biostudies-literatureGraul ELMedical Research CouncilNIHR Imperial Biomedical Research CentreNational Institute for Health Research (NIHR)ooad078https://www.ebi.ac.uk/biostudies/studies/S-EPMC10463548biostudies-literatureUnknown6(3)<h4>Objective</h4>To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases.<h4>Materials and methods</h4>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 335 931 COPD patients. We compared searching all drug dictionary variables (A) against searching only (B) chemical or (C) ontological variables.<h4>Results</h4>In Search A, we identified 165 150 patients prescribed cardiovascular drugs (49.2% of cohort), and 317 963 prescribed COPD inhalers (94.7% of cohort). Evaluating output per search strategy, Search C missed numerous prescriptions, including vasodilator anti-hypertensives (A and B:19 696 prescriptions; C:1145) and SAMA inhalers (A and B:35 310; C:564).<h4>Discussion</h4>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.<h4>Conclusions</h4>Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts.JAMIA openDetermining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.PMC10463548COV-LT-0009MC_PC_20059HDR-23004Graul ELStone PWDenaxas SMassen GMHatam SPeters NSQuint JKAdamson AfalseDetermining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.<h4>Objective</h4>To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases.<h4>Materials and methods</h4>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 335 931 COPD patients. We compared searching all drug dictionary variables (A) against searching only (B) chemical or (C) ontological variables.<h4>Results</h4>In Search A, we identified 165 150 patients prescribed cardiovascular drugs (49.2% of cohort), and 317 963 prescribed COPD inhalers (94.7% of cohort). Evaluating output per search strategy, Search C missed numerous prescriptions, including vasodilator anti-hypertensives (A and B:19 696 prescriptions; C:1145) and SAMA inhalers (A and B:35 310; C:564).<h4>Discussion</h4>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.<h4>Conclusions</h4>Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts.2023-01-01T00:00:00Z2023 Oct2026-05-29T05:54:04.955Z2025-04-19T06:47:13.843ZS-EPMC104635483764998810.1093/jamiaopen/ooad078