Predicting 6-year mortality risk in patients with type 2 diabetes
Brian J Wells, Anil Jain, Susana Arrigain, Changhong Yu, Wayne A Rosenkrans Jr, Michael W Kattan, Brian J Wells, Anil Jain, Susana Arrigain, Changhong Yu, Wayne A Rosenkrans Jr, Michael W Kattan
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.
Figures
References
- Kannel WB, McGee DL: Diabetes and glucose tolerance as risk factors for cardiovascular disease: the Framingham study. Diabetes Care 2:120–126, 1979
- Morgan CL, Currie CJ, Peters JR: Relationship between diabetes and mortality: A population study using record linkage. Diabetes Care 23:1103–1107, 2000
- Morgan CL, Currie CJ, Stott NC, Smithers M, Butler CC, Peters JR: The prevalence of multiple diabetes-related complications. Diabet Med 17:146–151, 2000
- Moss SE, Klein R, Klein BE: Cause-specific mortality in a population-based study of diabetes. Am J Public Health 81:1158–1162, 1991
- Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB: Prediction of coronary heart disease using risk factor categories. Circulation 97:1837-1847, 1998
- Lee ET, Howard BV, Wang W, Welty TK, Galloway JM, Best LG, Fabsitz RR, Zhang Y, Yeh J, Devereux RB: Prediction of coronary heart disease in a population with high prevalence of diabetes and albuminuria: the Strong Heart Study. Circulation 113:2897–2905, 2006
- Stevens RJ, Kothari V, Adler AI, Stratton IM, United Kingdom Prospective Diabetes Study (UKPDS) Group: The UKPDS risk engine: a model for the risk of coronary heart disease in type II diabetes (UKPDS 56). Clin Sci (Lond) 101:671–679, 2001
- Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, De Backer G, De Bacquer D, Ducimetiere P, Jousilahti P, Keil U, Njolstad I, Oganov RG, Thomsen T, Tunstall-Pedoe H, Tverdal A, Wedel H, Whincup P, Wilhelmsen L, Graham IM, SCORE project group: Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24:987–1003, 2003
- Young BA, Lin E, Von Korff M, Simon G, Ciechanowski P, Ludman EJ, Everson-Stewart S, Kinder L, Oliver M, Boyko EJ, Katon WJ: Diabetes complications severity index and risk of mortality, hospitalization, and healthcare utilization. Am J Manag Care 14:15–23, 2008
- Charlson ME, Pompei P, Ales KL, MacKenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383, 1987
- National Kidney Foundation: K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 39:S1–266, 2002
- Institute for Clinical Systems Improvement: Management of type 2 diabetes mellitus.[article online], 2006. Available from . Accessed 23 January 2008
- Gillum RF, Mussolino ME, Madans JH: Coronary heart disease incidence and survival in African-American women and men. the NHANES I epidemiologic follow-up study. Ann Intern Med 127:111–118, 1997
- Van Burren S, Oudshoorn CGM: MICE: multivariate imputation by chained equations: R package version 1.16.2007. Vienna, R Foundation for Statistical Computing, 2007
- R Development Core Team: R: a languate and environment for statistical computing. 2006
- Harrell FE Jr, Lee KL, Mark DB: Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387, 1996
- Harris MI, Eastman RC: Early detection of undiagnosed diabetes mellitus: a US perspective. Diabetes Metab Res Rev 16:230–236, 2000
Source: PubMed