用基于变压器的风险模型精细选择个人进行预防性心血管疾病治疗
Shishir Rao1, Yikuan Li1, Mohammad Mamouei1
1Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
The Lancet. Digital health
|June 3, 2025
概括
一个新的深度学习模型,TRisk,准确地预测了10年的心血管疾病风险. 与目前的方法相比,TRisk提供了更有针对性的患者选择,减少了初级预防和糖尿病患者群体的过度治疗.
科学领域:
- 心血管疾病风险预测预测
- 医疗保健中的人工智能
- 深度学习用于医学预后.
背景情况:
- 目前关于心血管疾病 (CVD) 风险的统计模型往往过度推预防性治疗.
- 现有的方法在患有糖尿病等先前存在的疾病的人群中不充分模拟风险.
- 需要改进风险分层模型以优化心血管疾病预防策略.
研究的目的:
- 开发和验证基于变压器的风险评估生存 (TRisk) 模型.
- 为了预测心血管疾病的10年风险.
- 评估TRisk在初级预防和糖尿病患者中的表现.
主要方法:
- 利用来自英语电子健康记录 (1998-2015) 的300万成年人 (25-84岁) 的大型,开放的队列.
- 开发并验证了TRisk模型,一种新的深度学习方法.
- 将TRisk与QRISK3分数和深度学习变体进行比较,分析跨人口统计和社会经济地位的表现.
主要成果:
- 在初级预防群体中,TRisk表现出优越的歧视 (C指数为0.910).
- 在各种年龄组,性别和社会经济地位中,TRisk表现和校准比基准模型更好.
- 与当前的策略相比,TRisk显著减少了被归类为高风险和假阴性患者的数量,特别是在初级预防和糖尿病队列中.
结论:
- TRisk模型可以更精确地识别患心血管疾病高风险的个体.
- 将TRisk纳入临床实践可能会使推治疗的患者数量减少约三分之一.
- TRisk的目标是保持或提高事件预防率,同时优化治疗资格.
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