Machine Learning-Based Clusters of Vital Signs and Lactate Levels Predict Vasopressor Use in Sepsis

Machine learning-derived clusters based on initial vital signs and serum lactate levels demonstrated different patterns of vasopressor use and mortality. The clinical utility of this approach for guiding timely or targeted vasopressor therapy requires prospective validation.

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Publication Details

Journal
Clinical and Experimental Emergency Medicine
Published
2026-01-14
DOI
https://doi.org/10.15441/ceem.25.247
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine Learning-Based Clusters of Vital Signs and Lactate Levels Predict Vasopressor Use in Sepsis

Minyoung Choi, Seung Jin Maeng, Sun-Young Hwang, Tae Gun Shin et al.
Clinical and Experimental Emergency Medicine
Sepsis Diagnosis and Treatment
article

Machine Learning-Based Clusters of Vital Signs and Lactate Levels Predict Vasopressor Use in Sepsis

Minyoung Choi, Seung Jin Maeng, Sun-Young Hwang, Tae Gun Shin, Daun Jeong, Gun Tak Lee, Tae Ho Lim, Sung Phil Chung, on behalf of the Korean Shock Society, Hanbeom Yoon, Jong Eun Park
article en

Abstract

Machine learning-derived clusters based on initial vital signs and serum lactate levels demonstrated different patterns of vasopressor use and mortality. The clinical utility of this approach for guiding timely or targeted vasopressor therapy requires prospective validation.

Clinical and Experimental Emergency Medicine
Yonsei University (KR), Samsung Medical Center (KR), Gangnam Severance Hospital (KR), Hanyang University (KR), Chung-Ang University (KR), Sungkyunkwan University (KR)
Good health and well-being
Openalex Percentile: Top 83%
Sepsis Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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Machine Learning-Based Clusters of Vital Signs and Lactate Levels Predict Vasopressor Use in Sepsis — Minyoung Choi, Seung Jin Maeng, et al. · Clinical and Experimental Emergency Medicine (2026) | TGRS Research Map | TGRS