Evidence map›Paper›PMID 41809043›Full record

ArticleiScience2026

Stratifying mortality risk in hypotension using outcome-oriented clustering of vital sign trajectories.

Zhuolun Liu, Li Li, Yongmin Zhang, Shasha Xue, Lina Zhang, Zhaoxin Qian, Shuai Zhang, Feng Lyu, Fan Wu

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PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Zhuolun LiuSchool of Computer Science and Engineering, Central South University, Changsha 410083, China.
Li LiDepartment of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha 410008, China.
Yongmin ZhangSchool of Computer Science and Engineering, Central South University, Changsha 410083, China.
Shasha XueSchool of Computer Science and Engineering, Central South University, Changsha 410083, China.
Lina ZhangDepartment of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha 410008, China.
Zhaoxin QianDepartment of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha 410008, China.
Shuai ZhangSchool of Computer Science and Engineering, Central South University, Changsha 410083, China.
Feng LyuSchool of Computer Science and Engineering, Central South University, Changsha 410083, China.
Fan WuSchool of Computer Science and Engineering, Central South University, Changsha 410083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prognostic significance of early vital sign trajectories in critically ill patients with hypotension remains unclear. This study applied an outcome-oriented two-step clustering model to characterize vital sign trajectories within 12 h of symptom onset, with the optimal window determined by weight learning, to improve mortality risk stratification. Patients (mean arterial pressure ≤65 mmHg or on vasopressors) were from the MIMIC-IV (development), eICU, and Xiangya multi-ICU (validation) databases. Three clusters with progressively increasing mortality were identified. Cluster 3 vs. 1 had the highest adjusted hazard ratio (2.30, 95%CI [2.04, 2.58]). The respiratory rate trajectory within the initial 2 h was the primary mortality discriminator. External validation demonstrated the model's generalizability, with hazard ratios (eICU, 2.96, 95% CI: 2.70, 3.23) and (Xiangya, 2.28, 95% CI: 1.93, 2.71) for cluster 3 vs. 1, respectively. This study demonstrated that hypotension can be risk-stratified by respiratory rate trajectories, with elevated breaths linked to higher mortality.

Indexed as

Cardiovascular medicineHealth sciencesIntensive care medicineInternal medicineMedical specialtyMedicine

Identifiers

PMID41809043
PMCPMC12969146

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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.