# CohortConstructor benchmarking results

# 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, emphasizing cohort building in a pipeline format. It first creates base cohorts and then applies specific inclusion criteria. Unlike the “by definition” approach, where::here cohorts are built independently, CohortConstructor follows a “by domain” approach, which minimizes redundant queries to large OMOP tables. More details on this approach can be found in the Introduction vignette.

We benchmarked this package using 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 CodelistGenerator and CohortConstructor to assess computational time and agreement between CIRCE and CohortConstructor.

## Code and collaboration

The benchmarking code is available on the BenchmarkCohortConstructor repository on GitHub.

If you are interested in running the code on your database, feel free to reach out to us for assistance, and we can also update the vignette with your results! :)

The benchmark script was executed against the following four databases:

- CPRD Gold: A primary care database from the UK, capturing data mostly from Northern Ireland, Wales, and Scotland clinics. The benchmark utilized a 100,000-person sample from this dataset, which is managed using PostgreSQL.

- CPRD Aurum: Another UK primary care database, primarily covering clinics in England. This database is managed on SQL Server.

- Coriva: A sample of approximately 400,000 patients from the Estonia National Health Insurance database, managed on PostgreSQL.

- OHDSI SQL Server: A mock OMOP CDM dataset provided by OHDSI, hosted on SQL Server.

The table below presents the number of records in the OMOP tables used in the benchmark script for each of the participating databases.

OMOP table | Database
--- | ---
CPRD Aurum | CORIVA-Estonia | CPRD Gold 100k | OHDSI Postgres server | OHDSI redshift | OHDSI snowflake | OHDSI SQL server
person | 47,193,158 | 438,433 | 100,000 | 1,000 | 1,000 | 116,352 | 1,000
observation_period | 47,193,158 | 438,433 | 100,000 | 1,048 | 1,000 | 104,891 | 1,048
drug_exposure | 3,256,609,138 | 31,265,445 | 12,403,195 | 49,542 | 57,095 | 6,303,388 | 49,542
condition_occurrence | 2,110,992,846 | 40,957,155 | 3,191,739 | 160,322 | 147,186 | 14,455,993 | 160,322
procedure_occurrence | 2,267,113,392 | 14,545,615 | 1,914,271 | 62,189 | 137,522 | 13,926,771 | 62,189
visit_occurrence | 7,091,248,835 | 38,037,330 | 9,183,206 | 47,457 | 55,261 | 5,579,542 | 47,457
measurement | 8,255,241,316 | 39,378,570 | 10,913,588 | 2,858 | 34,556 | 3,704,839 | 2,858
observation | 16,425,069,199 | 37,010,044 | 11,107,039 | 13,481 | 19,339 | 1,876,834 | 13,481

# 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.

## Cohort counts and overlap

The following table displays the number of records and subjects for each cohort across the participating databases:

