ArticleAlimentary pharmacology & therapeutics2025
aCCI-HBV-ACLF: A Novel Predictive Model for Hepatitis B Virus-Related Acute-On-Chronic Liver Failure.
Article in Alimentary pharmacology & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Characterization of multi-stage metabolic alterations in hepatitis B virus-related acute-on-chronic liver failure using high-coverage metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- 1,5-Anhydroglucitol Aggravates Acute Liver Failure via the PPARα Signaling Pathway.Journal of clinical and translational hepatology · 2026Article
- A predictive model for PICC-related thrombosis in sepsis patients using XGBoost algorithm.Scientific reports · 2026Article
- Article
- Article
- Gut microbiota links to histological damage in chronic HBV infection patients and aggravates fibrosis via fecal microbiota transplantation in mice.Microbiology spectrum · 2025Article
- CD45BMC gastroenterology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundEarly identification of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) holds crucial importance in guiding clinical management and reducing mortality. However, existing scoring systems often overlook patient's underlying clinical condition, which significantly impacts prognosis.
aimsUse the age-adjusted Charlson comorbidity index (aCCI) to evaluate the patient's complications to develop a more precise model for predicting transplant-free mortality in HBV-ACLF patients.
methodsNine hundred and six patients were included for investigation and were segregated into a training cohort and a temporal validation cohort according to the chronological order of admission in a ratio of 7:3. In the training cohort, univariate analysis, logistic regression analysis and LASSO regression analysis were used to construct a prognostic model and it was subsequently validated in a temporal validation cohort and an external validation cohort.
resultsWe found total bilirubin, neutrophils, international normalised ratio and aCCI exhibited significant associations with 28-day transplant-free mortality and established a novel prognostic model, named aCCI-HBV-ACLF. The model demonstrated strong predictive performance, with area under the receiver operating characteristic curve (ROC) values of 0.859 for 28-day mortality, 0.822 for 90-day mortality. In the temporal validation cohort, aCCI-HBV-ACLF achieved area under the ROC values of 0.869 for 28-day mortality and 0.850 for 90-day mortality. In the external validation cohort, aCCI-HBV-ACLF had area under the ROC values of 0.868 for 28-day mortality and 0.888 for 90-day mortality.
conclusionsThis study proposes a new prognostic model, which achieved excellent predictive ability for 28-/90-day transplant-free mortality rates among patients with HBV-ACLF.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.