Literature DB >> 22248043

The risk of overall mortality in patients with Type 2 diabetes receiving different combinations of sulfonylureas and metformin: a retrospective analysis.

K M Pantalone1, M W Kattan, C Yu, B J Wells, S Arrigain, B Nutter, A Jain, A Atreja, R S Zimmerman.   

Abstract

AIMS: Sulfonylureas have been shown to increase mortality when used in combination with metformin. This may not be a class effect of sulfonylureas, but rather secondary to differences in properties inherent to the individual sulfonylureas (hypoglycaemic risk, sulfonylurea receptor selectivity and effects on myocardial ischemic preconditioning). The purpose of this study was to assess the risk of overall mortality in patients with Type 2 diabetes treated with different combinations of sulfonylureas and metformin.
METHODS: A retrospective cohort study was conducted using an academic health center enterprise-wide electronic health record system to identify 7320 patients with Type 2 diabetes (3768 initiators of glyburide (glibenclamide) and metformin, 2277 initiators of glipizide and metformin and 1275 initiators of glimepiride and metformin), ≥ 18 years of age and not on insulin or a non-insulin injectable at baseline. The patients were followed for mortality by documentation in the electronic health record and Social Security Death Index. Multivariable Cox models with propensity analysis were used to compare cohorts.
RESULTS: No statistically significant difference in overall mortality risk was observed among the different combinations of sulfonylureas and metformin: glimepiride and metformin vs. glipizide and metformin (HR 1.03; 95% CI 0.89-1.20), glimepiride and metformin vs. glyburide (glibenclamide) and metformin (HR 1.08; 95% CI 0.90-1.30), or with glipizide and metformin vs. glyburide (glibenclamide) and metformin (HR 1.05; 95% CI 0.95-1.15).
CONCLUSIONS: Our results did not identify an increased mortality risk among the different combinations of sulfonylureas and metformin, suggesting that overall mortality is not substantially influenced by the choice of sulfonylurea.
© 2012 The Authors. Diabetic Medicine © 2012 Diabetes UK.

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Year:  2012        PMID: 22248043     DOI: 10.1111/j.1464-5491.2012.03577.x

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  6 in total

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2.  Feature extraction for phenotyping from semantic and knowledge resources.

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Journal:  J Am Med Inform Assoc       Date:  2015-04-29       Impact factor: 4.497

4.  In-Hospital Implementation of Evidence-Based Medications is Associated with Improved Survival in Diabetic Patients with Acute Coronary Syndrome - Data from TSOC ACS-DM Registry.

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5.  Surrogate-assisted feature extraction for high-throughput phenotyping.

Authors:  Sheng Yu; Abhishek Chakrabortty; Katherine P Liao; Tianrun Cai; Ashwin N Ananthakrishnan; Vivian S Gainer; Susanne E Churchill; Peter Szolovits; Shawn N Murphy; Isaac S Kohane; Tianxi Cai
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6.  Strategies for handling missing data in electronic health record derived data.

Authors:  Brian J Wells; Kevin M Chagin; Amy S Nowacki; Michael W Kattan
Journal:  EGEMS (Wash DC)       Date:  2013-12-17
  6 in total

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