Abdul Jabbar Ali Bin, Inam Maha, Butt Nausharwan, Khan Sadiya S, Sheikh Sana, Khoja Adeel, Perry Benjamin, Gomez Gerardo Zavala, Slipczuk Leandro, Virani Salim S
Department of Medicine, Internal Medicine Division, Creighton University School of Medicine, Omaha, NE, USA.
Department of Medicine, Yale New Haven Hospital, New Haven, CT, USA.
Curr Atheroscler Rep. 2025 Jul 21;27(1):73. doi: 10.1007/s11883-025-01320-z.
This review aims to examine the rationale, development, and implications of the newly developed Predicting Risk of CVD EVENTs (PREVENT) equations for cardiovascular disease (CVD) risk assessment.
The PREVENT equations were developed from diverse, contemporary, real-world datasets and offer accurate discrimination for predicting risk of total CVD and separately, atherosclerotic CVD (ASCVD) and heart failure (HF). It addresses the nearly twofold overprediction of ASCVD risk with PCEs and includes risk factors related to cardiovascular-kidney-metabolic (CKM) syndrome (body mass index and estimated glomerular filtration rate, with the option to include albumin-creatinine ratio and haemoglobin A1C). Unlike PCEs, PREVENT did not include race as a predictor. PREVENT provides an option to add Social Deprivation Index (SDI) as variable in risk prediction which allows incorporation of social determinants of health. Studies indicate that PREVENT estimates for 10-year ASCVD risk are significantly lower than those obtained using PCEs. PREVENT also has potential to assess HF risk and guide potential therapies in the future for the prevention of HF. The PREVENT equations represent a crucial step forward in personalized CVD risk assessment, addressing limitations of PCEs by incorporating a broader range of CKM risk factors and accounting for social determinants of health. While promising for guiding future preventive strategies and public health initiatives, endorsement by guidelines and effective implementation into clinical workflows will be essential to realize its full potential in reducing the burden of CVD.
本综述旨在探讨新开发的心血管疾病（CVD）风险评估预测心血管疾病事件（PREVENT）方程的基本原理、发展情况及意义。
PREVENT方程是根据多样的、当代的真实世界数据集开发的，在预测总CVD风险、动脉粥样硬化性CVD（ASCVD）风险和心力衰竭（HF）风险方面具有准确的区分能力。它解决了使用Pooled队列方程（PCEs）时对ASCVD风险近两倍的过度预测问题，并纳入了与心血管-肾脏-代谢（CKM）综合征相关的风险因素（体重指数和估计肾小球滤过率，可选择纳入白蛋白-肌酐比值和糖化血红蛋白A1C）。与PCEs不同，PREVENT没有将种族作为预测因素。PREVENT提供了将社会剥夺指数（SDI）作为风险预测变量添加的选项，从而能够纳入健康的社会决定因素。研究表明，PREVENT对10年ASCVD风险的估计显著低于使用PCEs得出的估计值。PREVENT还有潜力评估HF风险，并在未来指导预防HF的潜在治疗方法。PREVENT方程代表了个性化CVD风险评估向前迈出的关键一步，通过纳入更广泛的CKM风险因素并考虑健康的社会决定因素，解决了PCEs的局限性。虽然有望指导未来的预防策略和公共卫生倡议，但指南的认可以及在临床工作流程中的有效实施对于充分发挥其在减轻CVD负担方面的潜力至关重要。
