Evidence map›Paper›PMID 40340384›Full record

ArticleJournal of the American Society for Mass Spectrometry2025

Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites.

Yu Hong, Li-Hua Li, Ting-Hao Kuo, Yi-Tzu Lee, Cheng-Chih Hsu

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 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
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

5 authors.

Yu HongDepartment of Chemistry, National Taiwan University, 10617, Taipei, Taiwan.ORCID 0009-0008-8336-8404
Li-Hua LiDepartment of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, 11217, Taipei, Taiwan.
Ting-Hao KuoDepartment of Chemistry, National Taiwan University, 10617, Taipei, Taiwan.
Yi-Tzu LeeDepartment of Emergency Medicine, Taipei Veterans General Hospital, 11217, Taipei, Taiwan.
Cheng-Chih HsuDepartment of Chemistry, National Taiwan University, 10617, Taipei, Taiwan.ORCID 0000-0002-2892-5326

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early recognition of septic shock is crucial for improving clinical management and patient outcomes, especially in the emergency department (ED). This study conducted serum metabolomic profiling on ED patients diagnosed with septic shock (n = 32) and those without septic shock (n = 92) using a high-resolution mass spectrometer. By implementing a supervised machine learning algorithm, a prediction model based on a panel of metabolites achieved an accuracy of 87.8%. Notably, when employed on a low-resolution instrument, the model maintained its predictive performance with an accuracy of 84.2%. These results demonstrate the potential of metabolite-based algorithms to identify patients at high risk of septic shock. Our proposed workflow aims to optimize risk assessment and streamline clinical management processes in the ED, holding promise as an efficient routine test to promote timely intensive interventions and reduce septic shock mortality.

Indexed as

MetabolomeMetabolomicsShock, SepticAdultAgedAlgorithmsBiomarkersEarly DiagnosisEmergency Service, HospitalFemaleHumansMachine LearningMaleMass SpectrometryMiddle AgedBiomarkersbiomarkersemergency departmentmachine learningmetabolomicspredictionseptic shock

Identifiers

PMID40340384
PMCPMC12142664

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.