Evidence map›Paper›PMID 41088430›Full record

ArticlePerioperative medicine (London, England)2025

Identification of intraoperative hypotension endotypes and revolution with a temporal deep learning algorithm.

Yihao Zhu, Xuechao Hao, Peiyi Li, Yaqiang Wang, Fei Wang, Tao Zhu, Huadong Zhu

Abstract read
In one paragraph

Article in Perioperative medicine (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yihao Zhu *Emergency 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.
Xuechao Hao *Department of Anesthesiology, Research Units of Perioperative Stress Assessment and Clinical Decision (2018RU012), West China Hospital of Sichuan University, Chengdu, China.
Peiyi LiDepartment of Anesthesiology, Research Units of Perioperative Stress Assessment and Clinical Decision (2018RU012), West China Hospital of Sichuan University, Chengdu, China.
Yaqiang WangSchool of Software Engineering, Institute of Data Science and Engineering, Automatic Software Generation and Intelligent Service, Chengdu University of Information Technology, Chengdu, China.
Fei WangDepartment of Anesthesiology, Research Units of Perioperative Stress Assessment and Clinical Decision (2018RU012), West China Hospital of Sichuan University, Chengdu, China.
Tao ZhuDepartment of Anesthesiology, Research Units of Perioperative Stress Assessment and Clinical Decision (2018RU012), West China Hospital of Sichuan University, Chengdu, China. 739501155@qq.com.
Huadong ZhuEmergency 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. zhuhuadong1970@126.com.

Funding

1·3·5 project for disciplines of excellence ZYJC21008Major Science and Technology Project of Sichuan Province 2024YFFK0100National Natural Science Foundation of China 72304197
6 · The paper itself

Abstract

backgroundIdentifying specific causes of intraoperative hypotension (IOH) is a challenge in clinical practice. Improving the causal treatment of hypotension requires a more detailed understanding of the underlying hemodynamic alterations during hypotension. This study aims to identify distinct hemodynamic endotypes of IOH by applying a deep learning model to high-resolution intraoperative hemodynamic data.

methodsWe conducted a retrospective analysis for surgical patients who had undergone continuous intraoperative monitoring of systemic vascular resistance index (SVRI), stroke volume index (SVI), stroke volume variation (SVV), cardiac index (CI), and heart rate (HR). IOH was defined as a mean arterial pressure (MAP) < 65 mmHg sustained for at least 1 min. A long short-term memory (LSTM)-based autoencoder was developed to compress multivariate time-series data into a two-dimensional latent space. Unsupervised clustering, k-means, was performed on the two-dimensional latent representations, and the optimal number of clusters was determined by Calinski-Harabasz (CH) and Davies-Bouldin (DB) index.

resultsA total of 184 patients experienced at least one episode of IOH, and 1304 hypotensive episodes with 253,380 data points were included for analysis. K-means identified five distinct IOH endotypes. Based on the characteristic hemodynamic profiles of each cluster, we labeled the five endotypes: (1) severe vasodilation with high CI, (2) hypovolemia, (3) myocardial depression, (4) bradycardia, and (5) mild vasodilation with preserved CI. Mild vasodilation with preserved CI occurred mainly in the first and second quartiles of the procedure. Bradycardia occurred throughout the procedure, but more frequently in the fourth quartile. Myocardial depression occurred primarily in the second quartile, and hypovolemia occurred frequently throughout the procedure. It was also observed that intra-event transitions between endotypes within the same hypotensive episode occurred in five events.

conclusionsFive endotypes of IOH were identified, and this may support the development of causal treatment strategies of intraoperative hypotension, pending future validation.

Indexed as

AnesthesiaAutoencoderBlood pressure managementDeep learning modelHemodynamic monitoringHypotensionUnsupervised learning

Identifiers

PMID41088430
PMCPMC12523014

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.