Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.

Abstract

To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases. We developed methods to generate drug codelists and tested this using the Clinical Practice Research Datalink (CPRD) Aurum database, accounting for missing data in the database. We generated codelists for: (1) cardiovascular disease and (2) inhaled Chronic Obstructive Pulmonary Disease (COPD) therapies, applying them to a sample cohort of 335931 COPD patients. We compared searching all drug dictionary variables (A) against searching only (B) chemical or (C) ontological variables. In Search A, we identified 165150 patients prescribed cardiovascular drugs (49.2% of cohort), and 317963 prescribed COPD inhalers (94.7% of cohort). Evaluating output per search strategy, Search C missed numerous prescriptions, including vasodilator anti-hypertensives (A and B:19696 prescriptions; C:1145) and SAMA inhalers (A and B:35310; C:564). We recommend the full search (A) for comprehensiveness. There are special considerations when generating adaptable and generalizable drug codelists, including fluctuating status, cohort-specific drug indications, underlying hierarchical ontology, and statistical analyses. Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts.