超性心肌病中的机器学习:从临床和CMR特征的非线性模型预测心血管事件
Kankan Zhao1, Yanjie Zhu2, Xiuyu Chen3
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China.
JACC. Cardiovascular imaging
|July 13, 2024
概括
机器学习使用心磁共振 (CMR) 影像和临床数据准确预测高性心肌病 (HCM) 患者的主要不良心血管事件 (MACE). 这种方法超越了传统的风险模型,为HCM管理提供了更好的风险分层.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 增高性心肌病变 (HCM) 造成显著的临床负担,每年有10-15%的患者经历重大心血管不良事件 (MACE).
- 现有的HCM风险分层模型在预测心脏突然死亡 (SCD) 中的准确性有限,并且不包含心脏磁共振 (CMR) 参数.
研究的目的:
- 开发和验证一种机器学习 (ML) 框架,用于预测HCM患者的MACE.
- 将CMR成像功能和临床特征集成到ML模型中,以提高风险预测.
主要方法:
- 利用了来自四个医疗中心 (2010-2017) 的758名HCM患者的数据.
- 在内部队列 (533名患者) 上使用光梯度增强机器开发了一种ML模型,并对225名患者进行外部验证.
- 包含14个CMR成像特征 (包括菌株和晚期加多增强[LGE]) 和23个临床变量.
主要成果:
- ML模型实现了0.830 (内部) 和0.812 (外部) 的曲线下的面积 (AUC),超过了经典的HCM风险-SCD模型的22.7% (P < 0.001).
- 在平均109个月的随访期间,MACE发生在25%的患者中.
- 在LGE范围,全球辐射菌株 (GRS),全球环形菌株 (GCS) 和MACEs之间发现了非线性相关性.
结论:
- 基于ML的风险分层整合CMR和临床数据,在HCM中提供准确的MACE预测,超过传统模型.
- 确定了CMR特征 (LGE,左心室压力梯度) 和MACE之间的非线性关系,为HCM评估和管理提供了宝贵的临床见解.
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