Abstract：
OBJECTIVE: The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants non-invasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models for advanced fibrosis and cirrhosis in this patient population.
METHODS: Treatment-naive CHB patients with concurrent HS who underwent liver biopsy in ten medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from Shapley Additive exPlanations, were compared to Fibrosis-4 Index (FIB-4), Nonalcoholic fatty liver disease Fibrosis Score (NFS), and Aspartate transaminase to platelet ratio index (APRI) using the area under receiver operating characteristic curve (AUROC), and decision curve analysis (DCA).
RESULTS: Of 1,198 eligible patients, the random forest (RF) model achieved AUROCs of 0.778 [95% confidence interval (CI) 0.749-0.807] for diagnosing advanced fibrosis (RF-AF model) and 0.777 (95%CI 0.748-0.806) for diagnosing cirrhosis (RF-C model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the RF-AF model obtained an AUROC of 0.825 (95% CI 0.787-0.862) in patients with HBV DNA ≥105 IU/ml, and RF-C model had an AUROC of 0.828 (95% CI 0.774-0.883) in female patients. The two models outperformed FIB-4, NFS, and APRI in the training cohort, and also performed well in the validation cohort.
CONCLUSIONS: The RF models provide reliable, non-invasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the co-management of the two diseases.
METHODS: Treatment-naive CHB patients with concurrent HS who underwent liver biopsy in ten medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from Shapley Additive exPlanations, were compared to Fibrosis-4 Index (FIB-4), Nonalcoholic fatty liver disease Fibrosis Score (NFS), and Aspartate transaminase to platelet ratio index (APRI) using the area under receiver operating characteristic curve (AUROC), and decision curve analysis (DCA).
RESULTS: Of 1,198 eligible patients, the random forest (RF) model achieved AUROCs of 0.778 [95% confidence interval (CI) 0.749-0.807] for diagnosing advanced fibrosis (RF-AF model) and 0.777 (95%CI 0.748-0.806) for diagnosing cirrhosis (RF-C model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the RF-AF model obtained an AUROC of 0.825 (95% CI 0.787-0.862) in patients with HBV DNA ≥105 IU/ml, and RF-C model had an AUROC of 0.828 (95% CI 0.774-0.883) in female patients. The two models outperformed FIB-4, NFS, and APRI in the training cohort, and also performed well in the validation cohort.
CONCLUSIONS: The RF models provide reliable, non-invasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the co-management of the two diseases.
摘要：
目的：慢性乙型肝炎（CHB）叠加肝性脂肪变性（HS）的全球上升，评估纤维化进展的精确工具。这项研究利用机器学习（ML）来开发该患者人群中晚期纤维化和肝硬化的诊断模型。
方法：在十个医疗中心接受肝活检的并发HS的幼稚CHB患者作为训练队列和独立的外部验证队列（NCT05766449）。实施六个ML模型来预测晚期纤维化和肝硬化。最终的模型，源自Shapley添加剂扩张，与纤维化-4指数(FIB-4)进行比较，非酒精性脂肪性肝病纤维化评分（NFS），和天冬氨酸转氨酶与血小板比率指数（APRI）使用接受者工作特征曲线下面积（AUROC），和决策曲线分析(DCA)。
结果：在1,198名合格患者中，随机森林（RF）模型在训练队列中诊断晚期纤维化（RF-AF模型）和诊断肝硬化（RF-C模型）的AUROC为0.778[95％置信区间（CI）0.749-0.807]，并在验证队列中保持较高的AUROC。在训练组中，RF-AF模型在HBVDNA≥105IU/ml的患者中获得了0.825（95％CI0.787-0.862）的AUROC，在女性患者中，RF-C模型的AUROC为0.828（95％CI0.774-0.883）。这两种型号的性能优于FIB-4、NFS、和APRI在训练组中，并且在验证队列中也表现良好。
结论：RF模型提供了可靠的，非侵入性工具，用于识别慢性乙型肝炎患者并发HS的晚期纤维化和肝硬化，在这两种疾病的共同管理方面取得了重大进展。
方法：在十个医疗中心接受肝活检的并发HS的幼稚CHB患者作为训练队列和独立的外部验证队列（NCT05766449）。实施六个ML模型来预测晚期纤维化和肝硬化。最终的模型，源自Shapley添加剂扩张，与纤维化-4指数(FIB-4)进行比较，非酒精性脂肪性肝病纤维化评分（NFS），和天冬氨酸转氨酶与血小板比率指数（APRI）使用接受者工作特征曲线下面积（AUROC），和决策曲线分析(DCA)。
结果：在1,198名合格患者中，随机森林（RF）模型在训练队列中诊断晚期纤维化（RF-AF模型）和诊断肝硬化（RF-C模型）的AUROC为0.778[95％置信区间（CI）0.749-0.807]，并在验证队列中保持较高的AUROC。在训练组中，RF-AF模型在HBVDNA≥105IU/ml的患者中获得了0.825（95％CI0.787-0.862）的AUROC，在女性患者中，RF-C模型的AUROC为0.828（95％CI0.774-0.883）。这两种型号的性能优于FIB-4、NFS、和APRI在训练组中，并且在验证队列中也表现良好。
结论：RF模型提供了可靠的，非侵入性工具，用于识别慢性乙型肝炎患者并发HS的晚期纤维化和肝硬化，在这两种疾病的共同管理方面取得了重大进展。
