![]() In multivariate regression analysis, triglycerides and creatinine concentration (OR=1.040, p<0.001) were independent predictors of CVD risk, whereas hsCRP was not correlated with CVD risk. Also, about one third of diabetic patients (29.4%) were classified into the high-risk category. More males than females were classified at high UKPDS risk category (p<0.001). Biochemical and anthropometric parameters, and blood pressure were obtained. ![]() A total of 180 participants with DM2 (of them 50% females) were included in the current cross-sectional study. Furthermore, we aimed to explore whether non-traditional biomarker such as high sensitivity C-reactive protein (hsCRP) is superior for CVD risk prediction over old traditional risk factors. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be updated.Since there is a high prevalence of type 2 diabetes mellitus (DM2), as well as CVD in Montenegro, we aimed to estimate CVD risk by United Kingdom Prospective Diabetes Study (UKPDS) risk engine algorithm in individuals with DM2. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. Conclusions/interpretation We observed that the UKPDS risk engi Discrimination for these periods was still moderate to poor. The calibration of the UKPDS risk engine was slightly better for patients with type 2 diabetes who had been diagnosed with diabetes more than 10 years ago compared with patients diagnosed more recently, particularly for 4 and 5 year predicted CVD and CHD risks. Results The UKPDS risk engine showed moderate to poor discrimination for both CHD and CVD (c-statistic of 0.66 for both 5 year CHD and CVD risks), and an overestimation of the risk (224% and 112%). Discrimination was examined using the c-statistic and calibration by visually inspecting calibration plots and calculating the Hosmer-Lemeshow χ² statistic. Discrimination and calibration were assessed for 4, 5, 6 and 8 year risk. During a mean follow-up of 8 years, patients were followed for incidence of CHD and cardiovascular disease (CVD). Methods The cohort included 1,622 patients with type 2 diabetes. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes. The methods used in these validation studies were diverse, however, and sometimes insufficient. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be updated.Īims/hypothesis Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. Conclusions/interpretation We observed that the UKPDS risk engine overestimates CHD and CVD risk. Aims/hypothesis Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance.
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