基于机器学习的预测模型,用于接受植入式心脏转换器除器的患者的死亡率.
1Department of Cardiology, Zhongshan Hospital of Fudan University, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai, China.
Pacing and clinical electrophysiology : PACE
|July 8, 2025
概括
机器学习准确地预测了植入式心脏转换器-除器 (ICD) 患者的3年生存期. 多层感知模型确定了关键预测因素,包括GFR,用于个性化风险分层.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 准确预测植入式心脏变压器 (ICD) 患者的临床发展轨迹对于患者管理至关重要.
- 机器学习 (ML) 提供了分析复杂数据模式和提供个性化风险估计的先进功能,超越了传统的统计方法.
研究的目的:
- 开发和验证机器学习模型,用于预测ICD患者的临床发展轨迹.
- 为了比较不同的ML模型在预测患者结果方面的表现.
- 确定ICD患者临床结果的关键预测因素.
主要方法:
- 这是一项追溯性研究,涉及来自中国四家医院的3175名患者.
- 开发和验证六个ML模型,包括多层感知子 (MLP).
- 模型性能使用接收器操作特征曲线下的面积 (AUROC) 进行评估;生存率使用卡普兰-梅尔曲线和夏普利添加式扩展 (SHAP) 进行分层.
主要成果:
- 该MLP模型实现了优异的预测准确性,AUROC为0.70 (内部) 和0.72 (外部).
- 在MLP模型中,有效地将患者分为高风险和低风险组 (p < 0.001).
- 在七个关键因素中,淋巴膜过率 (GFR) 被确定为最有影响力的预测因素.
结论:
- 该MLP模型准确地预测了ICD/CRT-D患者的3年生存期,并将其分为不同的风险组.
- 整合MLP和SHAP提供了可解释的AI,用于个性化风险预测,帮助临床决策和治疗优化.
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