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<p>Accurate prediction of cardiovascular disease risk is crucial for prevention, but current models may not be generalizable to diverse populations. This study externally validated the predicting risk of cardiovascular disease EVENTs (PREVENT) model, which includes clinical and metabolic predictors, such as estimated glomerular filtration rate, to estimate 10-year ASCVD risk. Using data from the Tehran Lipid and Glucose Study, we assessed its performance in a Middle Eastern population of 5799 adults (ages 30–79 years) over a median of 13 years. We evaluated discrimination (AUC), calibration (pre- and post-recalibration), and decision-analytic metrics like net benefit (NB) and net reclassification improvement (NRI). The ASCVD incidence rate was 4.7 per 1000 person-years. PREVENT showed excellent discrimination in women (AUC: 0.84) and acceptable performance in men (AUC: 0.76). The model initially underestimated risk in men, which was corrected by recalibration, improving predicted risk from 4.2 to 7.6% and sensitivity from 62% to 80%. Recalibration also improved NRI (men: +61%, women: +79%) and slightly increased NB. Decision curve analysis showed clinical advantages for risk thresholds of 10–20% in women and 13–20% in men. PREVENT provides a promising tool for ASCVD risk prediction in Middle Eastern populations with local adaptation.</p>

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External validation of the PREVENT risk score: performance and clinical utility in an Iranian population

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月16日

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  • Amirhossein Hasanpour,
  • Samaneh Asgari,
  • Davood Khalili,
  • Fereidoun Azizi,
  • Farzad Hadaegh

摘要

Accurate prediction of cardiovascular disease risk is crucial for prevention, but current models may not be generalizable to diverse populations. This study externally validated the predicting risk of cardiovascular disease EVENTs (PREVENT) model, which includes clinical and metabolic predictors, such as estimated glomerular filtration rate, to estimate 10-year ASCVD risk. Using data from the Tehran Lipid and Glucose Study, we assessed its performance in a Middle Eastern population of 5799 adults (ages 30–79 years) over a median of 13 years. We evaluated discrimination (AUC), calibration (pre- and post-recalibration), and decision-analytic metrics like net benefit (NB) and net reclassification improvement (NRI). The ASCVD incidence rate was 4.7 per 1000 person-years. PREVENT showed excellent discrimination in women (AUC: 0.84) and acceptable performance in men (AUC: 0.76). The model initially underestimated risk in men, which was corrected by recalibration, improving predicted risk from 4.2 to 7.6% and sensitivity from 62% to 80%. Recalibration also improved NRI (men: +61%, women: +79%) and slightly increased NB. Decision curve analysis showed clinical advantages for risk thresholds of 10–20% in women and 13–20% in men. PREVENT provides a promising tool for ASCVD risk prediction in Middle Eastern populations with local adaptation.