利用机器学习模型预测慢性乙型肝炎功能性治愈后的肝脏相关结局

乙型肝炎表面抗原（HBsAg）血清清除后，肝细胞癌（HCC）和肝功能衰竭的风险仍然存在。本研究旨在开发并验证一种机器学习模型，以预测在HBsAg血清清除后发生肝脏相关结局（LROs）的风险。共纳入2000年至2022年期间在韩国六个中心及香港一项区域性数据库中连续实现HBsAg血清清除的4,787名患者，分别构建了训练（n=944）、内部验证（n=1,102）和外部验证（n=2,741）队列。每个队列中开发并比较了三种基于机器学习的模型。主要结局为任何肝脏相关结局的发生，包括肝细胞癌、肝功能衰竭和肝相关死亡。在中位随访55.2（四分位间距30.1-92.3）个月期间，韩国队列确认发生123例LROs（每人年发生率1.1%）。在训练队列中表现最佳的模型被选为最终模型（命名为PLAN-B-CURE），该模型采用梯度提升算法，结合七个变量（年龄、性别、糖尿病、饮酒、肝硬化、血清白蛋白和血小板计数）构建。与之前的HCC预测模型相比，PLAN-B-CURE在训练队列中显示出显著优越的预测准确性（c指数：0.82对比0.63-0.70，全部p <0.001；受试者工作特征曲线下面积：0.86对比0.62-0.72，全部p <0.01；精确-召回曲线下面积：0.53对比0.13-0.29，全部p <0.01）。PLAN-B-CURE表现出可靠的校准功能（Hosmer-Lemeshow检验p >0.05），这些结果在内部和外部验证队列中得到了重复验证。该由七个变量组成的新型机器学习模型为HBsAg血清清除后的肝脏相关结局提供了可靠的风险预测，可用于个性化监测。利用大规模多国数据，我们开发了一个机器学习模型，用于预测慢性乙型肝炎（CHB）功能性治愈后肝脏相关结局（包括肝细胞癌、肝功能衰竭和肝相关死亡）的风险。新模型名为PLAN-B-CURE，采用七个变量（年龄、性别、饮酒、糖尿病、肝硬化、血清白蛋白和血小板计数）和梯度提升机器算法构建，在训练和验证队列中均表现出显著优于以往模型的预测准确性。糖尿病和显著饮酒量作为模型输入，显示了在CHB功能性治愈后管理代谢风险因素的重要性。利用这七个易得的临床指标，PLAN-B-CURE作为首个基于机器学习的模型，为CHB功能性治愈后风险的个体化分层提供了基础。

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.