Last updated: 2022-06-10
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Knit directory: codemapper_notes/
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| Rmd | 92339ca | rmgpanw | 2022-04-28 | various code tweaks; rerun targets pipeline |
| html | 92339ca | rmgpanw | 2022-04-28 | various code tweaks; rerun targets pipeline |
| html | ae02335 | Chuin Ying Ung | 2022-02-22 | update notes for read3_icd10 |
| html | 1df97df | rmgpanw | 2022-02-17 | incorporate icd9 and icd10 to phecode maps |
| Rmd | 81047b4 | rmgpanw | 2022-02-17 | setup for gitlab CI with pkgdown site and test coverage; start adding read3 to snomed mapping |
| html | 81047b4 | rmgpanw | 2022-02-17 | setup for gitlab CI with pkgdown site and test coverage; start adding read3 to snomed mapping |
| html | 5c2a3e3 | Chuin Ying Ung | 2022-02-17 | update _targets.R (housekeeping) and phecode.Rmd |
| Rmd | f8d1889 | rmgpanw | 2021-10-09 | icd10 codes now returned as ALT CODE; codemapper app includes self-reported codes |
| html | f8d1889 | rmgpanw | 2021-10-09 | icd10 codes now returned as ALT CODE; codemapper app includes self-reported codes |
| Rmd | 919be0d | rmgpanw | 2021-10-07 | renamed functions and made shiny app for selecting codes |
| html | 919be0d | rmgpanw | 2021-10-07 | renamed functions and made shiny app for selecting codes |
| Rmd | d285b07 | rmgpanw | 2021-09-29 | add analysis/reformat_all_lkps_maps.Rmd to _targets.R; start analysis/clinical_codes_lkps_and_mappings.Rmd |
library(codemapper)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✔ ggplot2 3.3.5 ✔ purrr 0.3.4
#> ✔ tibble 3.1.7 ✔ dplyr 1.0.9
#> ✔ tidyr 1.2.0 ✔ stringr 1.4.0
#> ✔ readr 2.1.2 ✔ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
library(targets)
tar_load(ALL_LKPS_MAPS_DB)This vignette describes how to work with clinical codes using
codemapper. Specifically:
The functions provided by codemapper rely on UK Biobank
resource 592, which includes an Excel workbook containing lookup and
mapping tables, and the NHSBSA BNF to SNOMED code mapping file
(available here).
These tables have been converted into a named list of data frames. This
can be retrieved with TODO():
# retrieve code mappings in .Rdata format
targets::tar_load(all_lkps_maps)
# each item in the list is a sheet in the UKB Excel workbook (resource 592)
names(all_lkps_maps)
#> [1] "bnf_lkp" "dmd_lkp"
#> [3] "icd9_lkp" "icd10_lkp"
#> [5] "icd9_icd10" "read_v2_lkp"
#> [7] "read_v2_drugs_lkp" "read_v2_drugs_bnf"
#> [9] "read_v2_icd9" "read_v2_icd10"
#> [11] "read_v2_opcs4" "read_v2_read_ctv3"
#> [13] "read_ctv3_lkp" "read_ctv3_icd9"
#> [15] "read_ctv3_icd10" "read_ctv3_opcs4"
#> [17] "read_ctv3_read_v2" "opcs4_lkp"
#> [19] "self_report_cancer" "self_report_medication"
#> [21] "self_report_operation" "self_report_non_cancer"
#> [23] "bnf_dmd" "self_report_med_to_atc_map"
#> [25] "read_ctv3_sct" "phecode_lkp"
#> [27] "icd10_phecode" "icd9_phecode"To lookup details for a particular code, use
lookup_codes(). Setting
preferred_description_only to TRUE will return
only the preferred code descriptions if synonyms are present
(read2 and read3 may include multiple
descriptions for the same code):
# Some Read2 codes for T1DM
t1dm_read2 <- c("C10E.", "C108.")
