Introduction

Cohorts are a fundamental building block for studies that use the OMOP CDM, identifying people who satisfy one or more inclusion criteria for a duration of time based on their clinical records. Currently cohorts are typically built using CIRCE which allows complex cohorts to be represented using JSON. This JSON is then converted to SQL for execution against a database containing data mapped to the OMOP CDM. CIRCE JSON can be created via the ATLAS GUI or programmatically via the Capr R package. However, although a powerful tool for expressing and operationalising cohort definitions, the SQL generated can be cumbersome especially for complex cohort definitions, moreover cohorts are instantiated independently, leading to duplicated work.

The CohortConstructor package offers an alternative approach, emphasising cohort building in a pipeline format. It first creates base cohorts and then applies specific inclusion criteria. Unlike the “by definition” approach, where cohorts are built independently, CohortConstructor follows a “by domain/ table” approach, which minimises redundant queries to large OMOP tables. More details on this approach can be found in the Introduction vignette.

To test the performance of the package there is a benchmarking function which uses nine phenotypes from the OHDSI Phenotype library that cover a range of concept domains, entry and inclusion criteria, and cohort exit options. We replicated these cohorts using CohortConstructor to assess computational time and agreement between CIRCE and CohortConstructor.

library(CDMConnector)
library(CodelistGenerator)
library(PatientProfiles)
library(CohortConstructor)
library(dplyr)

con <- DBI::dbConnect(duckdb::duckdb(), 
                      dbdir = eunomiaDir())
cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main", 
                  writePrefix = "my_study_")

Once we have created our cdm reference we can run the benchmark. Once run we’ll have a set of results with the time taken to run the different tasks. For this example we will just run task of creating all the cohorts at once using CohortConstructor.

benchmark_results <- benchmarkCohortConstructor(cdm,
               runCIRCE = FALSE,
               runCohortConstructorDefinition = FALSE,
               runCohortConstructorDomain = TRUE)
benchmark_results |> 
  glimpse()

Cohorts

We replicated the following cohorts from the OHDSI phenotype library: COVID-19 (ID 56), inpatient hospitalisation (23), new users of beta blockers nested in essential hypertension (1049), transverse myelitis (63), major non cardiac surgery (1289), asthma without COPD (27), endometriosis procedure (722), new fluoroquinolone users (1043), acquired neutropenia or unspecified leukopenia (213).

The COVID-19 cohort was used to evaluate the performance of common cohort stratifications. To compare the package with CIRCE, we created definitions in Atlas, stratified by age groups and sex, which are available in the benchmark GitHub repository with the benchmark code.

Performance

To evaluate CohortConstructor performance we generated each of the CIRCE cohorts using functionalities provided by both CodelistGenerator and CohortConstructor, and measured the computational time taken.

Two different approaches with CohortConstructor were tested:

• By definition: we created each of the cohorts seprately.

• By domain: All nine targeted cohorts were created together in a set, following the by domain approach described in the Introduction vignette. Briefly, this approach involves creating all base cohorts at once, requiring only one call to each involved OMOP table.