Evidence map›Paper›PMID 41333802›Full record

ArticleFrontiers in cellular and infection microbiology2025

Multi-biomarker score model for predicting fatal outcomes in severe fever with thrombocytopenia syndrome: a multicenter cohort study.

Mengsha Chen, Yingying Yuan, Tao Ju, Rongrong Qu, Siheng Zhu, Airong Hu, Zumo Zhou, Lina Xu, Piao Hu, Yejin Xu and 5 more

Abstract readMulticenter Study
In one paragraph

Article in Frontiers in cellular and infection microbiology, 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
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Mengsha Chen *Department of Emergency Medicine, Second Affiliated Hospital; Department of Epidemiology and Biostatistics, School of Public Health; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Yingying Yuan *State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; National Medical Center for Infectious Diseases; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases; The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Tao Ju *The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.
Rongrong QuDepartment of Emergency Medicine, Second Affiliated Hospital; Department of Epidemiology and Biostatistics, School of Public Health; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Siheng ZhuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; National Medical Center for Infectious Diseases; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases; The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Airong HuNingbo No. 2 Hospital, Ningbo, China.
Zumo ZhouDepartment of Infectious Diseases, Zhuji People's Hospital, Shaoxing, China.
Lina XuDepartment of Infectious Diseases, Zhuji People's Hospital, Shaoxing, China.
Piao HuThe First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China.
Yejin XuJinhua Central Hospital, Jinhua, Zhejiang, China.
Lianqing LouDepartment of Critical Care Medicine, Yiwu Central Hospital, Jinhua, Zhejiang, China.
Shibo Li *Wenzhou Medical University Affiliated Zhoushan Hospital, Zhoushan, China.
Wei Ye *Department of Infectious Disease and Liver Disease, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China.
Shigui Yang *Department of Emergency Medicine, Second Affiliated Hospital; Department of Epidemiology and Biostatistics, School of Public Health; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Dong Yan *State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; National Medical Center for Infectious Diseases; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases; The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Severe fever with thrombocytopenia syndrome (SFTS) is a rapidly progressive disease with high mortality. This study aims to identify mortality risk factors in SFTS and create a prognostic model for early high-risk patient identification. Methods: A total of 301 SFTS patients were enrolled between May 2012 and July 2023 from nine clinical centers across the East China region. Using principal component analysis and cox regression, we identified independent risk factors for mortality and constructed the multi-biomarker score model (1 point was assigned when age ≥60 years, AST/ALT ratio ≥2.23, BUN ≥5.6 mmol/L, ALP ≥ 68 U/L, or ALB <33.3 g/L). Results: Of the 301 patients with SFTS, 57 (18.9%) experienced fatal outcomes during hospitalization. The risk of mortality escalated with each additional point on the multi-biomarker score, with a HR of 2.04 (95% CI, 1.60-2.61). Patients were stratified into low (0-1), intermediate (2-3), and high (4-5) risk groups based on their multi-biomarker scores. Notably, those in the high-risk category were at an over eightfold increased risk of mortality (HR, 8.59; 95% CI, 2.63-28.05). High scores (4-5) were also predictive of adverse outcomes, including secondary bacterial infection, meningitis, ICU admission, heart failure, respiratory failure, and renal failure. The receiver operating characteristic (ROC) curve analysis for the multi-biomarker score revealed an AUC of 0.769 (95% CI, 0.632-0.787), suggesting a cutoff value above 3 as the threshold for optimal discrimination. Conclusions: SFTS patients with more than three of the following criteria-age ≥60 years, AST/ALT ratio ≥2.23, BUN ≥5.6 mmol/L, ALP ≥68 U/L, or ALB <33.3 g/L-show a significantly elevated mortality risk.

Indexed as

BiomarkersSevere Fever with Thrombocytopenia SyndromeAdultAgedAged, 80 and overChinaCohort StudiesFemaleHumansMaleMiddle AgedPrognosisRisk FactorsBiomarkersfatal outcomemortalitymulti-biomarker scoreprognostic modelsevere fever with thrombocytopenia syndromeSFTS

Identifiers

PMID41333802
PMCPMC12665722

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.