Hur Moon Haeng, Yip Terry Cheuk-Fung, Kim Seung Up, Lee Hyun Woong, Lee Han Ah, Lee Hyung-Chul, Wong Grace Lai-Hung, Wong Vincent Wai-Sun, Park Jun Yong, Ahn Sang Hoon, Kim Beom Kyung, Kim Hwi Young, Seo Yeon Seok, Shin Hyunjae, Park Jeayeon, Ko Yunmi, Park Youngsu, Lee Yun Bin, Yu Su Jong, Lee Sang Hyub, Kim Yoon Jun, Yoon Jung-Hwan, Lee Jeong-Hoon
Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.
J Hepatol. 2025 Feb;82(2):235-244. doi: 10.1016/j.jhep.2024.08.016. Epub 2024 Aug 31.
BACKGROUND & AIMS: The risk of hepatocellular carcinoma (HCC) and hepatic decompensation persists after hepatitis B surface antigen (HBsAg) seroclearance. This study aimed to develop and validate a machine learning model to predict the risk of liver-related outcomes (LROs) following HBsAg seroclearance.
A total of 4,787 consecutive patients who achieved HBsAg seroclearance between 2000 and 2022 were enrolled from six centers in South Korea and a territory-wide database in Hong Kong, comprising the training (n = 944), internal validation (n = 1,102), and external validation (n = 2,741) cohorts. Three machine learning-based models were developed and compared in each cohort. The primary outcome was the development of any LRO, including HCC, decompensation, and liver-related death.
During a median follow-up of 55.2 (IQR 30.1-92.3) months, 123 LROs were confirmed (1.1%/person-year) in the Korean cohort. The model with the best predictive performance in the training cohort was selected as the final model (designated as PLAN-B-CURE), which was constructed using a gradient boosting algorithm and seven variables (age, sex, diabetes, alcohol consumption, cirrhosis, albumin, and platelet count). Compared to previous HCC prediction models, PLAN-B-CURE showed significantly superior accuracy in the training cohort (c-index: 0.82 vs. 0.63-0.70, all p <0.001; area under the receiver-operating characteristic curve: 0.86 vs. 0.62-0.72, all p <0.01; area under the precision-recall curve: 0.53 vs. 0.13-0.29, all p <0.01). PLAN-B-CURE showed a reliable calibration function (Hosmer-Lemeshow test p >0.05) and these results were reproduced in the internal and external validation cohorts.
This novel machine learning model consisting of seven variables provides reliable risk prediction of LROs after HBsAg seroclearance that can be used for personalized surveillance.
Using large-scale multinational data, we developed a machine learning model to predict the risk of liver-related outcomes (i.e., hepatocellular carcinoma, decompensation, and liver-related death) after the functional cure of chronic hepatitis B (CHB). The new model named PLAN-B-CURE was constructed using seven variables (age, sex, alcohol consumption, diabetes, cirrhosis, serum albumin, and platelet count) and a gradient boosting machine algorithm, and it demonstrated significantly better predictive accuracy than previous models in both the training and validation cohorts. The inclusion of diabetes and significant alcohol intake as model inputs suggests the importance of metabolic risk factor management after the functional cure of CHB. Using seven readily available clinical factors, PLAN-B-CURE, the first machine learning-based model for risk prediction after the functional cure of CHB, may serve as a basis for individualized risk stratification.
乙肝表面抗原（HBsAg）血清学清除后，肝细胞癌（HCC）和肝失代偿风险依然存在。本研究旨在开发并验证一种机器学习模型，以预测HBsAg血清学清除后肝脏相关结局（LRO）的风险。
从韩国6个中心和香港的一个全地区数据库中纳入了2000年至2022年间连续实现HBsAg血清学清除的4787例患者，组成训练队列（n = 944）、内部验证队列（n = 1102）和外部验证队列（n = 2741）。在每个队列中开发并比较了三种基于机器学习的模型。主要结局是发生任何LRO，包括HCC、失代偿和肝脏相关死亡。
在韩国队列中位随访55.2（IQR 30.1 - 92.3）个月期间，确认发生123例LRO（1.1%/人年）。在训练队列中预测性能最佳的模型被选为最终模型（命名为PLAN - B - CURE），该模型使用梯度提升算法和7个变量（年龄、性别、糖尿病、饮酒、肝硬化、白蛋白和血小板计数）构建。与既往HCC预测模型相比，PLAN - B - CURE在训练队列中显示出显著更高的准确性（c指数：0.82 vs. 0.63 - 0.70，均p <0.001；受试者工作特征曲线下面积：0.86 vs. 0.62 - 0.72，均p <0.01；精确召回率曲线下面积：0.53 vs. 0.13 - 0.29，均p <0.01）。PLAN - B - CURE显示出可靠的校准功能（Hosmer - Lemeshow检验p>0.05），这些结果在内部和外部验证队列中得到重现。
这个由7个变量组成的新型机器学习模型为HBsAg血清学清除后的LRO提供了可靠的风险预测，可用于个性化监测。
利用大规模跨国数据，我们开发了一种机器学习模型，以预测慢性乙型肝炎（CHB）功能性治愈后肝脏相关结局（即肝细胞癌、失代偿和肝脏相关死亡）的风险。名为PLAN - B - CURE的新模型使用7个变量（年龄、性别、饮酒、糖尿病、肝硬化、血清白蛋白和血小板计数）和梯度提升机算法构建，在训练和验证队列中均显示出比既往模型显著更好的预测准确性。将糖尿病和大量饮酒纳入模型输入表明CHB功能性治愈后代谢危险因素管理的重要性。利用7个易于获得的临床因素，PLAN - B - CURE作为首个基于机器学习的CHB功能性治愈后风险预测模型，可为个体化风险分层提供依据。
