TY - JOUR
T1 - Drug-Induced Acute Myocardial Infarction
T2 - Identifying 'Prime Suspects' from Electronic Healthcare Records-Based Surveillance System
AU - Coloma, Preciosa M.
AU - Schuemie, Martijn J.
AU - Trifirò, Gianluca
AU - Furlong, Laura
AU - van Mulligen, Erik
AU - Bauer-Mehren, Anna
AU - Avillach, Paul
AU - Kors, Jan
AU - Sanz, Ferran
AU - Mestres, Jordi
AU - Oliveira, José Luis
AU - Boyer, Scott
AU - Helgee, Ernst Ahlberg
AU - Molokhia, Mariam
AU - Matthews, Justin
AU - Prieto-Merino, David
AU - Gini, Rosa
AU - Herings, Ron
AU - Mazzaglia, Giampiero
AU - Picelli, Gino
AU - Scotti, Lorenza
AU - Pedersen, Lars
AU - van der Lei, Johan
AU - Sturkenboom, Miriam
PY - 2013/8/28
Y1 - 2013/8/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84883144641&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0072148
DO - 10.1371/journal.pone.0072148
M3 - Article
C2 - 24015213
AN - SCOPUS:84883144641
SN - 1932-6203
VL - 8
JO - PLoS ONE
JF - PLoS ONE
IS - 8
M1 - e72148
ER -