학술논문
Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program
Document Type
Article
Author
Ming-ying Lu; Chung-feng Huang; Chao-hung Hung; Chi-ming Tai; Lein-ray Mo; Hsing-tao Kuo; Kuo-chih Tseng; Ching-chu Lo; Ming-jong Bair; Szu-jen Wang; Jee-fu Huang; Ming-lun Yeh; Chun-ting Chen; Ming-chang Tsai; Chien-wei Huang; Pei-lun Lee; Tzeng-hue Yang; Yi-hsiang Huang; Lee-won Chong; Chien-lin Chen; Chi-chieh Yang; Sheng-shun Yang; Pin-nan Cheng; Tsai-yuan Hsieh; Jui-ting Hu; Wen-chih Wu; Chien-yu Cheng; Guei-ying Chen; Guo-xiong Zhou; Wei-lun Tsai; Chien-neng Kao; Chih-lang Lin; Chia-chi Wang; Ta-ya Lin; Chih-lin Lin; Wei-wen Su; Tzong-hsi Lee; Te-sheng Chang; Chun-jen Liu; Chia-yen Dai; Jia-horng Kao; Han-chieh Lin; Wan-long Chuang; Cheng-yuan Peng; Chun-wei-tsai; Chi-yi Chen; Ming-lung Yu
Source
Clinical and Molecular Hepatology(대한간학회지) / Clinical and Molecular Hepatology (CMH). Jan 01, 2024 30(1):64
Subject
Language
English
ISSN
2287-2728
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. (Clin Mol Hepatol 2024;30:64-79)