Con Danny, Clayton-Chubb Daniel, Tu Steven, Lubel John S, Nicoll Amanda, Bloom Stephen, Sawhney Rohit
Department of Gastroenterology, Eastern Health, Box Hill Hospital, 8 Arnold Street, Box Hill, Melbourne, Victoria, 3128, Australia.
Department of Gastroenterology, Alfred Health, Melbourne, Victoria, Australia.
Dig Dis Sci. 2025 Jan;70(1):367-377. doi: 10.1007/s10620-024-08746-6. Epub 2024 Nov 18.
Risk factors of chronic hepatitis B (CHB) immune flares are poorly understood. The primary aim of this study was to discover predictors of the CHB flare in non-cirrhotic, untreated CHB patients and develop a simple risk-stratifying score to predict the CHB flare. The secondary aim was to compare different machine learning methods for prediction.
A retrospective cohort of untreated, non-cirrhotic CHB patients with normal baseline ALT was followed up over time until an immune flare as defined by ALT twice the upper limit of normal. Statistical learning and machine learning algorithms were used to develop predictive models using baseline variables. Bootstrap validation was used to internally validate the models.
Of 405 patients (median age 44y; 41% male, 10% HBeAg positive), 67 (17%) experienced an immune flare by 5 years (annual incidence 4.0%). Predictors of flare included raised serum globulin, younger age, HBeAg positive status, higher viral load and raised liver stiffness. A simple predictive model "FLARE-B" had optimism-adjusted 1, 3 and 5-year AUCs of 0.813, 0.728 and 0.702, respectively. The random survival forest algorithm had the highest optimism-adjusted AUCs of 0.861, 0.766 and 0.725, respectively.
New, novel predictors of the CHB flare include a raised serum globulin and possibly raised liver stiffness and the absence of liver steatosis. FLARE-B can be used to risk-stratify individuals and potentially guide personalized management strategies such as monitoring schedules and proactive antiviral treatment in high-risk patients.
慢性乙型肝炎（CHB）免疫活动的危险因素尚不清楚。本研究的主要目的是发现非肝硬化、未经治疗的CHB患者发生CHB活动的预测因素，并开发一个简单的风险分层评分来预测CHB活动。次要目的是比较不同的机器学习方法用于预测。
对基线ALT正常的未经治疗的非肝硬化CHB患者进行回顾性队列研究，随访至ALT升高至正常上限两倍定义的免疫活动。使用统计学习和机器学习算法，利用基线变量建立预测模型。采用自助法验证对模型进行内部验证。
405例患者（中位年龄44岁；41%为男性，10%HBeAg阳性）中，67例（17%）在5年内发生免疫活动（年发病率4.0%）。活动的预测因素包括血清球蛋白升高、年龄较小、HBeAg阳性状态、病毒载量较高和肝脏硬度升高。一个简单的预测模型“FLARE - B”经乐观度调整后的1年、3年和5年AUC分别为0.813、0.728和0.702。随机生存森林算法经乐观度调整后的AUC最高，分别为0.861、0.766和0.725。
CHB活动新的预测因素包括血清球蛋白升高、可能的肝脏硬度升高以及无肝脂肪变性。FLARE - B可用于对个体进行风险分层，并可能指导个性化管理策略，如监测计划和对高危患者进行积极的抗病毒治疗。
