OHDSI Cohort Workflows

In syrona: Stratified Prevalence Comparison Across OMOP CDM Datasets

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Overview

By default, extract_all() extracts data for all persons in the database. But often you want to compare specific subpopulations - patients from a particular hospital, a clinical trial cohort, or a phenotype definition.

Syrona supports OHDSI-standard cohort tables for this. A cohort table has 4 columns:

| Column | Type | Description | |------------------------|---------|-----------------| | cohort_definition_id | INTEGER | Which cohort | | subject_id | INTEGER | person_id | | cohort_start_date | DATE | When they enter | | cohort_end_date | DATE | When they exit |

When you pass a cohort_id to extract_all(), it restricts extraction to cohort members and clips all events to each person's cohort window.

Defining a cohort

Following the OHDSI convention, a cohort is "a set of persons who satisfy one or more inclusion criteria for a duration of time" (The Book of OHDSI, Cohorts). Each person's membership is bounded by a cohort window: cohort_start_date (entry / index date) to cohort_end_date (exit — typically the end of a persistence window or of the observation period).

For Syrona, this window is the key: extract_all(cohort_id = ...) keeps only the cohort's subject_ids and clips each person's records and their observation-period denominator to [cohort_start_date, cohort_end_date]. The same person can therefore contribute different prevalence depending on the window you define — the window is how you scope a comparison in time (for example, restricting a care-site comparison to 2015–2022, or to a fixed follow-up interval after an index event).

Syrona does not author rule-based phenotype definitions itself; it consumes any OHDSI-standard cohort table. You have two options:

1. Use Syrona's built-in care-site generator (below) — the one cohort type Syrona builds itself, populated server-side via INSERT ... SELECT (the SQL approach described in the Book of OHDSI).
2. Bring a cohort from any OHDSI tool — a rule-based definition from ATLAS or Capr, a cohort built with CohortConstructor, a probabilistic PheValuator cohort, or a hand-built data frame (see Cohorts from data frames, below). As long as the result is the standard four-column cohort table, extract_all(cohort_id = ...) can use it.

Care-site cohorts

The most common use case: compare hospitals within the same OMOP CDM database.

library(syrona)

# Connect with write access (needed to create cohort table)
db <- syrona_connect_pg(
  dbname = "omop",
  user = "analyst",
  cdm_schema = "ohdsi_cdm_202511",
  write_schema = "results_analyst"
)

# Find available care sites
list_care_sites(db$con, cdm_schema = "ohdsi_cdm_202511")
#>   care_site_id    care_site_name  n_patients
#> 1          101   Central Hospital      45000
#> 2          205   University Clinic     28000
#> 3          312   Regional Hospital     15000

Create cohorts

# Create a cohort for each hospital
create_caresite_cohort(
  con = db$con,
  care_site_id = 101,
  cohort_id = 1,
  cohort_schema = "results_analyst",
  cdm_schema = "ohdsi_cdm_202511"
)

create_caresite_cohort(
  con = db$con,
  care_site_id = 205,
  cohort_id = 2,
  cohort_schema = "results_analyst",
  cdm_schema = "ohdsi_cdm_202511"
)

Each person's cohort window runs from their first visit start date to their last visit end date at that care site. By default, these are clipped to observation period overlap (restrict_to_observation = TRUE).

Extract by cohort

# Extract each hospital's data
extract_all("Central_Hospital", db = db,
            cohort_id = 1, cohort_schema = "results_analyst")

extract_all("University_Clinic", db = db,
            cohort_id = 2, cohort_schema = "results_analyst")

# Then compare
compare_all("Central_Hospital", "University_Clinic")

Verify a cohort

cohort_summary(db$con, cohort_id = 1, cohort_schema = "results_analyst")
#>   cohort_definition_id n_entries n_persons  min_start    max_end
#> 1                    1     45000     45000 2005-01-03 2023-11-28

Delete a cohort

delete_cohort(db$con, cohort_id = 1, cohort_schema = "results_analyst")

Cohorts from data frames

If you already have cohort membership computed locally (from a CSV, a phenotype algorithm, or another tool), use insert_cohort() to upload it via the modern omopgenerics pathway:

# Build a cohort data frame
cohort_df <- data.frame(
  cohort_definition_id = 1L,
  subject_id = c(1001L, 1002L, 1003L),
  cohort_start_date = as.Date("2015-01-01"),
  cohort_end_date = as.Date("2020-12-31")
)

# Upload to the CDM (requires writeSchema)
db$cdm <- insert_cohort(db$cdm, cohort_df, name = "my_cohort")

# The cohort is now a table in the database, usable by Syrona and other OHDSI tools

Note: insert_cohort() validates that cohort dates fall within each person's observation period, as required by the OHDSI standard.

Care-site cohort options

create_caresite_cohort() has two optional parameters for fine-tuning:

Collapse strategy

# Default: one row per person (first visit start to last visit end)
create_caresite_cohort(..., collapse_strategy = "person_span")

# Alternative: one row per visit (multiple cohort entries per person)
create_caresite_cohort(..., collapse_strategy = "visit_occurrence")

person_span is the default and recommended for Syrona extraction - it produces one cohort window per person, which is the standard expectation.

visit_occurrence keeps each visit as a separate entry. This can be useful for visit-level analyses but means a person may have multiple overlapping windows.

Observation period clipping

# Default: clip to observation period overlap
create_caresite_cohort(..., restrict_to_observation = TRUE)

# Skip clipping (use raw visit dates)
create_caresite_cohort(..., restrict_to_observation = FALSE)

Clipping ensures that the cohort window doesn't extend beyond the person's known observation period. This prevents counting events that happened outside the data capture window.

How cohort filtering works

When extract_all() receives a cohort_id, it calls apply_cohort_filter() which modifies the CDM table references:

• person: filtered to cohort members only
• observation_period: clipped to the intersection of observation period and cohort window (affects the ACHILLES-116 denominator)
• death: restricted to deaths within the cohort window
• condition_occurrence, procedure_occurrence, drug_exposure: events restricted to within the cohort window

This means all downstream extraction functions (denominators, prevalence, chapters, attributes) automatically operate on the filtered population without any changes to their logic.

Compatibility with other OHDSI tools

The cohort table created by Syrona follows the standard OHDSI schema, so it is compatible with:

• CohortDiagnostics - for cohort validation and diagnostics
• FeatureExtraction - for baseline characterization
• CohortConstructor - for matching and cohort operations

# Example: use CohortConstructor to create a matched control cohort
library(CohortConstructor)
db$cdm$matched <- matchCohorts(db$cdm$my_cohort, ratio = 2, name = "matched")

syrona documentation built on Sept. 5, 2026, 1:06 a.m.