开发和多国验证高Lp的算法策略 (a) 查
Arya Aminorroaya1, Lovedeep S Dhingra1, Evangelos K Oikonomou1
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Nature cardiovascular research
|August 28, 2024
概括
升高的脂蛋白 (a) (Lp(a)) 增加了心血管疾病的风险,但测试很少见. 一个新的机器学习模型,ARISE,有效地识别高Lp(a) 水平的个体,提高选效率.
科学领域:
- 心血管医学 心血管医学
- 人工智能的人工智能
- 遗传学和基因组学 在
背景情况:
- 升高的脂蛋白 (a) (Lp(a)) 是早期动脉样硬化心血管疾病的一个显著的遗传风险因素.
- 目前的Lp(a) 测试率很低 (<0.5%),阻碍了新兴向疗法的临床应用.
- 需要改进查策略,以识别因升高Lp (a) 的风险人群.
研究的目的:
- 开发和验证一种机器学习模型,用于针对性查升高的Lp (a) (≥150nmol/L).
- 评估该模型是否能够减少需要测试的个体数量,以识别患有高Lp的个体.
- 评估模型在多样化,大规模的队列研究中的表现.
主要方法:
- 开发了一种机器学习模型,ARISE (算法风险检查用于选高Lp(a),使用来自英国生物银行 (N=456,815) 的数据.
- 在三个独立的队列研究中对ARISE模型的外部验证:ARIC (N=14,484),CARDIA (N=4,124) 和MESA (N=4,672).
- 基于减少需要测试的人数 (NNT) 来识别患有升高Lp的个体的模型性能分析.
主要成果:
- ARISE模型在内部和外部验证队列中显示出一致的性能.
- 根据使用的概率值,ARISE将检测升高Lp的数量降低了67.3%,这取决于使用的概率值.
- 该模型有效地利用常见的临床特征用于查目的.
结论:
- 机器学习模型ARISE提供了一个有前途的工具,用于优化对Lp升高的选.
- 在电子健康记录中部署ARISE可以显著提高Lp (a) 测试在现实世界的临床环境中的产量.
- 通过ARISE促进的更好的查可能会促进早期干预和更好地管理与升高Lp (a) 相关的心血管风险.
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