Evidence map›Paper›PMID 39913913›Full record

ArticleJournal of medical Internet research2025

An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study.

Guanghao Liu, Shixiang Zheng, Jun He, Zi-Mei Zhang, Ruoqiong Wu, Yingying Yu, Hao Fu, Li Han, Haibo Zhu, Yichang Xu and 4 more

Abstract readValidation Study
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Guanghao Liu *Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0000-0001-8386-5410
Shixiang Zheng *Fujian Key Laboratory of Medical Bioinformatics, Institute of Precision Medicine, Fujian Medical University, Fuzhou, China.ORCID 0000-0001-9543-9953
Jun He *Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0000-0003-1510-1093
Zi-Mei ZhangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.ORCID 0000-0003-3428-9482
Ruoqiong WuDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0009-0000-0195-2079
Yingying YuDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0009-0008-6124-7791
Hao FuDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0009-0003-2725-8319
Li HanDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0009-0003-0280-5398
Haibo ZhuDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0009-0002-3371-9321
Yichang XuDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0009-0000-7142-8344
Huaguo ShaoDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0009-0000-7633-6879
Haidan YanDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0000-0003-0214-7471
Ting ChenDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-3228-9166
Xiaopei ShenDepartment of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-0004-4228

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeptic shock (SS) is a syndrome with high mortality. Early forewarning and diagnosis of SS, which are critical in reducing mortality, are still challenging in clinical management.

objectiveWe propose a simple and fast risk-stratified forewarning model for SS to help physicians recognize patients in time. Moreover, further insights can be gained from the application of the model to improve our understanding of SS.

methodsA total of 5125 patients with sepsis from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database were divided into training, validation, and test sets. In addition, 2180 patients with sepsis from the eICU Collaborative Research Database (eICU) were used as an external validation set. We developed a simplified risk-stratified early forewarning model for SS based on the weight of evidence and logistic regression, which was compared with multi-feature complex models, and clinical characteristics among risk groups were evaluated.

resultsUsing only vital signs and rapid arterial blood gas test features according to feature importance, we constructed the Septic Shock Risk Predictor (SORP), with an area under the curve (AUC) of 0.9458 in the test set, which is only slightly lower than that of the optimal multi-feature complex model (0.9651). A median forewarning time of 13 hours was calculated for SS patients. 4 distinct risk groups (high, medium, low, and ultralow) were identified by the SORP 6 hours before onset, and the incidence rates of SS in the 4 risk groups in the postonset interval were 88.6% (433/489), 34.5% (262/760), 2.5% (67/2707), and 0.3% (4/1301), respectively. The severity increased significantly with increasing risk in both clinical features and survival. Clustering analysis demonstrated a high similarity of pathophysiological characteristics between the high-risk patients without SS diagnosis (NS_HR) and the SS patients, while a significantly worse overall survival was shown in NS_HR patients. On further exploring the characteristics of the treatment and comorbidities of the NS_HR group, these patients demonstrated a significantly higher incidence of mean blood pressure <65 mmHg, significantly lower vasopressor use and infused volume, and more severe renal dysfunction. The above findings were further validated by multicenter eICU data.

conclusionsThe SORP demonstrated accurate forewarning and a reliable risk stratification capability. Among patients forewarned as high risk, similar pathophysiological phenotypes and high mortality were observed in both those subsequently diagnosed as having SS and those without such a diagnosis. NS_HR patients, overlooked by the Sepsis-3 definition, may provide further insights into the pathophysiological processes of SS onset and help to complement its diagnosis and precise management. The importance of precise fluid resuscitation management in SS patients with renal dysfunction is further highlighted. For convenience, an online service for the SORP has been provided.

Indexed as

Intensive Care UnitsShock, SepticAgedFemaleHumansMaleMiddle AgedRisk AssessmentRisk Factorsearly forewarningmachine learningrisk stratificationseptic shock

Identifiers

PMID39913913
PMCPMC11843061

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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.