# lookup details
lookup_codes(codes = t1dm_read2,
code_type = "read2",
all_lkps_maps = all_lkps_maps,
preferred_description_only = TRUE)
#> # A tibble: 2 × 3
#> code description code_type
#> <chr> <chr> <chr>
#> 1 C108. Insulin dependent diabetes mellitus read2
#> 2 C10E. Type 1 diabetes mellitus read2By default, the output is standardised to produce the columns shown
above. To output the original formatting from UK Biobank
resource 592, set standardise_output to
FALSE:
# lookup details
lookup_codes(codes = t1dm_read2,
code_type = "read2",
all_lkps_maps = all_lkps_maps,
preferred_description_only = TRUE,
standardise_output = FALSE)
#> # A tibble: 2 × 4
#> .rowid read_code term_code term_description
#> <int> <chr> <chr> <chr>
#> 1 56884 C108. 00 Insulin dependent diabetes mellitus
#> 2 57026 C10E. 00 Type 1 diabetes mellitusAn example: map codes for type 1 diabetes from Read2 to Read3. Note
that both of the Read 2 codes C10E. and C108.
map to a single read3 code, X40J4:
# Some Read2 codes for T1DM
t1dm_read2 <- c("C10E.", "C108.")
# lookup details
map_codes(codes = t1dm_read2,
from = "read2",
to = "read3",
all_lkps_maps = all_lkps_maps)
#> # A tibble: 5 × 3
#> code description code_type
#> <chr> <chr> <chr>
#> 1 X40J4 Type I diabetes mellitus read3
#> 2 X40J4 Type 1 diabetes mellitus read3
#> 3 X40J4 IDDM - Insulin-dependent diabetes mellitus read3
#> 4 X40J4 Juvenile onset diabetes mellitus read3
#> 5 X40J4 Insulin-dependent diabetes mellitus read3Note that preferred_description_only cannot be
TRUE if standardise_output is
FALSE with map_codes (will raise an error).
This is because some codes may otherwise be ‘lost’ in the mapping
process. When standardise_output is TRUE, the
mapped codes from map_codes are passed on to
lookup_codes, at which point one can request to return only
preferred code descriptions:
# mapping the Read 2 code "D4104" to Read 3 only returns the secondary Read 3
# description (`TERMV3_TYPE` == "S"), unlike for "D4103".
map_codes(codes = c("D4103", "D4104"),
from = "read2",
to = "read3",
all_lkps_maps = all_lkps_maps,
codes_only = FALSE,
preferred_description_only = FALSE,
standardise_output = FALSE) %>%
dplyr::select(tidyselect::contains("V3"))
#> # A tibble: 2 × 4
#> READV3_CODE TERMV3_CODE TERMV3_TYPE TERMV3_DESC
#> <chr> <chr> <chr> <chr>
#> 1 D4103 Y20e3 P Polycythaemia due to cyanotic respiratory…
#> 2 D4104 Y20e6 S Renal polycythaemia# if `standardise_output` is `TRUE`, then `preferred_description_only` may also
# be set to `TRUE`
map_codes(codes = c("D4103", "D4104"),
from = "read2",
to = "read3",
all_lkps_maps = all_lkps_maps,
codes_only = FALSE,
preferred_description_only = TRUE,
standardise_output = TRUE)
#> # A tibble: 2 × 3
#> code description code_type
#> <chr> <chr> <chr>
#> 1 D4103 Polycythaemia due to cyanotic respiratory disease read3
#> 2 D4104 Secondary polycythaemia with excess erythropoietin read3Mapping to ICD is more problematic as some results are a range of ICD codes (note also that for ICD-10, the mapping sheets use an alternative code format which removes any “.” characters):
map_codes(codes = t1dm_read2,
from = "read2",
to = "icd10",
all_lkps_maps = all_lkps_maps,
codes_only = TRUE,
standardise_output = FALSE,
# by default, an error is raised if any unrecognised codes are present
unrecognised_codes = 'warning')
#> Warning in handle_unrecognised_codes(unrecognised_codes = unrecognised_codes, :
#> The following 2 codes were not found for 'read2' in table 'read_v2_icd10':
#> 'C10E.', 'C108.'
