The goal of DrugExposureDiagnostics is to summarise ingredient specific drug exposure data in the OMOP CDM.
You can install the DrugExposureDiagnostics from CRAN like this:
install.packages("DrugExposureDiagnostics")
or install the development version:
install.packages("remotes")
remotes::install_github("darwin-eu/DrugExposureDiagnostics")
citation("DrugExposureDiagnostics")
#> Warning in citation("DrugExposureDiagnostics"): could not determine year for
#> 'DrugExposureDiagnostics' from package DESCRIPTION file
#> To cite package 'DrugExposureDiagnostics' in publications use:
#>
#> Inberg G, Burn E, Burkard T (????). _DrugExposureDiagnostics:
#> Diagnostics for OMOP Common Data Model Drug Records_. R package
#> version 1.1.0, https://github.com/darwin-eu/DrugExposureDiagnostics,
#> <https://darwin-eu.github.io/DrugExposureDiagnostics/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {DrugExposureDiagnostics: Diagnostics for OMOP Common Data Model Drug Records},
#> author = {Ger Inberg and Edward Burn and Theresa Burkard},
#> note = {R package version 1.1.0, https://github.com/darwin-eu/DrugExposureDiagnostics},
#> url = {https://darwin-eu.github.io/DrugExposureDiagnostics/},
#> }
library(DrugExposureDiagnostics) library(CDMConnector) library(dplyr)
cdm <- mockDrugExposure() #> Note: method with signature 'DBIConnection#Id' chosen for function 'dbExistsTable', #> target signature 'duckdb_connection#Id'. #> "duckdb_connection#ANY" would also be valid
Let´s look at the ingredient acetaminophen (https://athena.ohdsi.org/search-terms/terms/1125315).
We can run all the checks available in ´DrugExposureDiagnostics´ using the ´executeChecks´ function.
all_checks <- executeChecks(
cdm = cdm,
ingredients = 1125315,
checks = c(
"missing", "exposureDuration", "type", "route", "sourceConcept", "daysSupply",
"verbatimEndDate", "dose", "sig", "quantity", "diagnosticsSummary"
)
)
#> population after earliestStartDate smaller than sample, sampling ignored
#> ℹ The following estimates will be computed:
#> • daily_dose: count_missing, percentage_missing, mean, sd, q05, q25, median,
#> q75, q95, min, max
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2024-12-19 18:52:57.147139
#>
#> Registered S3 method overwritten by 'visOmopResults':
#> method from
#> tidy.summarised_result omopgenerics
#>
#> ✔ Summary finished, at 2024-12-19 18:52:57.302844
#> `sample_size` casted to character.
#> `sample_size` eliminated from settings as all elements are NA.
The output is a list which contains the following set of tibbles:
names(all_checks) #> [1] "conceptSummary" "missingValuesOverall" #> [3] "missingValuesByConcept" "drugExposureDurationOverall" #> [5] "drugExposureDurationByConcept" "drugTypesOverall" #> [7] "drugTypesByConcept" "drugRoutesOverall" #> [9] "drugRoutesByConcept" "drugSourceConceptsOverall" #> [11] "drugDaysSupply" "drugDaysSupplyByConcept" #> [13] "drugVerbatimEndDate" "drugVerbatimEndDateByConcept" #> [15] "drugDose" "drugSig" #> [17] "drugSigByConcept" "drugQuantity" #> [19] "drugQuantityByConcept" "diagnosticsSummary" #> [21] "metadata"
The first item contains information on the concept ids that are used in the database for a given ingredient.
