The goal of DrugExposureDiagnostics is to summarise ingredient specific drug exposure data in the OMOP CDM.
Installation
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
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 1.0.5, 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 1.0.5, https://github.com/darwin-eu/DrugExposureDiagnostics},
#> url = {https://darwin-eu.github.io/DrugExposureDiagnostics/},
#> }Example use
cdm <- mockDrugExposure()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, min, q05, q25,
#> median, q75, q95, max
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2024-06-12 14:48:26.801546
#>
#> ✔ Summary finished, at 2024-06-12 14:48:26.97046The 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"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: 25
#> Rowwise:
#> $ drug_concept_id <dbl> 40231925, 19133768, 1127433, 1127078, 4022…
#> $ drug <chr> "acetaminophen 325 MG / Hydrocodone Bitart…
#> $ ingredient_concept_id <dbl> 1125315, 1125315, 1125315, 1125315, 112531…
#> $ ingredient <chr> "acetaminophen", "acetaminophen", "acetami…
#> $ n_records <int> 10, 14, 13, 19, 12, 18
#> $ n_patients <int> 9, 13, 11, 13, 11, 15
#> $ 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> "857005", "282464", "1049221", "833036", "…
#> $ 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 <lgl> NA, NA, NA, NA, NA, NA
#> $ amount_value <dbl> NA, NA, 200, NA, 100, 200
#> $ amount_unit_concept_id <dbl> NA, NA, 9655, NA, 9655, 9655
#> $ numerator_value <dbl> 3, 3, NA, 3, NA, NA
#> $ numerator_unit_concept_id <dbl> 8576, 8576, NA, 8576, NA, NA
#> $ numerator_unit <chr> NA, NA, NA, NA, NA, NA
#> $ denominator_value <dbl> 10, 10, NA, 10, NA, NA
#> $ denominator_unit_concept_id <dbl> 8587, 8587, NA, 8587, NA, NA
#> $ denominator_unit <chr> NA, NA, NA, NA, NA, NA
#> $ 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$conceptSummary %>%
select("drug_concept_id", "drug")
#> # A tibble: 6 × 2
#> # Rowwise:
#> drug_concept_id drug
#> <dbl> <chr>
#> 1 40231925 acetaminophen 325 MG / Hydrocodone Bitartrate
#> 2 19133768 acetaminophen 160 MG Oral Tablet
#> 3 1127433 acetaminophen 325 MG / Oxycodone Hydrochloride
#> 4 1127078 acetaminophen 750 MG / Hydrocodone Bitartrate
#> 5 40229134 acetaminophen 21.7 MG/ML / Dextromethorphan
#> 6 40162522 acetaminophen 325 MG Oral TabletOther tibbles then contain information from the various checks performed.
For example, we can see a summary of missingness for the ingredient-related records in the drug exposure table, both overall and by concept.
all_checks$missingValuesOverall
#> # A tibble: 15 × 9
#> # Rowwise: ingredient_concept_id, ingredient
#> ingredient_concept_id ingredient variable n_records n_sample
#> <dbl> <chr> <chr> <int> <dbl>
#> 1 1125315 acetaminophen n_missing_drug_exposu… 44 10000
#> 2 1125315 acetaminophen n_missing_person_id 44 10000
#> 3 1125315 acetaminophen n_missing_drug_concep… 44 10000
#> 4 1125315 acetaminophen n_missing_drug_exposu… 44 10000
#> 5 1125315 acetaminophen n_missing_drug_exposu… 44 10000
#> 6 1125315 acetaminophen n_missing_verbatim_en… 44 10000
#> 7 1125315 acetaminophen n_missing_drug_type_c… 44 10000
#> 8 1125315 acetaminophen n_missing_quantity 44 10000
#> 9 1125315 acetaminophen n_missing_days_supply 44 10000
#> 10 1125315 acetaminophen n_missing_sig 44 10000
#> 11 1125315 acetaminophen n_missing_route_conce… 44 10000
#> 12 1125315 acetaminophen n_missing_drug_source… 44 10000
#> 13 1125315 acetaminophen n_missing_drug_source… 44 10000
#> 14 1125315 acetaminophen n_missing_route_sourc… 44 10000
#> 15 1125315 acetaminophen n_missing_dose_unit_s… 44 10000
#> # 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$missingValuesByConcept
#> # A tibble: 90 × 11
#> # Rowwise: drug_concept_id, drug, ingredient_concept_id, ingredient
#> drug_concept_id drug ingredient_concept_id ingredient variable n_records
#> <dbl> <chr> <dbl> <chr> <chr> <int>
#> 1 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 2 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 3 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 4 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 5 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 6 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 7 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 8 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 9 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> 10 40162522 acetamin… 1125315 acetamino… n_missi… 12
#> # ℹ 80 more rows
#> # ℹ 5 more variables: n_sample <dbl>, n_records_not_missing_value <dbl>,
#> # n_records_missing_value <dbl>, proportion_records_missing_value <dbl>,
#> # result_obscured <lgl>Or we can also see a summary of drug exposure duration (drug_exposure_end_date - drug_exposure_end_date + 1), again overall or by concept.
all_checks$drugExposureDurationOverall
#> # A tibble: 1 × 18
#> # Rowwise: ingredient_concept_id
#> ingredient_concept_id ingredient n_records n_sample n_person
#> <dbl> <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
#> <dbl> <chr> <dbl> <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())