The examples above are re-shown below with ICD10 code descriptions appended.

read_v2_icd10 %>%
  filter(!is.na(icd10_code_def)) %>%
  mutate(
    icd10_contains_dash = ifelse(str_detect(icd10_code,
                                        "-"),
                             yes = "dash",
                             no = NA_character_),
    icd10_contains_comma = ifelse(str_detect(icd10_code,
                                         ","),
                              yes = "comma",
                              no = NA_character_),
    icd10_contains_space = ifelse(str_detect(icd10_code,
                                         "\\s"),
                              yes = "space",
                              no = NA_character_),
    icd10_contains_plus = ifelse(str_detect(icd10_code,
                                        "\\+"),
                             yes = "plus",
                             no = NA_character_),
    icd10_contains_da_ending = ifelse(str_detect(icd10_code,
                                        "[D|A]$"),
                             yes = "DAending",
                             no = NA_character_)
  ) %>% 
  unite(col = "icd10_pattern", 
        starts_with("icd10_contains_"), 
        sep = "_", 
        remove = TRUE, 
        na.rm = TRUE) %>% 
  group_by(icd10_code_def,
           icd10_pattern) %>%
  slice(1L) %>%
  ungroup() %>% 
  codemapper:::reformat_read_v2_icd10(icd10_lkp = all_lkps_maps$icd10_lkp) %>% 
  append_read_icd10_descriptions() %>% 
  arrange(description_icd10) %>% 
  select(icd10_code_def,
         icd10_pattern,
         everything()) %>% 
  select(-.rowid) %>% 
  reactable(filterable = TRUE,
            searchable = TRUE,
            showPageSizeOptions = TRUE,
            pageSizeOptions = c(10, 25, 50, 10),
            groupBy = "icd10_code_def")

Session information

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          DBI_1.1.1         colorspace_2.0-2 
[13] withr_2.4.3       tidyselect_1.1.1  processx_3.5.2    bit_4.0.4        
[17] compiler_4.1.0    git2r_0.28.0      cli_3.0.1         rvest_1.0.1      
[21] xml2_1.3.2        officer_0.4.1     sass_0.4.0        scales_1.1.1     
[25] callr_3.7.0       systemfonts_1.0.4 digest_0.6.28     rmarkdown_2.11   
[29] base64enc_0.1-3   pkgconfig_2.0.3   htmltools_0.5.2   dbplyr_2.1.1     
[33] fastmap_1.1.0     highr_0.9         htmlwidgets_1.5.4 rlang_0.4.11     
[37] RSQLite_2.2.9     rstudioapi_0.13   shiny_1.7.0       jquerylib_0.1.4  
[41] generics_0.1.0    jsonlite_1.7.2    zip_2.2.0         magrittr_2.0.1   
[45] Rcpp_1.0.7        munsell_0.5.0     fansi_0.5.0       gdtools_0.2.4    
[49] lifecycle_1.0.1   stringi_1.7.4     whisker_0.4       yaml_2.2.1       
[53] blob_1.2.2        grid_4.1.0        promises_1.2.0.1  crayon_1.4.1     
[57] haven_2.4.3       hms_1.1.1         knitr_1.34        ps_1.6.0         
[61] pillar_1.6.3      uuid_0.1-4        igraph_1.2.6      codetools_0.2-18 
[65] reprex_2.0.1      glue_1.4.2        evaluate_0.14     data.table_1.14.2
[69] renv_0.13.2       modelr_0.1.8      vctrs_0.3.8       tzdb_0.1.2       
[73] httpuv_1.6.3      cellranger_1.1.0  gtable_0.3.0      reactR_0.4.4     
[77] assertthat_0.2.1  cachem_1.0.6      xfun_0.24         mime_0.12        
[81] xtable_1.8-4      broom_0.7.9       later_1.3.0       memoise_2.0.0    
[85] ellipsis_0.3.2