| Tool
--- | ---
Cohort name | CIRCE | CohortConstructor
Number records | Number subjects | Number records | Number subjects
CPRD Aurum
Acquired neutropenia or unspecified leukopenia | 1,429,966 | 632,966 | 1,302,498 | 633,030
Asthma without COPD | 4,009,925 | 4,009,925 | 3,934,106 | 3,934,106
COVID-19 | 5,600,429 | 4,452,410 | 6,206,907 | 4,452,196
COVID-19: female | 3,111,643 | 2,434,062 | 3,452,138 | 2,438,759
COVID-19: female, 0 to 50 | 2,172,113 | 1,730,180 | 2,382,039 | 1,730,116
COVID-19: female, 51 to 150 | 939,818 | 708,838 | 1,070,099 | 708,643
COVID-19: male | 2,488,786 | 2,018,348 | 2,754,769 | 2,020,625
COVID-19: male, 0 to 50 | 1,709,375 | 1,422,999 | 1,862,219 | 1,422,962
COVID-19: male, 51 to 150 | 779,629 | 597,804 | 892,550 | 597,663
Endometriosis procedure | 139 | 108 | 77 | 77
Inpatient hospitalisation | 0 | 0 | 0 | 0
Major non cardiac surgery | 1,932,745 | 1,932,745 | 1,932,745 | 1,932,745
New fluoroquinolone users | 1,765,274 | 1,765,274 | 1,817,439 | 1,817,439
New users of beta blockers nested in essential hypertension | 98,592 | 98,592 | 102,589 | 102,589
Transverse myelitis | 11,930 | 4,040 | 5,818 | 4,119
CORIVA-Estonia
Acquired neutropenia or unspecified leukopenia | 2,231 | 634 | 2,188 | 634
Asthma without COPD | 25,867 | 25,867 | 25,867 | 25,867
COVID-19 | 421,053 | 193,435 | 435,059 | 193,435
COVID-19: female | 235,740 | 105,849 | 243,773 | 106,322
COVID-19: female, 0 to 50 | 150,121 | 69,168 | 155,256 | 69,168
COVID-19: female, 51 to 150 | 85,620 | 37,154 | 88,517 | 37,154
COVID-19: male | 185,313 | 87,586 | 191,286 | 87,891
COVID-19: male, 0 to 50 | 130,252 | 63,558 | 134,415 | 63,558
COVID-19: male, 51 to 150 | 55,062 | 24,333 | 56,871 | 24,333
Endometriosis procedure | 0 | 0 | 0 | 0
Inpatient hospitalisation | 267,010 | 133,705 | 267,010 | 133,705
Major non cardiac surgery | 4,025 | 4,025 | 4,025 | 4,025
New fluoroquinolone users | 39,712 | 39,712 | 39,712 | 39,712
New users of beta blockers nested in essential hypertension | 18,967 | 18,967 | 18,967 | 18,967
Transverse myelitis | 27 | 10 | 12 | 10
CPRD Gold 100k
Acquired neutropenia or unspecified leukopenia | 2,719 | 1,167 | 2,675 | 1,167
Asthma without COPD | 8,808 | 8,808 | 8,741 | 8,741
COVID-19 | 3,231 | 2,881 | 3,275 | 2,881
COVID-19: female | 1,748 | 1,543 | 1,771 | 1,543
COVID-19: female, 0 to 50 | 1,271 | 1,125 | 1,291 | 1,125
COVID-19: female, 51 to 150 | 477 | 418 | 480 | 418
COVID-19: male | 1,483 | 1,338 | 1,504 | 1,341
COVID-19: male, 0 to 50 | 1,054 | 960 | 1,072 | 960
COVID-19: male, 51 to 150 | 429 | 381 | 432 | 381
Endometriosis procedure | 0 | 0 | 0 | 0
Inpatient hospitalisation | 0 | 0 | 0 | 0
Major non cardiac surgery | 4,146 | 4,146 | 4,146 | 4,146
New fluoroquinolone users | 5,412 | 5,412 | 5,412 | 5,412
New users of beta blockers nested in essential hypertension | 1,723 | 1,723 | 1,723 | 1,723
Transverse myelitis | 31 | 11 | 15 | 11
OHDSI Postgres server
Acquired neutropenia or unspecified leukopenia | 151 | 86 | 106 | 86
Asthma without COPD | 126 | 126 | 126 | 126
COVID-19 | 0 | 0 | 0 | 0
COVID-19: female | 0 | 0 | 0 | 0
COVID-19: female, 0 to 50 | 0 | 0 | 0 | 0
COVID-19: female, 51 to 150 | 0 | 0 | 0 | 0
COVID-19: male | 0 | 0 | 0 | 0
COVID-19: male, 0 to 50 | 0 | 0 | 0 | 0
COVID-19: male, 51 to 150 | 0 | 0 | 0 | 0
Endometriosis procedure | 0 | 0 | 0 | 0
Inpatient hospitalisation | 522 | 321 | 522 | 321
Major non cardiac surgery | 88 | 88 | 92 | 92
New fluoroquinolone users | 145 | 145 | 145 | 145
New users of beta blockers nested in essential hypertension | 112 | 112 | 112 | 112
Transverse myelitis | 0 | 0 | 0 | 0
OHDSI redshift
Acquired neutropenia or unspecified leukopenia | 155 | 88 | 108 | 88
Asthma without COPD | 228 | 228 | 228 | 228
COVID-19 | 0 | 0 | 0 | 0
COVID-19: female | 0 | 0 | 0 | 0
COVID-19: female, 0 to 50 | 0 | 0 | 0 | 0
COVID-19: female, 51 to 150 | 0 | 0 | 0 | 0
COVID-19: male | 0 | 0 | 0 | 0
COVID-19: male, 0 to 50 | 0 | 0 | 0 | 0
COVID-19: male, 51 to 150 | 0 | 0 | 0 | 0
Endometriosis procedure | 0 | 0 | 0 | 0
Inpatient hospitalisation | 612 | 365 | 612 | 365
Major non cardiac surgery | 616 | 616 | 613 | 613
New fluoroquinolone users | 109 | 109 | 109 | 109
New users of beta blockers nested in essential hypertension | 25 | 25 | 25 | 25
Transverse myelitis | - | - | - | -
OHDSI snowflake
Acquired neutropenia or unspecified leukopenia | 13,960 | 8,525 | 10,147 | 8,525
Asthma without COPD | 24,288 | 24,288 | 24,291 | 24,291
COVID-19 | 0 | 0 | 0 | 0
COVID-19: female | 0 | 0 | 0 | 0
COVID-19: female, 0 to 50 | 0 | 0 | 0 | 0
COVID-19: female, 51 to 150 | 0 | 0 | 0 | 0
COVID-19: male | 0 | 0 | 0 | 0
COVID-19: male, 0 to 50 | 0 | 0 | 0 | 0
COVID-19: male, 51 to 150 | 0 | 0 | 0 | 0
Endometriosis procedure | - | - | 0 | 0
Inpatient hospitalisation | 64,275 | 37,780 | 64,275 | 37,780
Major non cardiac surgery | 66,171 | 66,171 | 66,034 | 66,034
New fluoroquinolone users | 14,203 | 14,203 | 13,398 | 13,398
New users of beta blockers nested in essential hypertension | 2,022 | 2,022 | 2,028 | 2,028
Transverse myelitis | 102 | 43 | 42 | 42
OHDSI SQL server
Acquired neutropenia or unspecified leukopenia | 151 | 86 | 106 | 86
Asthma without COPD | 126 | 126 | 126 | 126
COVID-19 | 0 | 0 | 0 | 0
COVID-19: female | 0 | 0 | 0 | 0
COVID-19: female, 0 to 50 | 0 | 0 | 0 | 0
COVID-19: female, 51 to 150 | 0 | 0 | 0 | 0
COVID-19: male | 0 | 0 | 0 | 0
COVID-19: male, 0 to 50 | 0 | 0 | 0 | 0
COVID-19: male, 51 to 150 | 0 | 0 | 0 | 0
Endometriosis procedure | 0 | 0 | 0 | 0
Inpatient hospitalisation | 522 | 321 | 522 | 321
Major non cardiac surgery | 88 | 88 | 92 | 92
New fluoroquinolone users | 145 | 145 | 145 | 145
New users of beta blockers nested in essential hypertension | 112 | 112 | 112 | 112
Transverse myelitis | 0 | 0 | 0 | 0

