The goal of DrugExposureDiagnostics is to summarise ingredient specific drug exposure data in the OMOP CDM.
You can install the development version of DrugExposureDiagnostics like this:
install.packages("remotes")
remotes::install_github("darwin-eu-dev/DrugExposureDiagnostics")In order to use the example data from the eunomia dataset, you need to set an environment variable. You can do it like this:
{r} usethis::edit_r_environ()library(DrugExposureDiagnostics)
library(CDMConnector)
library(dplyr)cdm <- getEunomiaCdm()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, 1125315, minCellCount = NULL)
#> population after earliestStartDate smaller than sample, ignoring date for samplingThe 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] "drugSourceConceptsByConcept" "drugDaysSupply"
#> [13] "drugDaysSupplyByConcept" "drugVerbatimEndDate"
#> [15] "drugVerbatimEndDateByConcept" "drugDose"
#> [17] "drugDoseByConcept" "drugSig"
#> [19] "drugSigByConcept" "drugQuantity"
#> [21] "drugQuantityByConcept" "drugIngredientOverview"
#> [23] "drugIngredientPresence" "drugDaysSupplyHistogram"
#> [25] "drugQuantityHistogram" "drugDurationHistogram"
#> [27] "diagnostics_summary"The first item contains information on the concept ids that are used in the database for a given ingredient.
glimpse(all_checks$conceptSummary)
#> Rows: 2
#> Columns: 24
#> $ drug_concept_id <dbl> 40162522, 1127078
#> $ drug <chr> "Acetaminophen 325 MG / Hydrocodone Bitart…
#> $ ingredient_concept_id <dbl> 1125315, 1125315
#> $ ingredient <chr> "Acetaminophen", "Acetaminophen"
#> $ n_records <dbl> 312, 2158
#> $ domain_id <chr> "Drug", "Drug"
#> $ vocabulary_id <chr> "RxNorm", "RxNorm"
#> $ concept_class_id <chr> "Clinical Drug", "Clinical Drug"
#> $ standard_concept <chr> "S", "S"
#> $ concept_code <chr> "857005", "282464"
#> $ valid_start_date <date> 2009-08-02, 1970-01-01
#> $ valid_end_date <date> 2099-12-31, 2099-12-31
#> $ invalid_reason <lgl> NA, NA
#> $ amount_value <dbl> 1, 1
#> $ amount_unit_concept_id <dbl> 1, 1
#> $ numerator_value <dbl> 1, 1
#> $ numerator_unit_concept_id <dbl> 1, 1
#> $ numerator_unit <chr> NA, NA
#> $ denominator_value <dbl> 1, 1
#> $ denominator_unit_concept_id <dbl> 1, 1
#> $ denominator_unit <chr> NA, NA
#> $ box_size <int> NA, NA
#> $ amount_unit <chr> NA, NA
#> $ dose_form <chr> NA, NA
all_checks$conceptSummary %>%
select("drug_concept_id", "drug")
#> # A tibble: 2 × 2
#> drug_concept_id drug
#> <dbl> <chr>
#> 1 40162522 Acetaminophen 325 MG / Hydrocodone Bitartrate 7.5 MG Oral Tab…
#> 2 1127078 Acetaminophen 160 MG Oral TabletOther tibbles then contain information from the various checks performed.
For example, we can see a summary of missingness for the ingredient-related records in the drug exposure table, both overall and by concept.
