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Machine learning-driven analysis of serum GDF15 trajectories identifies novel sepsis sub-phenotypes and predicts 28-day mortality

delete2026-05-25
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PRE
AI
Q
Qinxue Wang
J
Jiawei Wang
Y
Yuhan Zhao
Y
Yuanze Ma
Y
Yong Ji *
Y
Yi Han *
DOI：10.1016/j.cyto.2026.157172delete
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Abstract

Abstract

En 中文
Sepsis heterogeneity complicates management and prognosis. Growth differentiation factor 15 (GDF15) may offer novel insights into sepsis sub-phenotyping. This study explored serum GDF15 trajectories for sub-phenotyping and prognostic stratification.
Keywords:
GDF15
sepsis
sub-phenotyping
machine learning
prognosis

Journal

Cytokine cover
Cytokine
IF:
3.7
Papers:
9.7K
Citations:
1.4W

Organization

X
xuzhou medical university
Scholars:
1.5W
Papers: 7.2K
Citations: 158
N
nanjing medical university
Scholars:
6.9K
Papers: 1.8K
Citations: 2
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