基于人工智能的聚类,以确定心力衰竭中的功能风险表型.
Xunhan Qiu1, Jun Ma1, Li Xu2
1Department of Cardiology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China.
Open heart
|February 26, 2026
概括
人工智能确定了三种心力衰竭表型,揭示了不同的心肺健康风险. 这种人工智能模型可以实现早期风险分层,以改善患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物统计学 生物统计学
背景情况:
- 心力衰竭 (HF) 患者经常经历未被检测到的心肺健康 (CRF) 衰退,增加了不良结果的风险.
- 目前的临床实践缺乏有效的工具,用于在HF患者早期的CRF风险分层.
研究的目的:
- 使用人工智能识别心力衰竭 (HF) 患者的新型心肺呼吸能力 (CRF) 风险表型.
- 开发和验证一种可解释和可通用的风险分层模型,用于HF早期功能评估.
主要方法:
- 一个人工智能驱动的无监督集群分析对505名高频率患者进行,使用了15个多模式临床变量.
- 鉴定的表型与CRF损伤 (VO2max≤20mL/kg/min) 之间的关联使用后勤回归和机器学习模型进行了评估.
- 沙普利添加式扩张 (SHAP) 分析确保了模型的解释性,在201名HF患者中进行了外部验证.
主要成果:
- 确定了三种不同的HF表型:平衡型,炎症性-sarcopenic型和代谢失调型.
- 这两种非平衡的表型都显示出心肺呼吸能力受损 (VO2max) 的几率明显更高.
- 机器学习模型,包括随机森林和XGBoost,在导出和验证队列中显示出强大的歧视性性能 (AUC ≈ 0.75).
结论:
- 通过人工智能驱动的多式联络数据整合,成功地在HF患者中确定了新的CRF风险表型.
- 为了早期的功能评估,建立了一个高度可解释和可通用的风险分层模型.
- 这些发现为心力衰竭管理中的精确康复策略提供了框架.
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