UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

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). Green open access

[thumbnail of ooad078.pdf]
Preview
Text
ooad078.pdf - Published Version

Download (894kB) | Preview

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
Downloads since deposit
2Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item