机器学习预测持续心房的患者在电动心转术后的复发
Soonil Kwon1, Eunjung Lee2, Hojin Ju1
1Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
Korean circulation journal
|September 1, 2023
概括
机器学习模型在持续性AF患者的电动心转动 (ECV) 后预测心房动 (AF) 复发时显示出适度的准确性. 这些预测工具需要进一步验证.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 在电心转动 (ECV) 后,心房动 (AF) 的复发是一个重大的临床挑战.
- 机器学习 (ML) 对于预测ECV后AF复发的证据有限.
研究的目的:
- 开发和评估一种ML模型,用于预测持续性AF患者在ECV后1个月AF复发.
- 用临床特征和心电图 (ECG) 来评估ML模型的预测性能.
主要方法:
- 基于极端梯度增强 (XGBoost) 的ML模型使用临床数据和718名持久性AF患者的ECV前/后12导电图进行了训练.
- 模型性能使用十倍交叉验证进行评估,并仅与临床特征进行比较.
- 扩展监测数据 (15-分钟心电图,光电磁抽象) 在一小部分患者中进行了探索.
主要成果:
- 在ECV后1个月内,AF在60.6%的患者中复发.
- 结合临床特征和心电图的最终ML模型实现了0.63的接收器运行特征曲线 (AUROC) 下的面积,仅仅超过临床特征 (AF持续时间的最佳AUROC为0.58).
- 最终模型的灵敏度为84.7%,特异性为28.2%,F1得分为0.73;扩展的监测数据没有显著改善性能.
结论:
- 机器学习在持续性AF中在ECV后预测AF复发时提供了适度的性能.
- 开发的XGBoost模型显示了潜力,但需要在更大,更多样化的队伍中进一步验证.
- 未来的研究应该专注于改进ML模型,并探索额外的数据源以提高预测准确度.
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