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


Coloma, PreciosaM; Schuemie, MartijnJ; Trifiro, Gianluca; Furlong, Laura; van Mulligen, Erik; Bauer-Mehren, Anna; Avillach, Paul; Kors, Jan; Sanz, Ferran; Mestres, Jordi; Oliveira, JoseLuis; Boyer, Scott; Helgee, ErnstAhlberg; Molokhia, Mariam; Matthews, Justin; Prieto-Merino, David; Gini, Rosa; Herings, Ron; Mazzaglia, Giampiero; Picelli, Gino; Scotti, Lorenza; Pedersen, Lars; van der Lei, Johan; Sturkenboom, Miriam; Consortium, Eu-, Adr; (2013) Drug-Induced Acute Myocardial Infarction: Identifying 'Prime Suspects' from Electronic Healthcare Records-Based Surveillance System. PLoS One, 8 (8). ISSN 1932-6203 DOI: https://doi.org/10.1371/journal.pone.0072148

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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 were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible. 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.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
PubMed ID: 24015213
Web of Science ID: 323733800032
URI: http://researchonline.lshtm.ac.uk/id/eprint/1300588

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