ORCID: https://orcid.org/0000-0002-0273-7984 und Dreischulte, Tobias
ORCID: https://orcid.org/0000-0003-2345-5377
(2025):
Implied ADR-Admissions: A Cohort Study Introducing a Novel Administrative Data Approach for Identifying Drug-Related Hospitalisations.
In: Drug Safety [Forthcoming]
Abstract
Background: Adverse drug reactions (ADRs) are a key contributor to unplanned hospitalisations, particularly in patients with polypharmacy. Traditional detection methods, such as expert reviews or diagnostic coding, are limited in scalability and sensitivity.
Objective: This study introduces and evaluates a novel scalable method, implied ADR-admissions, that links drug exposures to adverse events using administrative data to improve the detection of plausible drug-related hospitalisations.
Methods: A retrospective cohort study was conducted using linked health data from 123,662 individuals aged ≥ 40 years with polypharmacy in two Scottish health boards. Implied ADR-admissions were defined as emergency hospitalisations with one of 15 adverse events plausibly linked to drug exposure (based on a structured consensus process) within the prior 90 days. Incidence was compared with three existing approaches: adverse event-admissions (regardless of drug exposure), explicit ADR-admissions (explicitly coded as ADRs) and preventable ADR-admissions (with prior medication error). Multivariate logistic regression was used to identify predictors of implied ADR-admissions.
Results: Over 1 year, 2.6% experienced an implied ADR-admission, compared with 5.7% with adverse event-admissions, and 0.4% with explicit ADR-admissions. For gastrointestinal bleeding, the implied ADR-admission incidence was 20 times higher than the preventable ADR-admission incidence. Key predictors for implied ADR-admissions included prior hypokalaemia-related hospitalisation and use of potentially inappropriate medications.
Conclusions: The implied ADR-admission approach has improved specificity relative to broad adverse event definitions while enhancing sensitivity beyond methods that rely solely on explicit ADR codes or pre-specified medication errors. It offers a scalable automated tool for pharmacovigilance, though further validation is needed prior to routine use in medication safety monitoring.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Medizin > Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie
Medizin > Klinikum der LMU München > Medizinische Klinik und Poliklinik IV (Endokrinologie, Nephrologie, weitere Sektionen) |
| Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
| ISSN: | 0114-5916 |
| Sprache: | Englisch |
| Dokumenten ID: | 131212 |
| Datum der Veröffentlichung auf Open Access LMU: | 21. Jan. 2026 12:01 |
| Letzte Änderungen: | 21. Jan. 2026 12:01 |