#> No codes found after mapping. Returning `NULL`
#> NULLThe available mappings do not cover all possible mapping directions.
For example, while there are mappings for Read2 to ICD-10, there is no
mapping for ICD-10 to Read2. For cases like this, by setting the
reverse_mapping argument to ‘warning’ for
map_codes() an attempt to map anyway by using the same
mapping sheet in reverse (e.g. for mapping ICD-10 to Read2,
map_codes uses the read_v2_icd10 mapping
sheet).
# find ICD-10 code matching "diabetic retinopathy"
icd10_diabetic_retinopathy <-
code_descriptions_like(
reg_expr = "diabetic retinopathy",
code_type = "icd10",
all_lkps_maps = all_lkps_maps,
ignore_case = TRUE,
codes_only = TRUE
)
# attempting to map this to Read 2 returns a NULL result however
map_codes(
codes = icd10_diabetic_retinopathy,
from = "icd10",
to = "read2",
all_lkps_maps = all_lkps_maps,
standardise_output = TRUE,
codes_only = FALSE,
reverse_mapping = 'warning'
) %>%
knitr::kable()
#> Warning in check_mapping_args(from = from, to = to, reverse_mapping =
#> reverse_mapping): No mapping sheet available for this request. Attempting to map
#> anyway using: read_v2_icd10| code | description | code_type |
|---|---|---|
| C1087 | Insulin dependent diabetes mellitus with retinopathy | read2 |
| C1087 | Type I diabetes mellitus with retinopathy | read2 |
| C1087 | Type 1 diabetes mellitus with retinopathy | read2 |
| C1096 | Non-insulin-dependent diabetes mellitus with retinopathy | read2 |
| C1096 | Type II diabetes mellitus with retinopathy | read2 |
| C1096 | Type 2 diabetes mellitus with retinopathy | read2 |
| C10E7 | Type 1 diabetes mellitus with retinopathy | read2 |
| C10E7 | Type I diabetes mellitus with retinopathy | read2 |
| C10E7 | Insulin dependent diabetes mellitus with retinopathy | read2 |
| C10EP | Type 1 diabetes mellitus with exudative maculopathy | read2 |
| C10EP | Type I diabetes mellitus with exudative maculopathy | read2 |
| C10F6 | Type 2 diabetes mellitus with retinopathy | read2 |
| C10F6 | Type II diabetes mellitus with retinopathy | read2 |
| C10FQ | Type 2 diabetes mellitus with exudative maculopathy | read2 |
| C10FQ | Type II diabetes mellitus with exudative maculopathy | read2 |
| F420. | Diabetic retinopathy | read2 |
| F4200 | Background diabetic retinopathy | read2 |
| F4201 | Proliferative diabetic retinopathy | read2 |
| F4202 | Preproliferative diabetic retinopathy | read2 |
| F4203 | Advanced diabetic maculopathy | read2 |
| F4204 | Diabetic maculopathy | read2 |
| F4205 | Advanced diabetic retinal disease | read2 |
| F4206 | Non proliferative diabetic retinopathy | read2 |
| F4207 | High risk proliferative diabetic retinopathy | read2 |
| F4208 | High risk non proliferative diabetic retinopathy | read2 |
| F420z | Diabetic retinopathy NOS | read2 |
Use code_descriptions_like(). For example, to find Read2
codes that match the description ‘diabetic retinopathy’:
code_descriptions_like(reg_expr = "diabetic retinopathy",
code_type = "read2",
all_lkps_maps = all_lkps_maps,
ignore_case = TRUE,
codes_only = FALSE,
preferred_description_only = TRUE) %>%
head() %>%
knitr::kable()| code | description | code_type |
|---|---|---|
| 2BBJ. | O/E - no right diabetic retinopathy | read2 |
| 2BBk. | O/E - right eye stable treated proliferative diabetic retinopathy | read2 |
| 2BBK. | O/E - no left diabetic retinopathy | read2 |
| 2BBl. | O/E - left eye stable treated proliferative diabetic retinopathy | read2 |
| 2BBo. | O/E - sight threatening diabetic retinopathy | read2 |
| 2BBP. | O/E - right eye background diabetic retinopathy | read2 |
To get the children codes, use codes_starting_with().
