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 comprehensively searching on all search variables (A) to B) chemical and C) ontological information only.
Results:
In Search A we determined 165,150 patients prescribed cardiovascular drugs(49.2% of cohort), and 317,963 prescribed COPD inhalers (94.7% of cohort). Considering output per value set, Search C missed substantial prescriptions, including vasodilator anti-hypertensives (A and B:19,696 prescriptions; C:1,145) and SAMA inhalers (A and B:35,310; C:564).
Discussion:
We recommend the full methods (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.