AI-Safe-C score: Assessing liver-related event risks in patients without cirrhosis after successful direct-acting antiviral treatment

Huapeng Lin; Terry Cheuk-Fung Yip; Hye Won Lee; Xiangjun Meng; Jimmy Che-To Lai; Sang Hoon Ahn; Wenjing Pang; Grace Lai-Hung Wong; Lingfeng Zeng; Vincent Wai-Sun Wong; Victor de Lédinghen; Seung Up Kim

doi:10.1016/j.jhep.2024.09.020

AI-Safe-C score: Assessing liver-related event risks in patients without cirrhosis after successful direct-acting antiviral treatment

J Hepatol. 2024 Sep 20:S0168-8278(24)02560-1. doi: 10.1016/j.jhep.2024.09.020. Online ahead of print.

Authors

Affiliations

¹ Department of Gastroenterology and Hepatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Center for Digestive Diseases Research and Clinical Translation of Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Gut Microecology and Associated Major Diseases Research, Shanghai, China; Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong.
² Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong.
³ Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; Yonsei Liver Center, Severance Hospital, Seoul, Korea.
⁴ Department of Gastroenterology and Hepatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Center for Digestive Diseases Research and Clinical Translation of Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Gut Microecology and Associated Major Diseases Research, Shanghai, China.
⁵ Department of General Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
⁶ Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong. Electronic address: wongv@mect.cuhk.edu.hk.
⁷ Hepatology Unit, Hôpital Haut-Lévêque, Bordeaux University Hospital, Bordeaux, France; INSERM U1312, Bordeaux University, Bordeaux, France. Electronic address: professeur.deledinghen@gmail.com.
⁸ Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; Yonsei Liver Center, Severance Hospital, Seoul, Korea. Electronic address: KSUKOREA@yuhs.ac.

PMID: 39307372
DOI: 10.1016/j.jhep.2024.09.020

Abstract

Background & aims: Direct-acting antivirals (DAAs) have considerably improved chronic hepatitis C (HCV) treatment; however, follow-up after sustained virological response (SVR) typically neglects the risk of liver-related events (LREs). This study introduces and validates the artificial intelligence-safe score (AI-Safe-C score) to assess the risk of LREs in patients without cirrhosis after successful DAA treatment.

Methods: The random survival forest model was trained to predict LREs in 913 patients without cirrhosis after SVR in Korea and was further tested in a combined cohort from Hong Kong and France (n = 1,264). The model's performance was assessed using Harrell's C-index and the area under the time-dependent receiver-operating characteristic curve (AUROC).

Results: The AI-Safe-C score, which incorporated liver stiffness measurement (LSM), age, sex, and six other biochemical tests - with LSM being ranked as the most important among nine clinical features - demonstrated a C-index of 0.86 (95% CI 0.82-0.90) in predicting LREs in an external validation cohort. It achieved 3- and 5-year LRE AUROCs of 0.88 (95% CI 0.84-0.92) and 0.79 (95% CI 0.71-0.87), respectively, and for hepatocellular carcinoma, a C-index of 0.87 (95% CI 0.81-0.92) with 3- and 5-year AUROCs of 0.88 (95% CI 0.84-0.93) and 0.82 (95% CI 0.75-0.90), respectively. Using a cut-off of 0.7, the 5-year LRE rate within a high-risk group was between 3.2% and 6.2%, mirroring the incidence observed in individuals with advanced fibrosis, in stark contrast to the significantly lower incidence of 0.2% to 0.6% in a low-risk group.

Conclusion: The AI-Safe-C score is a useful tool for identifying patients without cirrhosis who are at higher risk of developing LREs. The post-SVR LSM, as integrated within the AI-Safe-C score, plays a critical role in predicting future LREs.

Impact and implications: The AI-Safe-C score introduces a paradigm shift in the management of patients without cirrhosis after direct-acting antiviral treatment, a cohort traditionally not included in routine surveillance protocols for liver-related events. By accurately identifying a subgroup at a comparably high risk of liver-related events, akin to those with advanced fibrosis, this predictive model facilitates a strategic reallocation of surveillance and clinical resources.

Keywords: Artificial Intelligence; Chronic Hepatitis C; Direct-Acting Antivirals; Liver-Related Events; Non-Cirrhosis.