Derivation and Validation of a Prediction Score for Major Coronary Heart Disease Events in a U.K. Type 2 Diabetic Population

Abstract

OBJECTIVE—To derive and validate an absolute risk algorithm for major coronary heart disease (CHD) events in the U.K. population with type 2 diabetes. RESEARCH DESIGN AND METHODS—A population cohort with type 2 diabetes was constructed in Tayside, Scotland, U.K., and longitudinally followed-up to June 2004. Participants were all people with type 2 diabetes registered with general practices and the Diabetes Audit and Research in Tayside, Scotland, database (97% sensitive) with no previous CHD event and with complete measurements (n = 4,569). The main outcome measure was risk of CHD defined as fatal or nonfatal myocardial infarction or CHD death, derived from the Weibull accelerated failure-time model. Validation of the algorithm was performed on an independent dataset from Salford, England, U.K. RESULTS—There were a total of 243 subjects (5.3%) with a fatal or nonfatal myocardial infarction or CHD death over the follow-up period from 1 January 1995 to 30 June 2004 (maximum follow-up 9.5 years). The final Weibull model included the significant predictors of age at diagnosis, duration of diabetes, HbA1c, smoking (current, past, never), sex, systolic blood pressure, treated hypertension, total cholesterol, and height. Assessment of discrimination and calibration in the Salford validation dataset demonstrated a good fit (c = 0.71 [95% CI 0.63–0.79]). CONCLUSIONS—This study provides the first validated, population-derived model for prediction of absolute risk of CHD in people with type 2 diabetes. It provides a useful additional decision aid for the clinician treating type 2 diabetes by indicating appropriate early action to decrease the risk of adverse outcomes.