Potential modification of the UKPDS risk engine and evaluation of macrovascular event rates in controlled clinical trials

Abstract

Background: The aim of this study was to evaluate a modified UKPDS risk engine in order to establish a risk prediction benchmark for the general diabetes population. Methods: Data sources were summary demographic and risk factor data from the major type 2 diabetes mellitus outcomes studies, including ACCORD, ADVANCE, VADT, RECORD, PROactive, ADOPT, and BARI 2D. Patients in these studies spanned a wide spectrum of disease, from drug-naidieve to insulin-dependent. Cardiovascular events/major adverse coronary events (CVE/MACE) were primary or safety end points. Overall observed rates for cardiovascular events/MACE were summarized, and the observed annualized event rates were calculated using linear approximation. Simulation studies were then conducted using original (cardiovascular history excluded) and modified (cardiovascular history included) United Kingdom Prospective Diabetes Study (UKPDS) models; the predicted event rates were then compared with the observed event rates for all studies. The consistency of the predicted rates derived from each model was then evaluated using descriptive statistics and linear regression. Results: The original UKPDS model tended to overestimate event rates across studies. The ratio of predicted events versus observed MACE ranged from 0.9 to 2.0, with mean of 1.5 ± 0.4 and a coefficient of variation of 26% (R2=0.80). However, cardiovascular risk predictions were more precise using a modified UKPDS model; the ratio of predicted versus observed MACE events ranged from 1.8 to 2.4, with a mean of 2.1 ± 0.25 and a coefficient of variation of 13% (R2=0.94). Conclusion: A modified UKPDS model which includes adjustments for prior cardiovascular history has the potential for use as a tool for benchmarking and may be useful for predicting cardiovascular rates in clinical studies. This modification could be further evaluated, recalibrated, and validated using patient-level information derived from prospective clinical studies to yield greater predictability. © 2013 Yang et al, publisher and licensee Dove Medical Press Ltd.