Introduction To DrugExposureDiagnostics


First, connect to the database.

cdm <- getEunomiaCdm()

Check for single ingredient.

In the DrugExposureDiagnostics package, all the diagnostics are conducted on ingredient level. We will use “acetaminophen” as an example. Here is a brief look at this ingredient.

Property Value
Concept Name acetaminophen
Domain ID Drug
Concept Class ID Ingredient
Vocabulary ID RxNorm
Concept ID 1125315
Concept code 161
Validity Valid
Concept Standard
Valid start 01-Jan-1970
Valid end 31-Dec-2099

We can run all available checks at the same time using the ´executeChecks()´ function. This will return a list which contains the results of each check.

all_checks <- executeChecks(cdm,
                            ingredients = c(1125315),
                            subsetToConceptId = NULL,
                            checks = c("missing", "exposureDuration", "type", "route", "sourceConcept", "daysSupply", "verbatimEndDate", 
                                       "dose", "sig", "quantity", "histogram", "diagnosticsSummary"), 
                            minCellCount = 5,
                            sample = 10000,
                            tablePrefix = NULL,
                            earliestStartDate = "2010-01-01",
                            verbose = FALSE,
                            byConcept = TRUE)
#> population after earliestStartDate smaller than sample
#> Joining with `by = join_by(ingredient_concept_id)`
#> Joining with `by = join_by(ingredient_concept_id)`

Thecdm is the database reference of the OMOP CDM using the CDMConnector package.
The ingredients is a list of ingredients of interests, by default it is 1125315 for acetaminophen. The subsetToConceptId vector of concept IDs of the ingredients to filter. If a concept ID is positive it will be included, a negative one will be excluded. If NULL, all concept IDs for an ingredient will be considered. checks allows to select the checks to be executed, by default the missing values, the exposure duration and the quantity checks will be run. The minCellCount is minimum number of events to report, numbers lower than this will be obscured. sample is the number of samples, by default, 10.000 drug record samples will be used.
The tablePrefix is an optional value for database tables that will be created during executeChecks. earliestStartDate is the earliest data from which drug records will be included. verbose is a parameter that enables the printing of messages to the console. byConcept is a boolean that determines if only overall results should be returned or also by drug concept.

We can then check what results available from ´executeChecks()´ by

#>  [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"         "drugDaysSupplyHistogram"      
#> [23] "drugQuantityHistogram"         "drugDurationHistogram"        
#> [25] "diagnosticsSummary"

Let’s take a look at the results. conceptSummary contains information on the concept ids that are used in the database for a given ingredient. So in the case of acetaminophen, the following drugs contain the acetaminophen as an ingredient.

  rownames = FALSE

After running the checks, we can write the CSV files into a zip file to disk using the writeResultToDisk() function.

                  databaseId = "your_database_id", 
                  outputFolder = "output_folder")