Predicting 6-Year Mortality Risk in Patients With Type 2 Diabetes

Predicting 6-Year Mortality Risk in Patients With Type 2 Diabetes Brian J. Wells , MD 1 , Anil Jain , MD 2 , Susana Arrigain , MA 1 , Changhong Yu , MS 1 , Wayne A. Rosenkrans, Jr. , PHD 3 and Michael W. Kattan , PHD 1 1 Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 2...

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Published inDiabetes care Vol. 31; no. 12; pp. 2301 - 2306
Main Authors WELLS, Brian J, JAIN, Anil, ARRIGAIN, Susana, CHANGHONG YU, ROSENKRANS, Wayne A, KATTAN, Michael W
Format Journal Article
LanguageEnglish
Published Alexandria, VA American Diabetes Association 01.12.2008
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Summary:Predicting 6-Year Mortality Risk in Patients With Type 2 Diabetes Brian J. Wells , MD 1 , Anil Jain , MD 2 , Susana Arrigain , MA 1 , Changhong Yu , MS 1 , Wayne A. Rosenkrans, Jr. , PHD 3 and Michael W. Kattan , PHD 1 1 Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 2 Information Technology, Cleveland Clinic, Cleveland, Ohio 3 Center for Biomedical Innovation, Massachusetts Institute of Technology, Cambridge, Massachusetts, Personalized Medicine Coalition, Washington, DC, and SciTech Strategies, Inc Corresponding author: Michael W. Kattan, kattanm{at}ccf.org Abstract OBJECTIVE —The objective of this study was to create a tool that predicts the risk of mortality in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS —This study was based on a cohort of 33,067 patients with type 2 diabetes identified in the Cleveland Clinic electronic health record (EHR) who were initially prescribed a single oral hypoglycemic agent between 1998 and 2006. Mortality was determined in the EHR and the Social Security Death Index. A Cox proportional hazards regression model was created using medication class and 20 other predictor variables chosen for their association with mortality. A prediction tool was created using the Cox model coefficients. The tool was internally validated using repeated, random subsets of the cohort, which were not used to create the prediction model. RESULTS —Follow-up in the cohort ranged from 1 day to 8.2 years (median 28.6 months), and 3,661 deaths were observed. The prediction tool had a concordance index (i.e., c statistic) of 0.752. CONCLUSIONS —We successfully created a tool that accurately predicts mortality risk in patients with type 2 diabetes. The incorporation of medications into mortality predictions in patients with type 2 diabetes should improve treatment decisions. Footnotes Published ahead of print at http://care.diabetesjournals.org on 22 September 2008. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Accepted August 29, 2008. Received June 11, 2008. DIABETES CARE
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Published ahead of print at http://care.diabetesjournals.org on 22 September 2008.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
ISSN:0149-5992
1935-5548
DOI:10.2337/dc08-1047