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
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...
January 2024: Clinical and Molecular Hepatology