Evidence map›Paper›PMID 41167422›Full record

ArticleJournal of advanced research2026

Development and validation of an early diagnostic model to distinguish bacterial infection in patients with acute-on-chronic liver disease (AoCLD) and systemic inflammatory response syndrome (SIRS): a post-hoc analysis of a prospective multicenter cohort.

Hui Zhou, Hai Li, Guohong Deng, Xianbo Wang, Xin Zheng, Jinjun Chen, Zhongji Meng, Yubao Zheng, Yanhang Gao, Zhiping Qian and 6 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of advanced research, 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

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

16 authors.

Hui ZhouDepartment of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, China.
Hai LiDepartment of Gastroenterology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China; Key Laboratory of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Chinese Ministry of Health (Shanghai Jiao Tong University), Shanghai, China; Chinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China.
Guohong DengChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Xianbo WangChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Xin ZhengChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Department of Infectious Diseases, Institute of Infection and Immunology, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan, China.
Jinjun ChenChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Hepatology Unit, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Zhongji MengChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Yubao ZhengChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Department of Infectious Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Yanhang GaoChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Department of Hepatology, The First Hospital of Jilin University, Changchun, China.
Zhiping QianChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Department of Liver Intensive Care Unit, Shanghai Public Health Clinical Centre, Fudan University, Shanghai, China.
Feng LiuChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Department of Infectious Diseases and Hepatology, The Second Hospital of Shandong University, Jinan, China.
Xiaobo LuChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Infectious Disease Center, The First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China.
Yu ShiChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Jia ShangChinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China; Department of Infectious Diseases, Henan Provincial People's Hospital, Zhengzhou, China.
Yan HuangDepartment of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, China; Chinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China. Electronic address: drhyan@163.com.
Ruochan ChenDepartment of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, China; Chinese Chronic Liver Failure (CLIF) Consortium, Shanghai, China. Electronic address: 405031@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesEarly differentiation between infectious and non-infectious systemic inflammatory response syndrome (SIRS) in patients with acute-on-chronic liver disease (AoCLD) remains challenging. This study aimed to develop and validate an early diagnostic model to accurately identify infection status in AoCLD patients with SIRS, thereby guiding targeted anti-infective therapy and improving clinical management.

methodsBased on two multicenter prospective cohorts from the Chinese Acute-on-Chronic Liver Failure (CATCH-LIFE) study, 515 AoCLD patients with SIRS were randomly divided into a training cohort (n = 361) and a validation cohort (n = 154). In the training cohort, predictive factors for infection were screened using logistic regression, least absolute shrinkage and selection operator (LASSO) regression, multicollinearity analysis, and stepwise regression. Restricted cubic splines (RCS) were employed to explore nonlinear relationships and interaction effects. A nomogram model was subsequently constructed. The model's performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), with internal validation via bootstrap method.

resultsThe final model incorporated five clinical indicators: jaundice, ascites, neutrophil count (N), albumin (ALB), and C-reactive protein (CRP). CRP exhibited a significant nonlinear relationship with infection risk (P < 0.001) and was included as a spline term, while an interaction term between ALB and jaundice was incorporated. The model demonstrated excellent discrimination, with area under the ROC curve (AUC) of 0.840 (95 % CI: 0.788-0.888) in the training cohort and 0.865 (95 % CI: 0.803-0.927) in the validation cohort, significantly outperforming single biomarkers. Calibration curves showed high consistency between predicted and observed probabilities. DCA indicated substantial clinical net benefit across threshold probabilities. A clinical decision pathway was developed based on the nomogram's risk prediction and optimal DCA threshold.

conclusionThe early diagnostic model developed in this study effectively differentiates between infectious and non-infectious SIRS in AoCLD patients, potentially reducing unnecessary antimicrobial exposure and improving clinical outcomes.

Indexed as

Acute-On-Chronic Liver FailureBacterial InfectionsSystemic Inflammatory Response SyndromeAdultBiomarkersDiagnosis, DifferentialEarly DiagnosisFemaleHumansMaleMiddle AgedProspective StudiesROC CurveBiomarkersAcute-on-chronic liver disease (AoCLD)Early diagnosisInfectionRisk factorsSystemic inflammatory response syndrome (SIRS)

Identifiers

PMID41167422
PMCPMC13316489

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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.