Good Performance for Universal CVD Risk Prediction Model

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A set of universal predictors, including traditional risk factors and cardiac biomarkers, provided an estimate risk in patients with and without atherosclerotic CVD.

A universal cardiovascular disease prediction tool performs well in patients with and without atherosclerotic CVD , a new study showed, suggesting this model could facilitate transition from primary to secondary prevention by streamlining risk classification.

Over a median follow-up of 18.9 years, 3209 ARIC participants developed MACE for an incidence rate per 1000 person-years of 21.3 for MACE, 12.6 for MI/stroke, and 13.8 for HF.Of all candidate predictors, 10 variables were included in the universal prediction model, which demonstrated good calibration in both those with ASCVD and without ASCVD .

 

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