在重症监护室内估计心脏输出量
Eric Palanques-Tost1, Roger Pallarès-López1, Raimon Padrós-Valls1
1Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, USA.
JACC. Advances
|April 26, 2025
概括
新的机器学习模型在危急疾病中准确估计心脏输出 (CO),优于估计Fick (eFick) 和热稀释 (TD) 等传统方法,以提供更好的患者护理.
科学领域:
- 心血管生理学心血管生理学
- 关键护理医学 关键护理医学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 心脏输出 (CO) 估计对于管理重症患者至关重要.
- 现有的方法如热稀释 (TD) 和估计Fick (eFick) 有局限性,需要新的方法.
研究的目的:
- 为重症监护机构开发和验证基于机器学习的新型心脏输出估计器.
- 为了解决目前在重症监护病房 (ICU) 的CO估计技术的局限性.
主要方法:
- 机器学习模型被训练和验证使用一个大数据集 (13,172测量从4,825名患者) 的TD-CO.
- 使用回归指标,轨迹分析和CO跟踪精度来评估性能.
- 模型使用了来自ICU或心脏导管实验室设置的常规生理测量.
主要成果:
- 由于静态氧气消耗估计 (例如,R2为-1.5),已建立的eFick模型在ICU中表现不佳.
- 新的CORE (导管优化cardiac输出估计) 模型实现了14%的MAPE和0.58的R2,明显优于eFick (P <0.001).
- CORE在三管吐的情况下表现出强度 (16%的MAPE,R2为0.65),并适应不同类型的导管.
结论:
- 结合动态生理学的机器学习模型,与eFick和TD相比,提高了ICU患者的CO估计准确度.
- 核心模型提供了多功能性,易用性和广泛适用于各种ICU环境.
- 这些先进的二氧化碳估计器有可能提高重症病情的患者护理.
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