We also computed the overlap between patients in CIRCE and CohortConstructor cohorts, with results shown in the plot below:

# 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.

## By definition

The following plot shows the times taken to create each cohort using CIRCE and CohortConstructor when each cohorts were created separately.

## By domain

The table below depicts the total time it took to create the nine cohorts when using the by domain approach for CohortConstructor.

Database_name | Time (minutes)
--- | ---
CIRCE | CohortConstructor
CORIVA-Estonia | 9.51 | 9.95
CPRD Aurum | 3,288.11 | 109.08
CPRD Gold 100k | 73.41 | 7.85
OHDSI Postgres server | 4.32 | 29.20
OHDSI SQL server | 2.89 | 18.56
OHDSI redshift | 5.44 | 34.05
OHDSI snowflake | 11.40 | 84.56

## Cohort stratification

Cohorts are often stratified in studies. With Atlas cohort definitions, each stratum requires a new CIRCE JSON to be instantiated, while CohortConstructor allows stratifications to be generated from an overall cohort. The following table shows the time taken to create age and sex stratifications for the COVID-19 cohort with both CIRCE and CohortConstructor.

Database | Time (minutes)
--- | ---
CIRCE | CohortConstructor
CORIVA-Estonia | 14.38 | 23.51
CPRD Aurum | 3,300.18 | 241.81
CPRD Gold 100k | 166.66 | 19.52
OHDSI Postgres server | 6.75 | 73.24
OHDSI SQL server | 4.56 | 46.64
OHDSI redshift | 8.32 | 84.79
OHDSI snowflake | 17.04 | 202.95

## Use of SQL indexes

For Postgres SQL databases, the package uses indexes in `conceptCohort` by default. To evaluate how much these indexes reduce computation time, we instantiated a subset of concept sets from the benchmark, both with and without indexes.

Four calls were made to `conceptCohort`, each involving a different number of OMOP tables. The combinations were:

1. Drug exposure

2. Drug exposure + condition occurrence

3. Drug exposure + condition occurrence + procedure occurrence

4. Drug exposure + condition occurrence + procedure occurrence + measurement

The plot below shows the computation time with and without SQL indexes for each scenario: