ArticleClinical and molecular hepatology2025
Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study.
Article in Clinical and molecular hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04546360 (Spleen Stiffness Combined With Liver Stiffness Measured by Two-dimensional Shear Wave Elastography for the Screening of High-risk Varices in Patients With Compensated Advanced Chronic Liver Disease), which is not on this map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Spleen Stiffness Combined With Liver Stiffness Measured by Two-dimensional Shear Wave Elastography for the Screening of High-risk Varices in Patients With Compensated Advanced Chronic Liver Disease (CHESS2004)
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolomics and metabolites in cancer diagnosis and treatment.Molecular biomedicine · 2025Pooled it
- fiLiver and spleen elastography-based prediction models for clinically significant portal hypertension and high-risk esophageal varices: a systematic review of conventional and machine-learning approaches.Journal of ultrasound · 2026Review
- Artificial intelligence in advanced liver disease: From risk stratification to decision.Journal of the Chinese Medical Association : JCMA · 2026Review
- The evolution of non-invasive strategies in cirrhosis management-from screening to precision monitoring: Editorial on "Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study".Clinical and molecular hepatology · 2026Article
- Liver stiffness measurement-based risk score for predicting liver decompensation risk: a single-center retrospective Chinese study.Clinical and experimental medicine · 2025Article
- [Non-invasive evaluation and prediction of portal hypertension: focusing on disease progression and outcome].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
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62 authors.
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Abstract
BACKGROUND/
aimsA large percentage of patients undergoing esophagogastroduodenoscopy (EGD) screening do not have esophageal varices (EV) or have only small EV. We evaluated a large, international, multicenter cohort to develop a novel score, termed FIB-4plus, by combining the fibrosis-4 (FIB-4) score, liver stiffness measurement (LSM), and spleen stiffness measurement (SSM) to identify high-risk EV (HRV) in compensated cirrhosis.
methodsThis international cohort study involved patients with compensated cirrhosis from 17 Chinese hospitals and one Croatian institution (NCT04546360). Two-dimensional shear wave elastography-derived LSM and SSM values, and components of the FIB-4 score (i.e., age, aspartate aminotransferase, alanine aminotransferase, and platelet count [PLT]) were combined using machine learning algorithms (logistic regression [LR] and extreme gradient boosting [XGBoost]) to develop the LR-FIB-4plus and XGBoost-FIB-4plus models, respectively. Shapley Additive exPlanations method was used to interpret the model predictions.
resultsWe analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. The XGBoost-FIB-4plus score demonstrated superior predictive performance for HRV, with an area under the receiver operating characteristic curve (AUROC) of 0.927 (95% confidence interval [CI] 0.897-0.957) in the training cohort (n=268), and 0.919 (95% CI 0.843-0.995) and 0.902 (95% CI 0.820-0.984) in the first (n=118) and second (n=82) external validation cohorts, respectively. Additionally, the XGBoost-FIB-4plus score exhibited high AUROC values for predicting EV across all cohorts. The FIB-4plus score outperformed the individual parameters (LSM, SSM, PLT, and FIB-4).
conclusionThe FIB-4plus score effectively predicted EV and HRV in patients with compensated cirrhosis, providing clinicians with a valuable tool for optimizing patient management and outcomes.
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