一个机器学习模型，以预测慢性肝炎功能治愈后与肝脏相关的结果

乙型肝炎表面抗原（HBSAG）血清清除术后，肝细胞癌（HCC）和肝功能负担的风险持续存在。这项研究旨在开发和验证机器学习模型，以预测HBSAG血清清除术后与肝脏相关结局（LROS）的风险。在2000年至2022年之间，共有4,787名连续4,787例获得了HBSAG血清清除率的患者，在韩国和一个韩国的六个中心之间招募了六个中心，并在Hong Kong的培训中（N = 94）（N = 94）（N = 94）（N = 94） 1,102）和外部验证（n = 2,741）队列。在每个队列中开发了三个基于机器学习的模型和比较。主要结果是任何LRO的发展，包括HCC，代理和与肝有关的死亡。在韩国队列中确认了55.2（IQR 30.1-92.3）月的中位随访（IQR 30.1-92.3）月，确认了123个LRO（1.1％/人/人）。在训练队列中具有最佳预测性能的模型被选为最终模型（指定为BLAN-B-CURE），该模型是使用梯度增强算法和七个变量（年龄，性别，性别，糖尿病，酒精消耗，cirrhosis，Cirrhosis，Chrorhosis，Chelloin和Platelet Count）构建的。与以前的HCC预测模型相比，PLAN-B-CUR在训练队列中的精度明显优于（0.82：0.82 vs. 0.63-0.70，所有p <0.001；在接收器操作特征曲线下面积：0.86 vs. 0.62-0.72 vs. 0.62-0.72，所有p <0.01;所有p <0.01; ast the Precision-Recal curve curve curve：0.53 vs. 0.53 vs. vs. 0.53 vs. vs. vs. vs. vs. vs. 0.53 vs. vs. vs. vs. vs. 0.53 vs. vs. vs. vs. vs. 0.53 vs. vss。 <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 （即肝细胞癌，代偿性和肝脏相关死亡）（CHB）的功能治愈后。使用七个变量（年龄，性别，酒精消耗，糖尿病，肝硬化，血清白蛋白和血小板计数）和一个梯度提升机算法构建了名为B Plan-B-CURE的新模型，并且与以前的培训模型相比，它在培训和验证群体中的预测准确性明显好得多。作为模型输入的糖尿病和大量酒精摄入的包括表明，CHB功能治疗后代谢风险因素管理的重要性。使用七个随时可用的临床因素，PLAN-B-CURE是CHB功能治疗后的第一个基于机器学习的风险预测模型，可以作为个性化风险分层的基础。

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.