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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 = eunomia_dir())
cdm <- cdm_from_con(db, cdm_schema = "main", write_schema = c(prefix = "cg_", schema = "main"))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] "Ingredient" "Quant Clinical Drug" "Branded Drug"
#> [4] "Quant Branded Drug" "Clinical Drug Comp" "Branded Drug Comp"
#> [7] "CVX" "Clinical Drug" "Branded Pack"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")
#>
#> - aspirin (2 codes)If we also want the details of these concept IDs we can get these like so.
getDrugIngredientCodes(cdm = cdm, name = "aspirin", withConceptDetails = TRUE)
#> $aspirin
#> # A tibble: 2 × 4
#> concept_id concept_name domain_id vocabulary_id
#> <int> <chr> <chr> <chr>
#> 1 19059056 Aspirin 81 MG Oral Tablet Drug RxNorm
#> 2 1112807 Aspirin Drug RxNormAnd if we want codelists for all drug ingredients we can simply omit the name argument and all ingredients will be returned.
ing <- getDrugIngredientCodes(cdm = cdm)
ing$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> "Synthea synthetic health database", "Synthea synthet…
#> $ 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", "Childhood asthma", "Asthma", "overall", "…
#> $ variable_level <chr> NA, "4051466", "317009", 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", "96", "5", "101", "96", "5"
#> $ additional_name <chr> "overall", "source_concept_name &&& source_concept_id…
#> $ additional_level <chr> "overall", "Childhood asthma &&& 4051466 &&& conditio…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.