Evidence map›Paper›PMID 39508793›Full record

Observational studyBiomolecules & biomedicine2025

NEWS, SIRS and qSOFA criteria for predicting sepsis and sepsis with high risk of death in emergency room: A comparison study and improved predictive models based on local data from CETAT and MIMIC-IV databases.

Wenwen Wang, Kaipeng Wang, Yueguo Wang, Qingyuan Liu, Jian Sun, Ronghua Shi, Sicheng Liu, Huanli Wang, Yuan Yuan, Jun Xu and 2 more

Abstract readComparative StudyObservational Study
In one paragraph

Observational study in Biomolecules & biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

12 authors.

Wenwen WangDepartment of Emergency Medicine, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, China.
Kaipeng WangSchool of Mathematics and Statistics, Nanjing University of Science and Technology, China.
Yueguo WangDepartment of Emergency Medicine, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, China.
Qingyuan LiuSchool of Mathematics and Physics, Anhui Jianzhu University, China.
Jian SunDepartment of Emergency Medicine, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, China.
Ronghua ShiHKU Business School, The University of Hong Kong, China.
Sicheng LiuSchool of Mathematical Sciences, University of Science and Technology of China, China.
Huanli WangInformation Technology Department, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, China.
Yuan YuanDepartment of Emergency Medicine, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, China.
Jun XuDepartment of Emergency Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Kui JinDepartment of Emergency Medicine, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, China.
Yixin ZhangSchool of Mathematical Sciences, University of Science and Technology of China, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early identification of sepsis in emergency department patients is critical for initiating timely interventions, highlighting the need for effective predictive scoring systems. A retrospective observational study was conducted using data from the CETAT database collected between December 2019 and October 2021. The study evaluated how well the systemic inflammatory response syndrome (SIRS), quick Sepsis-related Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) scoring systems, along with logistic regression models, predict sepsis, and high-risk sepsis in emergency department patients. The logistic regression models were further optimized by incorporating additional features based on local data. A total of 12,799 patients were analyzed, including 1360 sepsis cases, of which 373 were classified as high-risk sepsis. The NEWS score demonstrated superior predictive performance compared to qSOFA and SIRS, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.737 (95% confidence interval [CI] 0.72-0.75) for sepsis and 0.653 (95% CI 0.62-0.69) for high risk sepsis . After optimization, the NEWS-based model improved to an AUC-ROC of 0.756 (95% CI 0.74-0.77) for sepsis and 0.718 (95% CI 0.69-0.75) for high-risk sepsis. Further enhancement was observed with the inclusion of additional clinical variables, resulting in AUC-ROC values of 0.834 (95% CI 0.82-0.85) for sepsis and 0.756 (95% CI 0.73-0.78) for high-risk sepsis. Data from the Medical Information Mart for Intensive Care (MIMIC)-IV database, which included sepsis status and relevant variables for SIRS, qSOFA, and NEWS score calculations, confirmed that the optimized NEWS-based model improved the sepsis prediction AUC-ROC from 0.690 (95% CI 0.68-0.70) to 0.708 (95% CI 0.70-0.72), and consistently outperformed qSOFA and SIRS in sepsis prediction.

Indexed as

Emergency Service, HospitalOrgan Dysfunction ScoresSepsisSystemic Inflammatory Response SyndromeAdultAgedDatabases, FactualEarly Warning ScoreFemaleHumansLogistic ModelsMaleMiddle AgedRetrospective StudiesROC Curve

Identifiers

PMID39508793
PMCPMC12097386

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Registered trials

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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.