all_checks$missingValuesOverall
#> # A tibble: 18 × 7
#> # Groups: ingredient_concept_id, ingredient [1]
#> ingredient_concept_id ingredient variable n_rec…¹ n_rec…² n_rec…³ propo…⁴
#> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 2 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 3 1125315 Acetaminophen n_missin… 2470 2158 312 0.126
#> 4 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 5 1125315 Acetaminophen n_missin… 2470 0 2470 1
#> 6 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 7 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 8 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 9 1125315 Acetaminophen n_missin… 2470 0 2470 1
#> 10 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 11 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 12 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 13 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 14 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 15 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 16 1125315 Acetaminophen n_missin… 2470 2470 0 0
#> 17 1125315 Acetaminophen n_missin… 2470 0 2470 1
#> 18 1125315 Acetaminophen n_missin… 2470 0 2470 1
#> # … with abbreviated variable names ¹n_records, ²n_records_not_missing_value,
#> # ³n_records_missing_value, ⁴proportion_records_missing_value
all_checks$missingValuesByConcept
#> # A tibble: 36 × 9
#> # Groups: drug_concept_id, drug, ingredient_concept_id, ingredient [2]
#> drug_concept_id drug ingre…¹ ingre…² varia…³ n_rec…⁴ n_rec…⁵ n_rec…⁶ propo…⁷
#> <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1127078 Acet… 1125315 Acetam… n_miss… 2158 2158 0 0
#> 2 1127078 Acet… 1125315 Acetam… n_miss… 2158 2158 0 0
#> 3 1127078 Acet… 1125315 Acetam… n_miss… 2158 2158 0 0
#> 4 1127078 Acet… 1125315 Acetam… n_miss… 2158 2158 0 0
#> 5 1127078 Acet… 1125315 Acetam… n_miss… 2158 0 2158 1
#> 6 1127078 Acet… 1125315 Acetam… n_miss… 2158 2158 0 0
#> 7 1127078 Acet… 1125315 Acetam… n_miss… 2158 2158 0 0
#> 8 1127078 Acet… 1125315 Acetam… n_miss… 2158 2158 0 0
#> 9 1127078 Acet… 1125315 Acetam… n_miss… 2158 0 2158 1
#> 10 1127078 Acet… 1125315 Acetam… n_miss… 2158 2158 0 0
#> # … with 26 more rows, and abbreviated variable names ¹ingredient_concept_id,
#> # ²ingredient, ³variable, ⁴n_records, ⁵n_records_not_missing_value,
#> # ⁶n_records_missing_value, ⁷proportion_records_missing_valueOr we can also see a summary of drug exposure duration (drug_exposure_end_date - drug_exposure_end_date + 1), again overall or by concept.
all_checks$drugExposureDurationOverall
#> # A tibble: 1 × 15
#> # Groups: ingredient_concept_id [1]
#> ingredient_c…¹ ingre…² n_rec…³ n_non…⁴ n_neg…⁵ propo…⁶ minim…⁷ q05_d…⁸ q10_d…⁹
#> <dbl> <chr> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1125315 Acetam… 2470 2470 0 0 1 1 1
#> # … with 6 more variables: 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>, and abbreviated variable names
#> # ¹ingredient_concept_id, ²ingredient, ³n_records, ⁴n_non_negative_days,
#> # ⁵n_negative_days, ⁶proportion_negative_days, ⁷minimum_drug_exposure_days,
#> # ⁸q05_drug_exposure_days, ⁹q10_drug_exposure_days
all_checks$drugExposureDurationByConcept
#> # A tibble: 2 × 17
#> # Groups: drug_concept_id, drug, ingredient_concept_id [2]
#> drug_c…¹ drug ingre…² ingre…³ n_rec…⁴ n_non…⁵ n_neg…⁶ propo…⁷ minim…⁸ q05_d…⁹
#> <dbl> <chr> <dbl> <chr> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 1127078 Acet… 1125315 Acetam… 2158 2158 0 0 14 15
#> 2 40162522 Acet… 1125315 Acetam… 312 312 0 0 1 1
#> # … with 7 more variables: 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>, and
#> # abbreviated variable names ¹drug_concept_id, ²ingredient_concept_id,
#> # ³ingredient, ⁴n_records, ⁵n_non_negative_days, ⁶n_negative_days,
#> # ⁷proportion_negative_days, ⁸minimum_drug_exposure_days, …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())