深度学习预测冠心病患者的氧气吸收峰值:一项回顾性研究
Tao Shen1, Guomin Hu1, Wei Zhao2
1Department of Cardiology, Peking University Third Hospital, Beijing, China.
BMJ open
|October 5, 2025
概括
深度学习模型使用心肺运动测试 (CPET) 数据准确预测冠心病 (CHD) 患者的氧气吸收峰值 (VO2峰值). 这些模型显示出临床使用的前景,等待进一步验证.
科学领域:
- 心脏病学 心脏病学
- 运动生理学 运动生理学
- 人工智能在医学中的应用
背景情况:
- 准确预测氧气吸收峰值 (VO2峰值) 对于治疗冠心病 (CHD) 患者至关重要.
- 亚最大心肺运动测试 (CPET) 为评估心肺健康提供了有价值的指标.
- 传统的VO2峰值预测方法在准确性和适用性方面可能存在局限性.
研究的目的:
- 开发和验证深度学习模型,用于预测心脏病患者的VO2峰值.
- 将深度学习模型与传统机器学习算法的性能进行比较.
- 确定在VO2峰值预测中最具影响力的CPET关键指标.
主要方法:
- 一项涉及10538名接受CPET的CHD患者的回顾性研究.
- 开发和比较多个机器学习和深度学习模型.
- 使用R2,平均绝对误差 (MAE),偏差,布兰德-阿尔特曼分析和夏普利添加式扩展 (SHAP) 的性能评估.
主要成果:
- 神经网络模型表现出卓越的性能,达到0.82的R2和1.55毫升/公斤/分钟的MAE.
- XGBoost是表现最好的传统机器学习模型,R2为0.74.
- SHAP分析强调了VO2在无氧值 (VO2@AT),氧吸收效率斜率 (OUES) 和重量作为关键预测因素.
结论:
- 深度学习模型显示了使用CPET数据预测CHD患者的VO2峰值的巨大潜力.
- 开发的模型需要进一步的外部验证和前性研究以临床实施.
- 这种方法可以提高心脏呼吸能力在心血管疾病管理中的非侵入性评估.
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