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-naïve 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.

Keywords:

• type 2 diabetes mellitus
• macrovascular disease
• outcomes
• United Kingdom Prospective Diabetes Study
• modeling

Acknowledgments

FY conceived the study, participated in its design, interpreted the data, and helped to draft the manuscript. JY was responsible for performing the data acquisition and modeling, and contributed the methods section of the manuscript. KP served as scientific consultant to provide scientific input and was responsible for early drafts of the manuscript and for coordination of the drafting of the overall manuscript. MS contributed to the conception of the study, and providing medical input into the analyses. All authors contributed to the development and approval of the manuscript, and assume responsibility for the direction and content.

Disclosure

FY, JY, and MS are employees of GlaxoSmithKline. Each of the authors own stock in GlaxoSmithKline. KP was an employee of GlaxoSmithKline within Medical Communications Quality and Practices, under the Chief Medical Officer, at the time of development of this manuscript. Editorial support was provided by Michelle Evans, LeeAnn Pastorello, and Brett Scott at MediTech Media, Hamilton, NJ, with funding by GlaxoSmithKline. Editorial assistance was also provided by Douglas L Wicks of GlaxoSmithKline. This study was presented at the 70th Scientific Meeting of the American Diabetes Association, Orlando, FL, June 25–29, 2010, and the 46th Annual Meeting of the European Association for the Study of Diabetes, Stockholm, Sweden, September 20–24, 2010.