Drug-Induced Acute Myocardial Infarction: Identifying 'Prime Suspects' from Electronic Healthcare Records-Based Surveillance System

Preciosa Coloma, Martijn Schuemie, Gianluca Trifiro, L Furlong, Erik van Mulligen, A Bauer-Mehren, Paul Avillach, Jan Kors, F Sanz, J Mestres, JL Oliveira, S Boyer, EA Helgee, M Molokhia, J Matthews, D Prieto-Merino, R Gini, R Herings, G Mazzaglia, G PicelliL Scotti, L Pedersen, Johan Lei, MCJM Sturkenboom

Research output: Contribution to journalArticleAcademicpeer-review

37 Citations (Scopus)
3 Downloads (Pure)

Abstract

Background: Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings. Objective: To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network. Methods: Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations wer Results: Overall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs ('prime suspects'): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate. Limitations: Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out. Conclusion: A strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of 'prime suspects' makes a good starting point for further clinical, laboratory, and epidemiologic investigation.
Original languageUndefined/Unknown
JournalPLoS One (print)
Volume8
Issue number8
DOIs
Publication statusPublished - 2013

Cite this