However, the CVD outcomes were largely similar, and this is unlikely toīe a large source of bias. The Framingham and EPIC-Norfolk CVD definitions included angina as an outcome, whereas the UKPDS definition did not. Nevertheless, this method probably identifies nonfatal events of most clinical importance, e.g., those resulting in hospital admission. Hospital admission data probably underestimate nonfatal CVD events because not all of them result in hospital admission. However, previous validation studies in our cohort indicate high specificity of such case ascertainment (10). Although we could ascertain all deaths in the EPIC-Norfolk cohort, we could not identify all nonfatal cardiovascular events. Four-fifths of the CVD events were nonfatal and were identified by linking records with hospital admission data. Measurement error in determining cardiovascular disease outcomes may have been present in our analyses. As statin use was not common in the EPIC-Norfolk cohort at baseline, this is unlikely to account for the overestimation in risk using the UKPDS equation. The moderate predictive value of the UKPDS equation in the EPIC-Norfolk cohort can perhaps be attributed in part to the sizeable proportion of individuals aged >65 years (25%) in this cohort. However, because the characteristics of the CARDS population are similar to that of the UKPDS, caution should still be used when calculating CVD risk in individuals outside the 25- to 65-year age range. The UKPDS equations have since been updated for use among individuals with established type 2 diabetes (version 3) (7), and the Risk Engine has been externally validated using data from the CARDS study (3,8). Conversely, in men, the Fra-mingham equations was significantly better at discriminating between individuals at high risk and classified more participants correctly than the UKPDS Risk Engine (NRI -25%, P 65 years should be completed with caution. In normoglycemic women, the NRI became nonsignificant, indicating that that both sets of equations classified EPIC-Norfolk participants equally well. In hyperglycemic women, however, the UKPDS Risk Engine was significantly better (P = 0.02) at discriminating between individuals at high risk, although the NRI was nonsignificant (P = 0.93). Similarly, for hyperglycemic men, results were the same as the overall findings. ![]() NRIs for reclassification were nonsignificant in both sexes. NRI (%), P value comparing UKPDS and Framingham models NRI (%), P value comparing UKPDS and Framingham models Participants with normoglycemia 0-20% ![]() NRI (%), P value comparing UKPDS and Framingham models Participants with nondiabetic hyperglycemia 0-20% ![]() OBJECTIVE - The purpose of this study was to examine the performance of the UK Prospective Diabetes Study (UKPDS) Risk Engine (version 3) and the Framingham risk equations (2008) in estimating cardiovascular disease (CVD) incidence in three populations: 1) individuals with known diabetes 2) individuals with nondiabetic hyperglycemia, defined as A1C >6.0% and 3) individuals with normoglycemia defined as A1C 6.0%) and 3) individuals with A1C 6.0% and 3) individuals with A1C 20% Performance of the UK Prospective Diabetes Study Risk Engine and the Framingham Risk Equations in Estimating Cardiovascular Disease in the EPIC-Norfolk Cohort
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