UKPDS Risk Engine, Decode and Diabetes PHD Models for the Estimation of Cardiovascular Risk in Patients with Diabetes

Paloma      Almeda-Valdes; Daniel      Cuevas-Ramos; Roopa      Mehta; Francisco   J.   Gomez-Perez; Carlos   A.   Aguilar-Salinas

Abstract

Individuals with type 2 diabetes have a two to four fold increased risk for developing cardiovascular disease than persons without diabetes. The presence of traditional and nontraditional risk factors that frequently coexist with type 2 diabetes are associated with this higher cardiovascular risk. Diabetes itself has been considered a cardiovascular disease equivalent. Nevertheless, the American Diabetes Association has recognized that absolute risk for cardiovascular disease varies among individuals with diabetes and has recommended the use of designed models and algorithms to estimate risk, especially in younger patients ( < 40 years). Cardiovascular risk is best evaluated with an estimation that takes into account the individuals characteristics and risk factors.

The algorithms and models that have been designed specifically for the assessment of cardiovascular risk in individuals with diabetes will be the subject of this review. Specifically, the DECODE (Diabetes Epidemiology: Collaborative analysis of Diagnostic criteria in Europe) equation has been shown to have discriminative capacity of 0.67; the UKPDS Risk Engine model is reported to have a sensitivity of around 90% and specificity of 33%; and the Diabetes Personal Health Decisions (PHD) in our study had a sensitivity of 67% and specificity of 41%.

In this review we will discuss the pros and cons of each model, their use in clinical practice and the application of the UKPDS risk engine and PHD model in a Mexican population.

Keywords: Cardiovascular risk, Diabetes mellitus, UKPDS risk engine, Diabetes PHD, DECODE