一个机器学习衍生的风险评分来预测从临床心血管磁共振成像的左心室扩张功能障碍
Qingtao Zhou1, Lin Wang1,2, Jason Craft1,2
1DeMatteis Cardiovascular Institute, St. Francis Hospital & Heart Center, Roslyn, NY, United States.
Frontiers in cardiovascular medicine
|June 21, 2024
概括
一个机器学习算法有效地识别使用心脏磁共振 (CMR) 数据的左心室扩张功能障碍 (LVDD). 这种工具还可以预测心力衰竭患者住院和死亡风险.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 通过临床心脏磁共振 (CMR) 评估左心室扩张功能障碍 (LVDD) 是具有挑战性的.
- 准确的LVDD诊断对于预测不良心血管结果至关重要.
研究的目的:
- 开发和验证用于使用临床CMR变量进行LVDD评估的机器学习 (ML) 算法.
- 评估ML算法的预后能力,预测心力衰竭住院和全因死亡率.
主要方法:
- 使用人口和临床CMR变量 (包括延迟增强) 训练了一种随机森林ML模型.
- 模型性能使用引导和交叉验证进行评估,ROC曲线下的面积 (AUC) 作为主要指标.
- 获得ML风险评分来分层对患者不良结果的风险.
主要成果:
- 使用单独的CMR变量,ML算法实现了0.868的AUC,与人口统计数据相结合时改善到0.895.
- 该模型在独立验证队列中表现良好 (AUC 0.810).
- 较高的ML风险得分与心力衰竭住院和全因死亡率显著增加的风险相关.
结论:
- 使用临床CMR变量的ML算法对LVDD识别是有效的.
- 开发的ML工具为心血管疾病患者的不良临床结果提供了有价值的预后信息.
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