First, connect to the database. Examples of how to connect your
database using CDMConnector can be found here: https://darwin-eu.github.io/CDMConnector/articles/a04_DBI_connection_examples.html
Here we use the internal mock database.
In the DrugExposureDiagnostics package, all the diagnostics are
conducted on ingredient level, and if requested, additionally on drug
level. We will use the ingredient “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.
Multiple ingredients can be added in the vector of ´ingredients´.
If the parameter ´outputFolder´ is specified, the results will be saved
to disk. The created outputs (CSV files) will be zipped and saved to a
file ´filename´.zip This zip file serves as the input for the
corresponding Shiny Application. If the parameter ´outputFolder´ is not
specified and you want to save results to disk, it is needed to call
´writeResultToDisk´ (see below). If ´outputFolder´ is not specified,
results are only stored in memory.
all_checks <- executeChecks(cdm,
ingredients = c(1125315),
subsetToConceptId = NULL,
checks = c(
"missing", "exposureDuration", "type", "route", "sourceConcept", "daysSupply",
"verbatimEndDate", "dose", "sig", "quantity", "diagnosticsSummary"
),
minCellCount = 5,
sample = 10000,
tablePrefix = NULL,
earliestStartDate = "2010-01-01",
verbose = FALSE,
byConcept = TRUE,
outputFolder = "output_folder",
filename = "your_database"
)
#> 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 2024-12-22 22:04:35.992205
#>
#> ✔ Summary finished, at 2024-12-22 22:04:36.242458
#> `sample_size` casted to character.
#> `sample_size` eliminated from settings as all elements are NA.The cdm 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. outputFolder is the folder to
write to. If NULL, results will not be written to disk, but just saved
in memory. databaseId is the database identifier. This is
an optional parameter used to create the name of a temporary directory
to store results. fileName is the output file name. If
NULL, it will be set to the databaseId.
The check “missing” outputs the missingValuesOverall if
byConcept = FALSE and additionally
missingValuesbyConcept if
byConcept = TRUE
The check “exposureDuration” outputs the
drugExposureDurationOverall if
byConcept = FALSE and additionally
drugExposureDurationbyConcept if
byConcept = TRUE
The check “type” outputs the drugTypesOverall if
byConcept = FALSE and additionally
drugTypesbyConcept if byConcept = TRUE
The check “route” outputs the drugRoutesOverall if
byConcept = FALSE and additionally
drugRoutesbyConcept if byConcept = TRUE
The check “sourceConcept” outputs the sourceConceptsOverall
(which is actually the drug concept level. It disregards the
byConcept argument because only the drug concept level
makes sense here)
The check “daysSupply” outputs the drugDaysSupplyOverall if
byConcept = FALSE and additionally
drugDaysSupplybyConcept if
byConcept = TRUE
The check “verbatimEndDate” outputs the drugVerbatimEndDate
if byConcept = FALSE and additionally
drugVerbatimEndDatebyConcept if
byConcept = TRUE
The check “dose” outputs the drugDoseOverall (output only
on ingredient level. It disregards the byConcept argument
because only the drug concept level makes sense here)
The check “sig” outputs the drugSig if
byConcept = FALSE and additionally
drugSigbyConcept if byConcept = TRUE
The check “quantity” outputs the drugQuantity if
byConcept = FALSE and additionally
drugQuantitybyConcept if
byConcept = TRUE
The check “diagnosticsSummary” outputs a summary called
diagnosticsSummary
Here, we see the files created:
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 shown, the conceptSummary is not the
output from a check but always output. This table is used within the
function as an input for all the requested checks.
It contains information from the CONCEPT table and DRUG_STRENGTH
table.
The conceptSummary has several use:
It can be quickly seen whether a certain drug is mapped on ingredient
level. Thereby, the drug_concept_id and ingredient_concept_id are
identical.
Furthermore, the conceptSummary can be used to make
decisions about further refine the concept list. For example, though the
argument subsetToConceptId in the
executeChecks() function, the selected concepts can be
requested or eliminated.
The conceptSummary in the case of acetaminophen with
subsetToConceptId = NULL, the following drug concepts are
part of the diagnostics investigation.
| Column | Description |
|---|---|
| drug_concept_id | ID of the drug concept. |
| drug | Name of the drug concept. |
| ingredient_concept_id | Concept ID of ingredient. |
| ingredient | Name of drug ingredient for which the checks have been performed. |
| n_records | Number of records for drug concept. |
| domain_id | From the CONCEPT table. A foreign key to the domain table the drug concept belongs to. |
| vocabulary_id | From the CONCEPT table. A foreign key to the vocabulary table indicating from which source the drug concept has been adapted. |
| concept_class_id | From the CONCEPT table. The concept class of the drug concept (e.g.‘Ingredient’ or ‘Clinical Drug’). |
| standard_concept | From the CONCEPT table. A flag indicating whether the drug concept is standard (‘S’), a classification (‘C’), or a non-standard Source (NULL). |
| concept_code | From the CONCEPT table. The concept code represents the identifier of the Concept in the source vocabulary. |
| valid_start_date | From the CONCEPT table. The date when the drug concept was first recorded |
| valid_end_date | From the CONCEPT table. The date when the drug concept became invalid because it was deleted or superseded (updated) by a new concept. |
| invalid_reason | From the CONCEPT table. Reason the Concept was invalidated. Possible values are D (deleted), U (replaced with an update) or NULL when valid_end_date has the default value. |
| amount_value | From DRUG_STRENGTH table. The numeric value or the amount of active ingredient contained within the drug product. |
| amount_unit_concept_id | From DRUG_STRENGTH table. The Concept representing the Unit of measure for the amount of active ingredient contained within the drug product. |
| numerator_value | From DRUG_STRENGTH table. The concentration of the active ingredient contained within the drug product. |
| numerator_unit_concept_id | From DRUG_STRENGTH table. The Concept representing the Unit of measure for the concentration of active ingredient. |
| numerator_unit | From the CONCEPT table. The concept name associated with the numerator_unit_concept_id. |
| denominator_value | From DRUG_STRENGTH table. The amount of total liquid (or other divisible product, such as ointment, gel, spray, etc.). |
| denominator_unit_concept_id | From DRUG_STRENGTH table. The Concept representing the denominator unit for the concentration of active ingredient. |
| denominator_unit | From the CONCEPT table. The concept name associated with the denominator_unit_concept_id |
| box_size | The number of units of Clinical Branded Drug or Quantified Clinical or Branded Drug contained in a box as dispensed to the patient. |
| amount_unit | From DRUG_STRENGTH table. |
| dose_form | From CONCEPT_RELATIONSHIP table. The RxNorm dose form associated with the drug concept. |
| result_obscured | TRUE if count has been suppressed due to being below the minimum cell count, otherwise FALSE. |
After running the checks, we can write the created outputs (CSV
files) into a zip file to disk using the
writeResultToDisk() function. Note: this is only needed if
the outputFolder argument is not added to
executeChecks! The zip created from using the
writeResultToDisk() function serves as the input for the
corresponding Shiny Application.
Please replace the “your_database” with the name of your database.
Create your designated “output_folder” at your desired location and put
the path if not the working directory.