Sanjay Basu1, Jeremy B Sussman2, Seth A Berkowitz3, Rodney A Hayward2, John S Yudkin4. 1. Center for Population Health Sciences, Center for Primary Care and Outcomes Research, and Departments of Medicine and of Health Research and Policy, Stanford University, Palo Alto, CA, USA; Center for Primary Care, Massachusetts General Hospital, Boston, MA, USA. Electronic address: basus@stanford.edu. 2. Division of General Medicine, University of Michigan, and Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare, Ann Arbor, MI, USA. 3. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, and Division of General Internal Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. 4. Institute of Cardiovascular Science, Division of Medicine, University College London, London, UK.
Abstract
BACKGROUND: In view of substantial mis-estimation of risks of diabetes complications using existing equations, we sought to develop updated Risk Equations for Complications Of type 2 Diabetes (RECODe). METHODS: To develop and validate these risk equations, we used data from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD, n=9635; 2001-09) and validated the equations for microvascular events using data from the Diabetes Prevention Program Outcomes Study (DPPOS, n=1018; 1996-2001), and for cardiovascular events using data from the Action for Health in Diabetes (Look AHEAD, n=4760; 2001-12). Microvascular outcomes were nephropathy, retinopathy, and neuropathy. Cardiovascular outcomes were myocardial infarction, stroke, congestive heart failure, and cardiovascular mortality. We also included all-cause mortality as an outcome. We used a cross-validating machine learning method to select predictor variables from demographic characteristics, clinical variables, comorbidities, medications, and biomarkers into Cox proportional hazards models for each outcome. The new equations were compared to older risk equations by assessing model discrimination, calibration, and the net reclassification index. FINDINGS: All equations had moderate internal and external discrimination (C-statistics 0·55-0·84 internally, 0·57-0·79 externally) and high internal and external calibration (slopes 0·71-1·31 between observed and estimated risk). Our equations had better discrimination and calibration than the UK Prospective Diabetes Study Outcomes Model 2 (for microvascular and cardiovascular outcomes, C-statistics 0·54-0·62, slopes 0·06-1·12) and the American College of Cardiology/American Heart Association Pooled Cohort Equations (for fatal or non-fatal myocardial infarction or stroke, C-statistics 0·61-0·66, slopes 0·30-0·39). INTERPRETATION: RECODe might improve estimation of risk of complications for patients with type 2 diabetes. FUNDING: National Institute for Diabetes and Digestive and Kidney Disease, National Heart, Lung and Blood Institute, and National Institute on Minority Health and Health Disparities, National Institutes of Health, and US Department of Veterans Affairs.
BACKGROUND: In view of substantial mis-estimation of risks of diabetes complications using existing equations, we sought to develop updated Risk Equations for Complications Of type 2 Diabetes (RECODe). METHODS: To develop and validate these risk equations, we used data from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD, n=9635; 2001-09) and validated the equations for microvascular events using data from the Diabetes Prevention Program Outcomes Study (DPPOS, n=1018; 1996-2001), and for cardiovascular events using data from the Action for Health in Diabetes (Look AHEAD, n=4760; 2001-12). Microvascular outcomes were nephropathy, retinopathy, and neuropathy. Cardiovascular outcomes were myocardial infarction, stroke, congestive heart failure, and cardiovascular mortality. We also included all-cause mortality as an outcome. We used a cross-validating machine learning method to select predictor variables from demographic characteristics, clinical variables, comorbidities, medications, and biomarkers into Cox proportional hazards models for each outcome. The new equations were compared to older risk equations by assessing model discrimination, calibration, and the net reclassification index. FINDINGS: All equations had moderate internal and external discrimination (C-statistics 0·55-0·84 internally, 0·57-0·79 externally) and high internal and external calibration (slopes 0·71-1·31 between observed and estimated risk). Our equations had better discrimination and calibration than the UK Prospective Diabetes Study Outcomes Model 2 (for microvascular and cardiovascular outcomes, C-statistics 0·54-0·62, slopes 0·06-1·12) and the American College of Cardiology/American Heart Association Pooled Cohort Equations (for fatal or non-fatal myocardial infarction or stroke, C-statistics 0·61-0·66, slopes 0·30-0·39). INTERPRETATION: RECODe might improve estimation of risk of complications for patients with type 2 diabetes. FUNDING: National Institute for Diabetes and Digestive and Kidney Disease, National Heart, Lung and Blood Institute, and National Institute on Minority Health and Health Disparities, National Institutes of Health, and US Department of Veterans Affairs.
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