Add indications to Drug Utilisation Cohort

Introduction

In this vignette, we demonstrate how to use the addIndications() function to establish a binary indicator between the drug utilisation cohort and another concept-based cohort.

The DrugUtilisation package is designed to work with data in the OMOP CDM format, so our first step is to create a reference to the data using the DBI and CDMConnector packages. The connection to a Postgres database would look like:

library(DrugUtilisation)
library(DBI)
library(duckdb)
library(CDMConnector)
library(dplyr)
library(PatientProfiles)


con <- DBI::dbConnect(duckdb::duckdb(), eunomia_dir())

cdm <- CDMConnector::cdm_from_con(
  con = con,
  cdm_schema = "main",
  write_schema = "main"
)

Create a drug utilisation cohort

We will use Acetaminophen as our example drug to construct our drug utilisation cohort. To begin, we’ll employ the CodelistGenerator package to generate a concept list associated with Acetaminophen.

conceptList <- CodelistGenerator::getDrugIngredientCodes(cdm, "acetaminophen")
conceptList
#> 
#> - 161_acetaminophen (7 codes)

Next, we can create a drug utilisation cohort by using the conceptList with the generateDrugUtilisationCohortSet() function. For a better understanding of the arguments and functionalities of generateDrugUtilisationCohortSet(), please refer to the Use DrugUtilisation to create a cohort vignette.

cdm <- generateDrugUtilisationCohortSet(
  cdm = cdm,
  name = "acetaminophen_users",
  conceptSet = conceptList,
  limit = "All",
  gapEra = 30,
  priorUseWashout = 0
)

Create a indication cohort

Next we going to create our indications cohort to indicate patients with sinusitis and bronchitis. This can be done by using CDMConnector::generateConceptCohortSet().

indications <-
  list(
    sinusitis = c(257012, 4294548, 40481087),
    bronchitis = c(260139, 258780)
  )

cdm <-
  CDMConnector::generateConceptCohortSet(cdm, name = "indications_cohort", indications)

cohortCount(cdm[["indications_cohort"]]) |>
  left_join(
    settings(cdm[["indications_cohort"]]) |>
      select(cohort_definition_id, cohort_name),
    by = "cohort_definition_id"
  )
#> # A tibble: 2 × 4
#>   cohort_definition_id number_records number_subjects cohort_name
#>                  <int>          <int>           <int> <chr>      
#> 1                    1           2688            2688 sinusitis  
#> 2                    2           2546            2546 bronchitis

Add indications with addIndication() function

Then to add indication to the drug utilisation cohort we can simple use the addIndication() function. To do that, we only need the drug utilisation cohort and the indicationCohortName. However, other arguments can be specified using the additional parameters. An example is provided below.


cdm[["acetaminophen_users"]] |>
  addIndication(
    indicationCohortName = "indications_cohort",
    indicationWindow =  list(c(0,0),c(-30,0),c(-365,0)),
    unknownIndicationTable =  c("condition_occurrence")
  )
#> # Source:   table<og_006_1721204179> [?? x 7]
#> # Database: DuckDB v1.0.0 [unknown@Linux 6.5.0-1023-azure:R 4.4.1//tmp/RtmprJVhK8/file1d5668e77d98.duckdb]
#>    cohort_definition_id subject_id cohort_start_date cohort_end_date
#>                   <int>      <int> <date>            <date>         
#>  1                    1       3408 1997-01-13        1997-01-20     
#>  2                    1        114 1999-11-18        1999-12-02     
#>  3                    1       2945 1964-11-05        1964-11-12     
#>  4                    1       4370 1968-08-30        1968-09-13     
#>  5                    1        712 2011-07-27        2011-08-03     
#>  6                    1       5274 1953-09-29        1953-10-06     
#>  7                    1        565 1960-12-07        1960-12-14     
#>  8                    1       2380 1950-06-28        1950-07-12     
#>  9                    1         99 1980-11-30        1980-12-14     
#> 10                    1        219 1968-08-28        1968-09-11     
#> # ℹ more rows
#> # ℹ 3 more variables: indication_0_to_0 <chr>, indication_m30_to_0 <chr>,
#> #   indication_m365_to_0 <chr>

Use indicationGap to specify the indication gaps, which are defined as the gap between the event and the indication. Additionally, you can use unknownIndicationTable to specify the tables to look for unknown indication.

Summarise indications with summariseIndication()

To create a summary table of the indications cohort, you can use the summariseIndication() function.

# cdm[["acetaminophen_users"]] |>
#   addIndication(
#     cdm = cdm, 
#     indicationCohortName = "indications_cohort",
#     indicationGap =  c(0, 30, 365),
#     unknownIndicationTable =  c("condition_occurrence")
#   ) |>
#   summariseIndication(cdm) |>
#   select("variable_name", "estimate_name", "estimate_value")

You can also summarise the indications by using the strata argument in the summariseIndication() function. In the example below, it is summarized by ageGroup and sex.


# cdm[["acetaminophen_users"]] |>
#   addDemographics(ageGroup = list(c(0, 19), c(20, 150))) |>
#   addIndication(
#     cdm = cdm,
#     indicationCohortName = "indications_cohort",
#     indicationGap =  c(0),
#     unknownIndicationTable =  c("condition_occurrence")
#   ) |>
#   summariseIndication(
#     cdm,
#     strata = list("age" = "age_group", "sex" = "sex")) |>
#       select("variable_name", "estimate_name", "estimate_value","strata_name")

Marti Catala, Mike Du, Yuchen Guo, Kim Lopez-Guell, Edward Burn, Xintong Li

2024-07-17

Introduction

Create a drug utilisation cohort

Create a indication cohort

Add indications with addIndication() function

Summarise indications with summariseIndication()