Clin Mol Hepatol.  2024 Jan;30(1):64-79. 10.3350/cmh.2023.0287.

Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program

Affiliations
  • 1School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
  • 2Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
  • 3Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
  • 4Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
  • 5Division of Gastroenterology and Hepatology, Department of Internal Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
  • 6School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
  • 7Division of Gastroenterology, Tainan Municipal Hospital (Managed By Show Chwan Medical Care Corporation), Tainan, Taiwan
  • 8Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Yongkang District, Tainan, Taiwan
  • 9Ph.D. Program in Translational Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, and Academia Sinica, Taiwan
  • 10Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
  • 11School of Medicine, Tzuchi University, Hualien, Taiwan
  • 12Division of Gastroenterology, Department of Internal Medicine, St. Martin De Porres Hospital, Chiayi, Taiwan
  • 13Division of Gastroenterology, Department of Internal Medicine, Taitung Mackay Memorial Hospital, Taitung, Taiwan
  • 14Mackay Medical College, New Taipei City, Taiwan
  • 15Division of Gastroenterology, Department of Internal Medicine, Yuan's General Hospital, Kaohsiung, Taiwan
  • 16Division of Gastroenterology, Department of Internal Medicine, Tri Service General Hospital, National Defense Medical Center, Taipei, Taiwan
  • 17Division of Gastroenterology, Department of Internal Medicine, Tri Service General Hospital Penghu Branch, National Defense Medical Center, Taipei, Taiwan
  • 18School of Medicine, Chung Shan Medical University, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
  • 19Division of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
  • 20Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
  • 21Lotung Poh-Ai Hospital, Yilan, Taiwan
  • 22Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
  • 23Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
  • 24Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
  • 25School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
  • 26Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
  • 27Department of Gastroenterology, Division of Internal Medicine, Show Chwan Memorial Hospital, Changhua, Taiwan
  • 28Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
  • 29Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
  • 30Liver Center, Cathay General Hospital, Taipei, Taiwan
  • 31Wen-Chih Wu Clinic, Fengshan, Kaohsiung, Taiwan
  • 32Division of Infectious Diseases, Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
  • 33Penghu Hospital, Ministry of Health and Welfare, Penghu, Taiwan
  • 34Zhou Guoxiong Clinic, Penghu, Taiwan
  • 35Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
  • 36National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
  • 37Liver Research Unit, Department of Hepato-Gastroenterology and Community Medicine Research Center, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Keelung, Taiwan
  • 38Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and School of Medicine, Tzu Chi University, Taipei, Taiwan
  • 39Cishan Hospital, Ministry of Health and Welfare, Kaohsiung, Taiwan
  • 40Department of Gastroenterology, Renai Branch, Taipei City Hospital, Taipei, Taiwan
  • 41Department of Gastroenterology and Hepatology, Changhua Christian Hospital, Changhua, Taiwan
  • 42Division of Gastroenterology and Hepatology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
  • 43Division of Hepatogastroenterology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan and College of Medicine, Chang Gung University, Taoyuan, Taiwan
  • 44Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
  • 45Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
  • 46School of Medicine, China Medical University, Taichung, Taiwan
  • 47Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
  • 48Division of Gastroenterology and Hepatology, Department of Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan

Abstract

Background/Aims
Despite 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.
Methods
We 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.
Results
The 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.
Conclusions
Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Keyword

Hepatitis C virus; Antiviral agents; Artificial intelligence; Machine learning; Algorithms
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