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

Authors :
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, Vol 30, Iss 1, Pp 64-79 (2024)
Publication Year :
2024
Publisher :
Korean Association for the Study of the Liver, 2024.

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.

Details

Language :
English
ISSN :
22872728 and 2287285X
Volume :
30
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Clinical and Molecular Hepatology
Publication Type :
Academic Journal
Accession number :
edsdoj.29b467adfe714190932d96e188f51734
Document Type :
article
Full Text :
https://doi.org/10.3350/cmh.2023.0287