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. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. 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.

Ul pds risk engine