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You can install CodelistGenerator from CRAN
install.packages("CodelistGenerator")Or you can also install the development version of CodelistGenerator
install.packages("remotes")
remotes::install_github("darwin-eu/CodelistGenerator")library(dplyr)
library(CDMConnector)
library(CodelistGenerator)For this example we’ll use the Eunomia dataset (which only contains a subset of the OMOP CDM vocabularies)
db <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())
cdm <- cdmFromCon(db,
cdmSchema = "main",
writeSchema = "main",
writePrefix = "cg_")OMOP CDM vocabularies are frequently updated, and we can identify the version of the vocabulary of our Eunomia data
getVocabVersion(cdm = cdm)
#> [1] "v5.0 18-JAN-19"CodelistGenerator provides various other functions to explore the vocabulary tables. For example, we can see the the different concept classes of standard concepts used for drugs
getConceptClassId(cdm,
standardConcept = "Standard",
domain = "Drug")
#> [1] "Branded Drug" "Branded Drug Comp" "Branded Pack"
#> [4] "Clinical Drug" "Clinical Drug Comp" "CVX"
#> [7] "Ingredient" "Quant Branded Drug" "Quant Clinical Drug"CodelistGenerator provides functions to extract code lists based on vocabulary hierarchies. One example is `getDrugIngredientCodes, which we can use, for example, to get all the concept IDs used to represent aspirin.
getDrugIngredientCodes(cdm = cdm, name = "aspirin", nameStyle = "{concept_name}")
#>
#> - aspirin (2 codes)And if we want codelists for all drug ingredients we can simply omit the name argument and all ingredients will be returned.
ing <- getDrugIngredientCodes(cdm = cdm, nameStyle = "{concept_name}")
ing$aspirin
#> [1] 19059056 1112807
ing$diclofenac
#> [1] 1124300
ing$celecoxib
#> [1] 1118084CodelistGenerator can also support systematic searches of the vocabulary tables to support codelist development. A little like the process for a systematic review, the idea is that for a specified search strategy, CodelistGenerator will identify a set of concepts that may be relevant, with these then being screened to remove any irrelevant codes by clinical experts.
We can do a simple search for asthma
asthma_codes1 <- getCandidateCodes(
cdm = cdm,
keywords = "asthma",
domains = "Condition"
)
asthma_codes1 |>
glimpse()
#> Rows: 2
#> Columns: 6
#> $ concept_id <int> 4051466, 317009
#> $ found_from <chr> "From initial search", "From initial search"
#> $ concept_name <chr> "Childhood asthma", "Asthma"
#> $ domain_id <chr> "Condition", "Condition"
#> $ vocabulary_id <chr> "SNOMED", "SNOMED"
#> $ standard_concept <chr> "S", "S"But perhaps we want to exclude certain concepts as part of the search strategy, in this case we can add these like so
asthma_codes2 <- getCandidateCodes(
cdm = cdm,
keywords = "asthma",
exclude = "childhood",
domains = "Condition"
)
asthma_codes2 |>
glimpse()
#> Rows: 1
#> Columns: 6
#> $ concept_id <int> 317009
#> $ found_from <chr> "From initial search"
#> $ concept_name <chr> "Asthma"
#> $ domain_id <chr> "Condition"
#> $ vocabulary_id <chr> "SNOMED"
#> $ standard_concept <chr> "S"We can compare these two code lists like so
compareCodelists(asthma_codes1, asthma_codes2)
#> # A tibble: 2 × 3
#> concept_id concept_name codelist
#> <int> <chr> <chr>
#> 1 4051466 Childhood asthma Only codelist 1
#> 2 317009 Asthma BothWe can then also see non-standard codes these are mapped from, for example here we can see the non-standard ICD10 code that maps to a standard snowmed code for gastrointestinal hemorrhage returned by our search
Gastrointestinal_hemorrhage <- getCandidateCodes(
cdm = cdm,
keywords = "Gastrointestinal hemorrhage",
domains = "Condition"
)
Gastrointestinal_hemorrhage |>
glimpse()
#> Rows: 1
#> Columns: 6
#> $ concept_id <int> 192671
#> $ found_from <chr> "From initial search"
#> $ concept_name <chr> "Gastrointestinal hemorrhage"
#> $ domain_id <chr> "Condition"
#> $ vocabulary_id <chr> "SNOMED"
#> $ standard_concept <chr> "S"summariseCodeUse(list("asthma" = asthma_codes1$concept_id),
cdm = cdm) |>
glimpse()
#> Rows: 6
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1
#> $ cdm_name <chr> "An OMOP CDM database", "An OMOP CDM database", "An O…
#> $ group_name <chr> "codelist_name", "codelist_name", "codelist_name", "c…
#> $ group_level <chr> "asthma", "asthma", "asthma", "asthma", "asthma", "as…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "overall", "Asthma", "Childhood asthma", "overall", "…
#> $ variable_level <chr> NA, "317009", "4051466", NA, "4051466", "317009"
#> $ estimate_name <chr> "record_count", "record_count", "record_count", "pers…
#> $ estimate_type <chr> "integer", "integer", "integer", "integer", "integer"…
#> $ estimate_value <chr> "101", "5", "96", "101", "96", "5"
#> $ additional_name <chr> "source_concept_name &&& source_concept_id &&& source…
#> $ additional_level <chr> "NA &&& NA &&& NA &&& NA", "Asthma &&& 317009 &&& 195…These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.