Diagnostic value of cytokines combined with Model for End-Stage Liver Disease score in predicting hepatic encephalopathy in end-stage liver disease

Jinyu Xiang; Lixian Chang; Yingyuan Zhang; Huan Mu; Wenyan Li; Hongyan Wei; Chunyun Liu; Li Liu

doi:10.12449/JCH260721

Volume 42 Issue 7

Jul. 2026

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Article Navigation > Journal of Clinical Hepatology > 2026 > 42(7): 1638-1647

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Diagnostic value of cytokines combined with Model for End-Stage Liver Disease score in predicting hepatic encephalopathy in end-stage liver disease

DOI: 10.12449/JCH260721

Jinyu Xiang^{1, 2, 3},
Lixian Chang^{1, 2},
Yingyuan Zhang^{1, 2},
Huan Mu^{1, 2},
Wenyan Li^{1, 2},
Hongyan Wei^{1, 2, 3},
Chunyun Liu^{1, 2
,
,
,},
Li Liu^{1, 2}

1.
Department of Hepatology and Immunology，The Third People’s Hospital of Kunming，Kunming 650041，China
2.
Yunnan Clinical Center for Infectious Diseases，Kunming 650041，China
3.
School of Public Health，Dali University，Dali，Yunnan 671000，China

Research funding:

Kunming Science and Technology Plan Project (2024-1-NS-0035)

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Corresponding author: Liu Chunyun， 751440760@qq.com （ORCID： 0000-0001-5343-5305）
Received Date: 2026-01-20
Accepted Date: 2026-02-14
Published Date: 2026-07-25

Abstract

Abstract

Objective To construct and validate a predictive model for hepatic encephalopathy （HE） in patients with end-stage liver disease （ESLD） by combining key indicators such as cytokines and Model for End-Stage Liver Disease （MELD） score， and to provide evidence-based support for the early identification of high-risk populations and the optimization of intervention strategies in clinical practice. Methods A retrospective analysis was performed for 2 167 patients with ESLD who were admitted to The Third People’s Hospital of Kunming from January 2022 to December 2024， the patients were divided into a training set with 1 517 patients and a validation set with 650 patients at a ratio of 7∶3 using the stratified random sampling method. A univariate analysis and a least absolute shrinkage and selection operator （LASSO） regression analysis were performed for the training set to identify potential influencing variables， and then a binary Logistic regression model was constructed to investigate the independent influencing factors for HE in ESLD patients. A nomogram prediction model was established based on this regression model， and the Hosmer-Lemeshow goodness-of-fit test was performed. The receiver operating characteristic （ROC） curve， calibration curves， decision curve analysis， and clinical impact curve were used to comprehensively evaluate the degree of fit， accuracy， consistency， and clinical practicability of the predictive model derived from the training set. The Mann-Whitney U test was used for comparison of non-normally distributed quantitative data between two groups. The chi-square test or Fisher exact test was used for comparison of categorical data between two groups. Results The univariate analysis showed that there were significant differences between the study group and the control group in age， sex， blood ammonia， lymphocytes， hemoglobin， platelet count， prothrombin time， fibrinogen， international normalized ratio， total bilirubin， aspartate aminotransferase， total protein， albumin， prealbumin， alkaline phosphatase， cholinesterase， total bile acid， triglyceride， total cholesterol， high-density lipoprotein， low-density lipoprotein， blood glucose， CD3⁺ T cells， CD4⁺ T cells， CD8⁺ T cells， high-sensitivity C-reactive protein， carcinoembryonic antigen， triiodothyronine， thyroxine， free triiodothyronine， free thyroxine， interleukin-1β， interleukin-5， interleukin-6， interleukin-8， interleukin-12p70， interleukin-17， interferon-γ， and MELD score （all P<0.05）. The LASSO regression analysis identified nine variables of age， blood ammonia， albumin， prealbumin， cholinesterase， total cholesterol， thyroxine， interleukin-17， and MELD score， and the binary Logistic regression analysis showed that blood ammonia， interleukin-17， and MELD score were independent risk factors for HE in ESLD patients， while age and thyroxine were independent protective factors （all P<0.05）. A nomogram model was constructed based on these five variables. The Hosmer-Lemeshow goodness-of-fit test in the training set showed a good degree of fit （P=0.207）， with a McFadden’s pseudo-R² of 0.184， suggesting that the model had acceptable explanatory power； the Hosmer-Lemeshow goodness-of-fit test in the internal validation set further confirmed the calibration stability of the model （P=0.067）， suggesting that the model had a good degree of fit in independent data. The model had an area under the ROC curve of 0.784 in the validation set， with a sensitivity of 0.710 and a specificity of 0.752. The mean absolute error of the calibration curve was 0.067， and decision curve analysis and clinical impact curve showed that the nomogram model had positive net benefit and good clinical practicability. Conclusion The nomogram model constructed based on age， blood ammonia， thyroxine， interleukin-17， and MELD score for predicting HE in patients with ESLD has a good degree of fit， high accuracy and consistency， and excellent clinical practicability.
- End Stage Liver Disease,
- Hepatic Encephalopathy,
- Cytokines,
- Model for End Stage Liver Disease

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Diagnostic value of cytokines combined with Model for End-Stage Liver Disease score in predicting hepatic encephalopathy in end-stage liver disease

DOI: 10.12449/JCH260721

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Diagnostic value of cytokines combined with Model for End-Stage Liver Disease score in predicting hepatic encephalopathy in end-stage liver disease

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