ArticleClinical and molecular hepatology2024
Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program.
Article in Clinical and molecular hepatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 23 citations in OpenAlex.
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Cross-Model Explainability Consistency in Hepatitis C Stage Classification: A SHAP, LIME, and Counterfactual Analysis Across Five Machine Learning Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.Gut and liver · 2026Review
- CATS-BIT-ME Score: A Machine Learning and Conventional Risk Model for Prognosis in Hepatocellular Carcinoma.Gastro hep advances · 2026Article
- Review
- Review
- Unveiling the nexus between direct-acting antivirals in hepatitis C virus elimination and immune response.Clinical and experimental medicine · 2025Review
- Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.World journal of gastroenterology · 2025Article
- Real-world efficacy and safety of universal 8-week glecaprevir/pibrentasvir in patients with chronic hepatitis C with early chronic kidney disease or pre-end-stage renal disease: Insights from a nationwide hepatisis C virus registry in Taiwan.The Kaohsiung journal of medical sciences · 2025Article
- Correspondence to letter to the editor 1 on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".Clinical and molecular hepatology · 2025Article
- Real-World Experience, Effectiveness, and Safety of Direct-Acting Antivirals for the Treatment of Hepatitis C in Oman: A Cross-Sectional, Multicenter Study.Journal of clinical medicine · 2024Article
- The role of artificial intelligence in the management of liver diseases.The Kaohsiung journal of medical sciences · 2024Review
- Correspondence to editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".Clinical and molecular hepatology · 2024Article
- Benefits of Hepatitis C Viral Eradication: A Real-World Nationwide Cohort Study in Taiwan.Digestive diseases and sciences · 2024Article
- Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries.JMIR medical informatics · 2024Article
- Metformin and statins reduce hepatocellular carcinoma risk in chronic hepatitis C patients with failed antiviral therapy.Clinical and molecular hepatology · 2024Article
- Toward hepatitis C virus elimination using artificial intelligence.Clinical and molecular hepatology · 2024Article
- Correspondence on Letter regarding "Toward hepatitis C virus elimination using artificial intelligence".Clinical and molecular hepatology · 2024Article
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48 authors at 20 institutions in 2 countries.
Funding
Abstract
BACKGROUND/
aimsDespite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.
methodsWe analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment.
resultsThe training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset.
conclusionMachine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.
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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.