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

Abstract Objective To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases. Materials and Methods 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. Results 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). Discussion 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. Conclusions Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts. Lay Summary Health research using patient medical records informs everyday clinical practice and involves using collections of clinical codes (codelists) to define a specific diagnosis or prescription. Yet methods to create drug codelists are inconsistent, may not include physician expertise, nor be reported. We developed a reproducible search strategy to create drug codelists, testing it using deidentified healthcare records. We generated codelists for: (1) heart conditions and (2) inhalers to identify prescriptions in a sample group of 335 931 patients with chronic lung disease. We compared our full search strategy (Search A) against 2 restricted searches to show prescriptions can be missed if considerations are not made. In Search A, we identified 165 150 people (49.2% of sample group) prescribed drugs from the heart codelist. For lung inhalers, we identified 317 963 prescriptions (94.7% of group). Search C missed numerous prescriptions for a class of blood pressure lowering drugs (A and B:19