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通过时间深度学习算法识别手术内低血压内型和革命

Yihao Zhu^1,2, Xuechao Hao², Peiyi Li²

¹Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.

Perioperative medicine (London, England)

|October 15, 2025

概括

这项研究使用深度学习来识别五种不同的血液动力学内手术性低血压 (IOH) 末型. 这些发现可能有助于开发IOH的向治疗方法,改善患者的治疗结果.

科学领域:

麻醉学和重症监护医学
生物医学工程生物医学工程
在医疗保健中的数据科学.

背景情况:

术内低血压 (IOH) 在临床实践中带来了挑战.
了解IOH期间的血液动力学变化对于有效治疗至关重要.
目前的方法缺乏对IOH的各种原因的详细见解.

研究的目的:

为了确定手术内低血压 (IOH) 的不同血液动力学内型.
将深度学习应用到高分辨率的手术内血液动力学数据,用于内型化IOH.
为了潜在地实现更精确的低血压因果治疗策略.

主要方法:

对手术患者进行持续血液动力学监测 (SVRI,SVI,SVV,CI,HR) 的回顾性分析.
IOH定义为至少1分钟的平均动脉压<65mmHg.
长期短期记忆 (LSTM) 自动编码器用于数据压缩,随后进行k-means聚类以识别内型.

主要成果:

分析包括了来自184名患者的1304次低血压发作.
通过K-means聚类,确定了五种不同的IOH内型:高CI的严重血管扩张,低血压,心肌抑郁,心,以及CI保持的轻度血管扩张.
特定的内型在不同的手术阶段表现出特征性事件,观察到一些事件内部的过渡.

结论:

该研究成功地确定了五种不同的血液动力学末型的手术内低血压.
这些内型为开发IOH更有针对性和因果治疗策略提供了基础.
需要进一步验证,以确认这些已识别的内型的临床实用性.

关键词:

麻醉麻醉是一种麻醉.自动编码器自动编码器血压管理血压管理深度学习模型深度学习模型血液动力学监测血液动力学监测血压下降血压下降血压下降没有监督的学习学习.

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