ArticleBMC gastroenterology2020
Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure.
Article in BMC gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk factors for the mortality of hepatitis B virus-associated acute-on-chronic liver failure: a systematic review and meta-analysis.BMC gastroenterology · 2023Pooled it
- Predicting 90-day mortality in patients with HBV-ACLF: A comparison of machine learning models and logistic regression.Medicine · 2026Article
- A machine learning model for 90-day mortality prediction in hepatitis B virus-related acute-on-chronic liver failure: the pivotal role of CALLY index.Frontiers in medicine · 2026Article
- Predictive machine learning model in intensive care unit patients with acute-on-chronic liver failure and two or more organ failures.Clinical and molecular hepatology · 2025Article
- An immune-based predictive model for HBV clearance: validation in multicenter cohorts and mechanistic insights from in vivo studies.Virology journal · 2025Article
- Machine learning-based algorithms for the prediction of 90-day survival in patients with liver failure receiving artificial liver therapy.Frontiers in physiology · 2025Article
- Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment.Computational and structural biotechnology journal · 2024Review
- From prognostication to therapeutics: Four key questions to accelerate the development of therapeutics for patients with acute-on-chronic liver failure.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2024Article
- Sarcopenia is associated with short- and long-term mortality in patients with acute-on-chronic liver failure.Journal of cachexia, sarcopenia and muscle · 2024Article
- The value of dynamic changes in FT3 level for predicting 90-day prognosis of HBV-ACLF patients.European journal of medical research · 2024Article
- Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning-powered 28-day Mortality Prediction Model for Hospitalized Patients with Acute Decompensation of Liver Cirrhosis.Oman medical journal · 2024Article
- Prevalence of Hepatitis B Virus Infection and Associated Factors Among High School Students in Shinshicho Town, Southern Ethiopia.Health services insights · 2024Article
- Prognostic value of neutrophil count to albumin ratio in patients with decompensated cirrhosis.Scientific reports · 2023Observational
- Progress on clinical prognosis assessment in liver failure.Liver research (Beijing, China) · 2023Review
- Machine learning-based model for predicting the esophagogastric variceal bleeding risk in liver cirrhosis patients.Diagnostic pathology · 2023Article
- Risk Association of Liver Cancer and Hepatitis B with Tree Ensemble and Lifestyle Features.International journal of environmental research and public health · 2022Article
- Recent Advances in Understanding, Diagnosing, and Treating Hepatitis B Virus Infection.Microorganisms · 2020Review
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12 authors.
Funding
Abstract
backgroundThis study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF) through artificial neural network (ANN) systems.
methodsSix hundred and eight-four cases of consecutive HBV-ACLF patients were retrospectively reviewed. Four hundred and twenty-three cases were used for training and constructing ANN models, and the remaining 261 cases were for validating the established models. Predictors associated with mortality were determined by univariate analysis and were then included in ANN models for predicting prognosis of mortality. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of the ANN models in comparison with various current prognostic models.
resultsVariables with statistically significant difference or important clinical characteristics were input in the ANN training process, and eight independent risk factors, including age, hepatic encephalopathy, serum sodium, prothrombin activity, γ-glutamyltransferase, hepatitis B e antigen, alkaline phosphatase and total bilirubin, were eventually used to establish ANN models. For 28-day mortality in the training cohort, the model's predictive accuracy (AUR 0.948, 95% CI 0.925-0.970) was significantly higher than that of the Model for End-stage Liver Disease (MELD), MELD-sodium (MELD-Na), Chronic Liver Failure-ACLF (CLIF-ACLF), and Child-Turcotte-Pugh (CTP) (all p < 0.001). In the validation cohorts the predictive accuracy of ANN model (AUR 0.748, 95% CI: 0.673-0.822) was significantly higher than that of MELD (p = 0.0099) and insignificantly higher than that of MELD-Na, CTP and CLIF-ACLF (p > 0.05). For 90-day mortality in the training cohort, the model's predictive accuracy (AUR 0.913, 95% CI 0.887-0.938) was significantly higher than that of MELD, MELD-Na, CTP and CLIF-ACLF (all p < 0.001). In the validation cohorts, the prediction accuracy of the ANN model (AUR 0.754, 95% CI: 0.697-0.812 was significantly higher than that of MELD (p = 0.019) and insignificantly higher than MELD-Na, CTP and CLIF-ACLF (p > 0.05).
conclusionsThe established ANN models can more accurately predict short-term mortality risk in patients with HBV- ACLF. The main content has been postered as an abstract at the AASLD Hepatology Conference (https://doi.org/10.1002/hep.30257).
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