{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Graul EL"],"funding":["Medical Research Council","NIHR Imperial Biomedical Research Centre","National Institute for Health Research (NIHR)"],"pagination":["ooad078"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10463548"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6(3)"],"pubmed_abstract":["<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."],"journal":["JAMIA open"],"pubmed_title":["Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists."],"pmcid":["PMC10463548"],"funding_grant_id":["COV-LT-0009","MC_PC_20059","HDR-23004"],"pubmed_authors":["Graul EL","Stone PW","Denaxas S","Massen GM","Hatam S","Peters NS","Quint JK","Adamson A"],"additional_accession":[]},"is_claimable":false,"name":"Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.","description":"<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.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Oct","modification":"2026-05-29T05:54:04.955Z","creation":"2025-04-19T06:47:13.843Z"},"accession":"S-EPMC10463548","cross_references":{"pubmed":["37649988"],"doi":["10.1093/jamiaopen/ooad078"]}}