Graul EL, Stone PW, Massen GM, Hatam S, Adamson A, Denaxas S, Peters NS, Quint JK. Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.
JAMIA Open 2023;
6:ooad078. [PMID:
37649988 PMCID:
PMC10463548 DOI:
10.1093/jamiaopen/ooad078]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/04/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
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.
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