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使用机器学习进行不适当的植入式心脏转换器-除器治疗的风险预测

Ryo Tateishi¹, Makoto Suzuki², Masato Shimizu¹

¹Department of Cardiology, Yokohama Minami Kyosai Hospital, 1-21-1 Mutsuura-Higashi, Kanazawa-ku, Yokohama, Japan.

Scientific reports

|November 10, 2023

概括

机器学习模型可以预测不适当的植入式心脏转换器-除器 (ICD) 治疗. 开发的 Cardi35 评分,使用六个关键预测指标,提供了一个方便的工具来识别患有不适当ICD治疗风险的患者.

科学领域:

心脏病学心脏病学
生物医学工程生物医学工程
医疗保健中的机器学习

背景情况:

不适当的 implantable cardioverter-defibrillator (ICD) 治疗对患者构成风险.
需要预测模型来识别有接受不必要的ICD冲击风险的患者.
现有的预测方法可能无法充分利用复杂的患者数据.

研究的目的:

为不适当的ICD治疗开发和验证基于机器学习 (ML) 的预测模型.
确定关键的临床和心电图 (ECG) 参数,预测不适当的ICD治疗.
为了创建一个简单的,可计算的临床使用分数.

主要方法:

在182名患者的数据上使用了14个非深度学习的ML模型.
使用的心电图参数和ICD植入后的临床特征.
应用递归特征消除与交叉验证来识别重要的预测因素.

主要成果:

确定了六个重要的预测因素:心房律不整史,缺血性心肌病,没有糖尿病,缺乏心脏再同步治疗,J点的V3 ST水平和V5 R波幅度.
额外树木分类器实现了最高的预测性能 (AUROC 0.869).
导出的心脏35分数显示了1.62的危险比率 (P < 0.001) 和0.826的AUROC在特定的切线.

结论:

机器学习方法可以为不适当的ICD治疗产生强大的预测模型.
卡迪35评分为预测不适当的ICD治疗提供了一个方便和有效的工具.
临床实施 Cardi35 评分可能有助于优化 ICD 治疗并降低患者的风险.

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