Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-3-1
pubmed:abstractText
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 (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. 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% 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). 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.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-15215798, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-15824549, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-16134080, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-16214598, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-17145742, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-17517853, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-17517854, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-17551159, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-17674425, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-17679700, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-17848652, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-17848653, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-18041104, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-18042753, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-18046025, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-18073359, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-18383443, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-18539917, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-18784090, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-19029503, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-19174434, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-19501900, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-19502645, http://linkedlifedata.com/resource/pubmed/commentcorrection/20009093-9673301
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1935-5548
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
33
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
526-31
pubmed:dateRevised
2011-7-25
pubmed:meshHeading
pubmed-meshheading:20009093-Adverse Drug Reaction Reporting Systems, pubmed-meshheading:20009093-Aged, pubmed-meshheading:20009093-Cohort Studies, pubmed-meshheading:20009093-Diabetes Complications, pubmed-meshheading:20009093-Diabetes Mellitus, pubmed-meshheading:20009093-Drug Monitoring, pubmed-meshheading:20009093-Early Diagnosis, pubmed-meshheading:20009093-Electronic Health Records, pubmed-meshheading:20009093-Female, pubmed-meshheading:20009093-Humans, pubmed-meshheading:20009093-Hypoglycemic Agents, pubmed-meshheading:20009093-Male, pubmed-meshheading:20009093-Mass Screening, pubmed-meshheading:20009093-Middle Aged, pubmed-meshheading:20009093-Myocardial Infarction, pubmed-meshheading:20009093-Retrospective Studies, pubmed-meshheading:20009093-Risk Factors, pubmed-meshheading:20009093-Time Factors
pubmed:year
2010
pubmed:articleTitle
Rapid identification of myocardial infarction risk associated with diabetes medications using electronic medical records.
pubmed:affiliation
Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Boston, Massachusetts, USA. john_brownstein@harvard.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural, Validation Studies