4.7 Article

Rapid Identification of Myocardial Infarction Risk Associated With Diabetes Medications Using Electronic Medical Records

Journal

DIABETES CARE
Volume 33, Issue 3, Pages 526-531

Publisher

AMER DIABETES ASSOC
DOI: 10.2337/dc09-1506

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Funding

  1. National Institutes of Health National Center for Biomedical Computing [5U54-LM-008748]

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OBJECTIVE - To assess the ability to identify potential association(s) of diabetes medications with myocardial infarction using usual care clinical data obtained from the electronic medical record. RESEARCH DESIGN AND METHODS - We defined a retrospective cohort of patients (m = 34,253) treated with a sulfonylurea, metformin, rosiglitazone, or pioglitazone in a single academic health care network. All patients were aged >18 years with at least one prescription for one of the medications between I January 2000 and 31. December 2006. The Study Outcome was acute myocardial infarction requiring hospitalization. We used a cumulative temporal approach to ascertain the calendar date for earliest identifiable risk associated with rosiglitazone compared with that for other therapies. RESULTS - Sulfonylurea, metformin, rosiglitazone, Or pioglitazone therapy was prescribed for 11,200, 12,490, 1,879, and 806 patients, respectively. A total of 1,343 myocardial infarctions were identified. After adjustment for potential myocardial infarction risk factors, the relative risk for myocardial infarction With rosiglitazone was 1.3 (95% Cl 1.1-1.6) compared With sulfonylurea, 2.2 (1.6-3.1) compared With metformin, and 2.2 (1.5-3.4) compared with pioglitazone. Prospective surveillance using these data would have identified increased risk for myocardial infarction With rosiglitazone compared with metformin within 18 months of its introduction with a risk ratio of 2.1 (95% Cl 1.2-3.8). CONCLUSIONS - our results are consistent with a relative adverse cardiovascular risk profile for rosiglitazone. Our use of usual care electronic data sources from a large hospital network represents an innovative approach to rapid safety signal detection that may enable more effective postmarketing drug surveillance.

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