This will return all unique clinical codes that start with the codes of
interest:
# Some Read2 codes for T1DM
t1dm_read2 <- c("C10E.", "C108.")
# lookup details
codes_starting_with(codes = t1dm_read2,
code_type = "read2",
all_lkps_maps = all_lkps_maps,
codes_only = TRUE,
standardise_output = FALSE) %>%
knitr::kable()| x |
|---|
| C108. |
| C10E. |
Note: Some coding systems include a ‘.’ (e.g. ICD-10) - this may return unexpected results with
codes_starting_with(), as this function searches using regexs and ‘.’ is interpreted as a wildcard.
By default, a character vector of codes is returned. To return a data
frame including code descriptions, set the argument
codes_only to FALSE:
# Some Read2 codes for T1DM
t1dm_read2 <- c("C10E.", "C108.")
# lookup details
codes_starting_with(codes = t1dm_read2,
code_type = "read2",
all_lkps_maps = all_lkps_maps,
codes_only = FALSE) %>%
head() %>%
knitr::kable()| code | description | code_type |
|---|---|---|
| C108. | Insulin dependent diabetes mellitus | read2 |
| C10E. | Type 1 diabetes mellitus | read2 |
ICD-10 codes are presented in two different formats in the
icd10_lkp table: ICD_10 and
ALT_CODE. The latter is how ICD-10 codes are recorded in
UKB. However, an ‘X’ is appended to 3 character codes without any 4
character children (e.g. A38X, ‘Scarlet fever’).
Another issue with the ICD_10 format is that is contains
some duplicated codes e.g. I70.0 appears 3 times. This is
because of MODIFIER_5 - the ALT_CODE format
records a different code for each of these.
sessionInfo()
#> R version 4.2.0 (2022-04-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur/Monterey 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] targets_0.12.0 forcats_0.5.1 stringr_1.4.0
#> [4] dplyr_1.0.9 purrr_0.3.4 readr_2.1.2
#> [7] tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.5
#> [10] tidyverse_1.3.1 codemapper_0.0.0.9001 workflowr_1.7.0
#>
#> loaded via a namespace (and not attached):
#> [1] httr_1.4.2 sass_0.4.1 jsonlite_1.8.0 modelr_0.1.8
#> [5] bslib_0.3.1 shiny_1.7.1 assertthat_0.2.1 getPass_0.2-2
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#> [13] yaml_2.3.5 pillar_1.7.0 backports_1.4.1 glue_1.6.2
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#> [21] htmltools_0.5.2 httpuv_1.6.5 pkgconfig_2.0.3 broom_0.8.0
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#> [41] evaluate_0.15 ps_1.7.0 fs_1.5.2 fansi_1.0.3
#> [45] xml2_1.3.3 tools_4.2.0 data.table_1.14.2 hms_1.1.1
#> [49] lifecycle_1.0.1 munsell_0.5.0 reprex_2.0.1 callr_3.7.0
#> [53] compiler_4.2.0 jquerylib_0.1.4 rlang_1.0.2 grid_4.2.0
#> [57] rstudioapi_0.13 igraph_1.3.1 rmarkdown_2.14 gtable_0.3.0
#> [61] codetools_0.2-18 DBI_1.1.2 R6_2.5.1 lubridate_1.8.0
#> [65] knitr_1.39 fastmap_1.1.0 utf8_1.2.2 rprojroot_2.0.3
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#> [73] tidyselect_1.1.2 xfun_0.30