glimpse(all_checks$conceptSummary)
#> Rows: 6
#> Columns: 26
#> Rowwise:
#> $ drug_concept_id <int> 1127078, 40162522, 40229134, 40231925, 191…
#> $ drug <chr> "acetaminophen 750 MG / Hydrocodone Bitart…
#> $ ingredient_concept_id <int> 1125315, 1125315, 1125315, 1125315, 112531…
#> $ ingredient <chr> "acetaminophen", "acetaminophen", "acetami…
#> $ n_records <int> 19, 18, 12, 10, 14, 13
#> $ n_patients <int> 13, 15, 11, 9, 13, 11
#> $ domain_id <chr> "Drug", "Drug", "Drug", "Drug", "Drug", "D…
#> $ vocabulary_id <chr> "RxNorm", "RxNorm", "RxNorm", "RxNorm", "R…
#> $ concept_class_id <chr> "Clinical Drug", "Clinical Drug", "Clinica…
#> $ standard_concept <chr> "S", "S", "S", "S", "S", "S"
#> $ concept_code <chr> "833036", "313782", "1043400", "857005", "…
#> $ valid_start_date <date> 1970-01-01, 1970-01-01, 1970-01-01, 1970-0…
#> $ valid_end_date <date> 2099-12-31, 2099-12-31, 2099-12-31, 2099-1…
#> $ invalid_reason <chr> NA, NA, NA, NA, NA, NA
#> $ amount_value <dbl> 200, NA, NA, 300, 200, 200
#> $ amount_unit_concept_id <int> 9655, NA, NA, 9655, 9655, 9655
#> $ numerator_value <dbl> NA, 1, 1, NA, NA, NA
#> $ numerator_unit_concept_id <int> NA, 8576, 8576, NA, NA, NA
#> $ numerator_unit <chr> NA, NA, NA, NA, NA, NA
#> $ denominator_value <dbl> NA, 10, 10, NA, NA, NA
#> $ denominator_unit_concept_id <int> NA, 8576, 8576, NA, NA, NA
#> $ denominator_unit <chr> NA, NA, NA, NA, NA, NA
#> $ box_size <dbl> 0, 0, 0, 0, 0, 0
#> $ amount_unit <chr> NA, NA, NA, NA, NA, NA
#> $ dose_form <chr> "Oral Tablet", "Oral Tablet", "Oral Tablet…
#> $ result_obscured <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
all_checks$conceptSummary %>%
select("drug_concept_id", "drug")
#> # A tibble: 6 × 2
#> # Rowwise:
#> drug_concept_id drug
#> <int> <chr>
#> 1 1127078 acetaminophen 750 MG / Hydrocodone Bitartrate
#> 2 40162522 acetaminophen 325 MG Oral Tablet
#> 3 40229134 acetaminophen 21.7 MG/ML / Dextromethorphan
#> 4 40231925 acetaminophen 325 MG / Hydrocodone Bitartrate
#> 5 19133768 acetaminophen 160 MG Oral Tablet
#> 6 1127433 acetaminophen 325 MG / Oxycodone Hydrochloride
Other tibbles then contain information from the various checks performed.
For example, we can see a summary of missingness for the ingredient-related records in the drug exposure table, both overall and by concept.
