Graul, Emily L;
Stone, Philip W;
Massen, Georgie M;
Hatam, Sara;
Adamson, Alexander;
Denaxas, Spiros;
Peters, Nicholas S;
(2023)
Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.
JAMIA Open
, 6
(3)
, Article ooad078. 10.1093/jamiaopen/ooad078.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/jamiaopen/ooad078 |
| Publisher version: | https://doi.org/10.1093/jamiaopen/ooad078 |
| Language: | English |
| Additional information: | © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
| Keywords: | code sets, electronic medical records, epidemiology, health data science, misclassification bias, value sets |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10177068 |
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