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

Abstract

OBJECTIVETo 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 METHODSWe defined a retrospective cohort of patients (n = 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 1 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.RESULTSSulfonylurea, 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% CI 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% CI 1.2–3.8).CONCLUSIONSOur 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.

Potential for Electronic Health Records and Online Social Networking to Redefine Medical Research

“…Most notably, observational studies using large-scale provider databases in both the UK (29 ) and Canada (30 ) provided early human-based evidence of the protective effect of metformin therapy as well as the increased risk of insulin and sulfonylureas on the development of cancer. More recently, the use of EHR-based research for surveillance purposes was demonstrated to be a useful adjunct to RCTs and provide signals of potential ADE in a study by one of the coauthors of a head-tohead comparison of myocardial infarction risk among users with rosiglitazone vs metformin (31 ). This study demonstrated that EHR-based ADE signal detection systems could have raised early warnings about the excess risk of myocardial infarction associated with rosiglitazone.…”

Derivation and Validation of Automated Electronic Search Strategies to Identify Pertinent Risk Factors for Postoperative Acute Lung Injury

“…In particular, the availability of high-throughput information technology solutions for extracting clinical data provides an opportunity to improve the efficiency and conduct of clinical and translational research. [10][11][12] However, electronic data extraction is not without limitations. [13][14][15] Validation of data accuracy and completeness is necessary because data can be missing or incorrect as a result of the data-entry process or of corruption during data transmission, storage, or retrieval.…”

Open Data: Can It Prevent Research Fraud, Promote Reproducibility, and Enable Big Data Analytics In Clinical Research?

“…Other industries, such as finance and energy, have embraced data analytics [6], and researchers at Google have shown that an order of magnitude growth in the size of data sets leads to significant improvements in performance of analyses and can overshadow improvements in modeling techniques [7]. Big data have only been reported in a select few epidemiologic studies, such as those linking myocardial infarction and rosiglitazone [8] or rofecoxib [9]. Two significant issues need to be resolved to enable open data mandates: guaranteeing the privacy of study subjects [10] and creating a safe, fair, and open infrastructure for data sharing [4].…”