基于机器学习的预测心房在患有心房高频发作的患者心房动
Amir Askarinejad1, Tommaso Bucci1,2, Niloofar Asgharzadeh3
1Liverpool Centre for Cardiovascular Sciences at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
European journal of clinical investigation
|September 15, 2025
概括
机器学习模型有效预测心房动 (AF) 风险在心脏植入式电子设备 (CIED) 检测到心脏高频发作 (AHREs) 的患者. CatBoost模型显示出高准确度,识别了关键预测因素,如左心房扩大和高血压.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 目前的心脏植入式电子设备 (CIED) 检测到心脏高频发作 (AHREs) 的患者心房动 (AF) 风险的预测模型具有适度的性能.
- 需要改进的方法来识别患有AHREs的患者,这些患者有发展AF的高风险.
研究的目的:
- 评估机器学习 (ML) 算法的有效性,以预测CIED检测到AHREs的患者新发性AF的1年风险.
- 将ML模型的性能与传统的临床风险得分进行比较.
主要方法:
- 一项前性研究招募了100名没有先前AF史的患者,他们经历了CIED检测到的AHREs.
- 机器学习技术被用于预测1年的AF风险,使用临床变量,包括年龄,BMI,并发症和左心房扩大 (LAE).
主要成果:
- 24%的患者在1年内发展出新发的AF.
- CatBoost ML 模型表现出卓越的性能,曲线下面积 (AUC) 最高为 .857.7.
- 左心房扩大 (LAE),高血压,糖尿病和年龄被确定为AF最有影响力的预测因素.
结论:
- 机器学习技术为预测AHREs患者的AF提供了强大的方法.
- 建议在更大,独立的队列中进一步验证这些ML模型.
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