all_checks$missingValuesOverall #> # A tibble: 15 × 10 #> # Rowwise: ingredient_concept_id, ingredient #> ingredient_concept_id ingredient variable n_records n_sample n_person #> <int> <chr> <chr> <int> <dbl> <dbl> #> 1 1125315 acetaminophen n_missing_dr… 44 10000 25 #> 2 1125315 acetaminophen n_missing_pe… 44 10000 25 #> 3 1125315 acetaminophen n_missing_dr… 44 10000 25 #> 4 1125315 acetaminophen n_missing_dr… 44 10000 25 #> 5 1125315 acetaminophen n_missing_dr… 44 10000 25 #> 6 1125315 acetaminophen n_missing_ve… 44 10000 25 #> 7 1125315 acetaminophen n_missing_dr… 44 10000 25 #> 8 1125315 acetaminophen n_missing_qu… 44 10000 25 #> 9 1125315 acetaminophen n_missing_da… 44 10000 25 #> 10 1125315 acetaminophen n_missing_sig 44 10000 25 #> 11 1125315 acetaminophen n_missing_ro… 44 10000 25 #> 12 1125315 acetaminophen n_missing_dr… 44 10000 25 #> 13 1125315 acetaminophen n_missing_dr… 44 10000 25 #> 14 1125315 acetaminophen n_missing_ro… 44 10000 25 #> 15 1125315 acetaminophen n_missing_do… 44 10000 25 #> # ℹ 4 more variables: n_records_not_missing_value <dbl>, #> # n_records_missing_value <dbl>, proportion_records_missing_value <dbl>, #> # result_obscured <lgl> all_checks$missingValuesByConcept #> # A tibble: 90 × 12 #> # Rowwise: drug_concept_id, drug, ingredient_concept_id, ingredient #> drug_concept_id drug ingredient_concept_id ingredient variable n_records #> <int> <chr> <int> <chr> <chr> <int> #> 1 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 2 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 3 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 4 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 5 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 6 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 7 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 8 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 9 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> 10 40229134 acetamin… 1125315 acetamino… n_missi… 6 #> # ℹ 80 more rows #> # ℹ 6 more variables: n_sample <dbl>, n_person <dbl>, #> # n_records_not_missing_value <dbl>, n_records_missing_value <dbl>, #> # proportion_records_missing_value <dbl>, result_obscured <lgl>
Or we can also see a summary of drug exposure duration (drug_exposure_end_date - drug_exposure_end_date + 1), again overall or by concept.
all_checks$drugExposureDurationOverall #> # A tibble: 1 × 18 #> # Rowwise: ingredient_concept_id #> ingredient_concept_id ingredient n_records n_sample n_person #> <int> <chr> <int> <dbl> <int> #> 1 1125315 acetaminophen 44 10000 25 #> # ℹ 13 more variables: n_non_negative_days <int>, n_negative_days <int>, #> # proportion_negative_days <dbl>, minimum_drug_exposure_days <dbl>, #> # q05_drug_exposure_days <dbl>, q10_drug_exposure_days <dbl>, #> # q25_drug_exposure_days <dbl>, median_drug_exposure_days <dbl>, #> # q75_drug_exposure_days <dbl>, q90_drug_exposure_days <dbl>, #> # q95_drug_exposure_days <dbl>, maximum_drug_exposure_days <dbl>, #> # result_obscured <lgl> all_checks$drugExposureDurationByConcept #> # A tibble: 6 × 20 #> # Rowwise: drug_concept_id, drug, ingredient_concept_id #> drug_concept_id drug ingredient_concept_id ingredient n_records n_sample #> <int> <chr> <int> <chr> <int> <dbl> #> 1 1127078 acetamino… 1125315 acetamino… 8 10000 #> 2 1127433 acetamino… 1125315 acetamino… 8 10000 #> 3 19133768 acetamino… 1125315 acetamino… 8 10000 #> 4 40162522 acetamino… 1125315 acetamino… 12 10000 #> 5 40229134 acetamino… 1125315 acetamino… 6 10000 #> 6 40231925 acetamino… 1125315 acetamino… NA NA #> # ℹ 14 more variables: n_person <int>, n_non_negative_days <int>, #> # n_negative_days <int>, proportion_negative_days <dbl>, #> # minimum_drug_exposure_days <dbl>, q05_drug_exposure_days <dbl>, #> # q10_drug_exposure_days <dbl>, q25_drug_exposure_days <dbl>, #> # median_drug_exposure_days <dbl>, q75_drug_exposure_days <dbl>, #> # q90_drug_exposure_days <dbl>, q95_drug_exposure_days <dbl>, #> # maximum_drug_exposure_days <dbl>, result_obscured <lgl>
For further information on the checks performed please see the package vignettes.
After running the checks we can write the CSVs to disk using the writeResultToDisk function.
writeResultToDisk(all_checks, databaseId = "Synthea", outputFolder = tempdir() )
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