Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials.
Pubmed ID: 28803840
Pubmed Central ID: PMC5769867
Journal: The lancet. Diabetes & endocrinology
Publication Date: Oct. 1, 2017
MeSH Terms: Humans, Male, Female, Aged, Cardiovascular Diseases, Risk Factors, Middle Aged, Randomized Controlled Trials as Topic, Risk Assessment, Diabetic Angiopathies, Diabetes Mellitus, Type 2
Grants: P30 DK092926, K08 HL121056, U54 MD010724, DP2 MD010478, L30 DK103291, K23 DK109200, P60 DK020572, R01 HL132814
Authors: Hayward RA, Basu S, Sussman JB, Berkowitz SA, Yudkin JS
Cite As: Basu S, Sussman JB, Berkowitz SA, Hayward RA, Yudkin JS. Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials. Lancet Diabetes Endocrinol 2017 Oct;5(10):788-798. Epub 2017 Aug 10.
Studies:
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.