Extraction

PostgreSQL database

library(syrona)

# Direct connection
db <- syrona_connect_pg(
  host = "db-server.example.com",
  dbname = "omop",
  user = "analyst",
  cdm_schema = "cdm",
  write_schema = "results_analyst"
)

# Via SSH tunnel (start tunnel first: ssh -L 5432:localhost:5432 user@server)
db <- syrona_connect_pg(
  host = "localhost",
  dbname = "omop",
  user = "analyst",
  cdm_schema = "ohdsi_cdm_202511",
  write_schema = "results_analyst"
)

Credentials. You can pass password = "..." directly, but it is cleaner to omit it and let RPostgres read the password from a ~/.pgpass file or the PGPASSWORD environment variable — for example, set PGPASSWORD=... in your ~/.Renviron. This keeps the password out of your R scripts and command history.

The write_schema parameter tells CDMConnector where it can create temporary tables. This is required for cohort operations and some extraction queries. On PostgreSQL, this is typically a user-specific results schema.

DuckDB (local files)

# Read-only (default) - safe for shared databases
db <- syrona_connect("path/to/omop.duckdb")

# Writable - needed if you want to create cohort tables in the same DB
db <- syrona_connect("path/to/omop.duckdb", read_only = FALSE)

All domains (default)

tables <- extract_all("Dataset_A", db = db)

This extracts conditions, procedures, and drugs. Results are saved to data/sources/Dataset_A/ and returned as a named list.

Single domain

# Conditions only (fastest)
tables <- extract_all("Dataset_A", db = db, domains = "conditions")

# Drugs only
tables <- extract_all("Dataset_A", db = db, domains = "drugs")

# Conditions + procedures (no drugs)
tables <- extract_all("Dataset_A", db = db, domains = c("conditions", "procedures"))

Shorthand: pass a DuckDB path directly

# This connects, extracts, and disconnects automatically
tables <- extract_all("Synthetic", db = "path/to/omop.duckdb")

Denominators (ACHILLES-116)

extract_denominators() computes the number of persons observed per year x sex x age group. This is the denominator for all prevalence calculations. A person is counted in a year if their observation period overlaps that year.

Age groups are 10-year decades (0-9, 10-19, …, 70-79, 80+). Ages above 80 are clamped into a single group for statistical stability.

Condition prevalence (ACHILLES-404)

extract_condition_prevalence() counts persons with at least one condition occurrence per concept x year x sex x age group. Events must fall within the person’s observation period. Only standard SNOMED concepts (concept_id != 0) are included.

Condition chapters

extract_condition_chapters() assigns each condition concept to chapters via three classification systems:

• body_system - SNOMED hierarchy from “Disorder of body system” (L1 + L2)
• disease_category - SNOMED hierarchy from “Disease” (L1 + L2)
• icd10_chapter - ICD-10 chapters via SNOMED-to-ICD-10 mapping (code prefix matching)

A concept can belong to multiple chapters. Concepts without an ICD-10 mapping get an “(Unmapped)” pseudo-chapter.

Drug prevalence

extract_drug_prevalence() rolls up drug exposures to the Ingredient level via concept_ancestor. This means a prescription for “Aspirin 100mg tablet” counts toward the “Aspirin” ingredient. One row per ingredient x year x sex x age group.

Drug chapters

extract_drug_chapters() assigns each ingredient to ATC 1st level chapters (e.g. “A. Alimentary tract and metabolism”) via concept_ancestor.

Procedure chapters

extract_procedure_chapters() assigns procedures to two SNOMED hierarchies:

• by_method - from “Procedure by method” (L1 + L2)
• by_site - from “Procedure by site” (L1 + L2)