采用PREVENT (预测心血管疾病风险事件) 风险算法:潜在的国际影响
G B John Mancini1, Arnold Ryomoto1
1Division of Cardiology, Department of Medicine, Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, British Columbia, Canada.
JACC. Advances
|August 2, 2024
概括
考虑慢性病的PREVENT算法,与标准得分相比,显著重新分类心血管风险. 它显示出改变预防实践的潜力,特别是在中度风险人群中.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 预防医学 预防医学
背景情况:
- 预防 (预测心血管疾病风险事件) 风险算法是为了更好地将代谢因素纳入心血管风险评估而开发的.
- 慢性病 (CKD) 显著影响心血管风险,需要改进风险分层工具.
研究的目的:
- 将PREVENT算法的性能与已知的心血管风险计算器进行比较 (弗雷明汉姆,聚合队列方程,SCORE2).
- 评估风险分层的准确性,特别是对于慢性病患者和那些被标准方法归类为中等风险的人.
主要方法:
- 创建了一个年龄在40-75岁的成年人模拟队列,包括正常和异常估计膜过率 (eGFR) 的人.
- 该研究分析了PREVENT和比较算法之间的一致性和重新分类率,重点关注中等风险分类.
主要成果:
- PREVENT证明了降低eGFR和增加心血管风险之间的明显相关性.
- 当中等风险被确定时,与标准算法一致性差异很大 (6%-88%).
- 在正常的eGFR模拟中,PREVENT显著重新分类了风险:它在18%-88%的正常eGFR模拟中分配了较低的风险,在异常eGFR模拟中,它在4%-94%的异常eGFR模拟中分配了更高的风险.
结论:
- 预防算法大大重新分类心血管风险,提供了预防策略的潜在转变.
- 如果PREVENT倾向于将正常的eGFR与较低风险联系在一起,可能会影响预防性治疗的开始.
- 医疗保健系统应监测由新风险算法驱动的预防实践变化的公共卫生影响.
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