开发和验证一个新的算法,以改善心血管风险预测.
Julia Hippisley-Cox1, Carol A C Coupland2,3, Mona Bafadhel4
1Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK. julia.hippisley-cox@phc.ox.ac.uk.
Nature medicine
|April 18, 2024
概括
一个新的QR4算法通过结合某些癌症和唐氏综合征等新型因素来改善心血管疾病 (CVD) 风险预测. 与现有评分相比,该工具为英国男性和女性提供了优越的10年心血管疾病风险估计.
科学领域:
- 心脏病学 心脏病学
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 心血管疾病 (CVD) 仍然是全球主要的死亡原因.
- 现有的风险预测算法,如QRISK3,为临床医生提供了宝贵的工具.
- 通过纳入新的风险因素,不断需要完善心血管疾病风险评估.
研究的目的:
- 导出和外部验证一个新的心血管疾病 (CVD) 风险算法,命名为QR4.4.
- 在CVD风险预测中将新型风险因素分别纳入男性和女性.
- 将QR4的性能与现有的心血管疾病风险评分 (如QRISK3,SCORE2和ASCVD) 进行比较.
主要方法:
- 利用了990万英国成年人的健康数据来推导算法和679万用于外部验证.
- 采用特定原因的考克斯模型来开发10年心血管疾病风险的预测模型.
- 外部验证了QR4算法,并将其性能 (C统计) 与QRISK3,SCORE2和ASCVD风险得分进行了比较.
主要成果:
- 确定了男性和女性的七种新风险因素 (例如,脑癌,肺癌,唐氏综合征,慢性肺炎),以及女性的另外两种风险因素 (孕前,产后抑郁症).
- 在外部验证时,QR4在女性 (0.835对0.831) 和男性 (0.814对0.812) 两种情况下都显示出高于QRISK3的C统计值.
- 与ASCVD和SCORE2风险分数相比,QR4在两种性别中都表现出更高的准确性.
结论:
- QR4算法在英国人口中提供了增强的心血管疾病风险预测.
- 纳入新的风险因素显著提高了心血管疾病风险评估的准确性.
- 与国际评分系统相比,QR4识别了新的风险群体,并为临床决策提供了更精确的工具.
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