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将风险因素的纵向病史纳入动脉样硬化心血管疾病风险预测中,使用深度学习

Jingzhi Yu¹, Xiaoyun Yang¹, Yu Deng¹

¹Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

Scientific reports

|January 31, 2024

概括

纵向深度学习模型比传统方法提高了动脉样硬化心血管疾病 (ASCVD) 风险预测. 纳入长期风险因素数据可以提高模型的准确性和校准,从而改善患者的治疗结果.

科学领域:

心血管疾病研究研究
医疗人工智能医疗人工智能
预测分析在医疗保健中的应用

背景情况:

目前动脉样硬化心血管疾病 (ASCVD) 风险预测依赖于使用聚合队列方程 (PCE) 的横截面数据.
纵向风险因子数据和轨迹可以提供超出单个措施的优异预测.
使用纵向数据进行ASCVD风险预测的深度学习 (DL) 模型的比较好处仍未得到充分研究.

研究的目的:

评估纵向深度学习 (DL) 模型Dynamic-DeepHit的性能,用于动脉样硬化心血管疾病 (ASCVD) 风险预测.
使用纵向数据,将DL模型的预测精度与传统的聚合队列方程 (PCE) 进行比较.
通过结合纵向风险因素来评估模型歧视和校准的改进.

主要方法:

利用了来自15565名参与者在四个心血管疾病队列中的数据,没有基线ASCVD.
使用聚合队列方程 (PCE) 和纵向DL模型 (Dynamic-DeepHit) 计算了10年ASCVD风险.
包括标准PCE预测因素 (性别,种族,年龄,脂质,血压,糖尿病,高血压治疗,吸烟) 和8年的纵向数据.

主要成果:

与PCE相比,纵向DL模型显示了更好的歧视 (AUROC:0.815比0.792).
DL模型实现了0.385的净重新分类指数和较低的Brier分数 (0.0514对0.0542).
在15565名参与者中,有2170人 (13.9%) 在随访期间发展出ASCVD.

结论:

通过深度学习将纵向风险因素纳入,显著提高了动脉样硬化心血管疾病 (ASCVD) 风险预测.
与传统的聚合队列方程 (PCE) 相比,Dynamic-DeepHit模型显示出更高的歧视和校准.
在DL模型中纵向数据集成为临床风险评估和患者管理提供了有前途的进展.

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