Wu Hongjie, Yue Mingqiang, Wang Tianbao, Wei Xiaoxia, Wang Yanping, Si Changyun
Department of Infectious Diseases, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.
Front Med (Lausanne). 2025 Jun 12;12:1542104. doi: 10.3389/fmed.2025.1542104. eCollection 2025.
To construct and validate a nomogram prediction model based on clinical characteristics and intestinal flora distribution in patients with chronic hepatitis B.
Patients with chronic hepatitis B were divided into training set ( = 175) and verification set ( = 75) according to the ratio of 7:3 by complete random method. In the training set, multivariate logistic regression was used to analyze the risk factors for the failure of antiviral therapy and the nomogram prediction model was constructed. The ROC curve and calibration curve were drawn to evaluate the prediction efficiency of the nomogram model and were verified in the verification set.
There was no significant difference in the incidence, clinical characteristics and distribution parameters of intestinal flora between the training set and the verification set ( > 0.05). Univariate analysis showed that the training set treatment ineffective group and the effective group had statistical differences in ALT, AST, hepatitis B virus DNA quantification, Shannon-Wiener index, Simpson index, Chao1 index, ACE index, relative abundance of , relative abundance of Bacteroides immitis, and PCA clustering separation ( < 0.05). Multivariate logistic regression analysis identified AST, hepatitis B virus DNA quantification, Shannon-Wiener index, Simpson index, and the relative abundance of Firmicutes and Bacteroides as independent risk factors for antiviral therapy failure ( < 0.05). Further, the nomogram prediction model was constructed, and the nomogram model had good calibration and fitting between prediction and reality in the training set and the verification set (ROC curves were shown in the training set and the verification set); AUC of the nomogram model for predicting the antiviral treatment effect was 0.869 and 0.829.
The nomogram model shows good discriminative ability for predicting suboptimal antiviral response, requiring multicenter validation. It should complement, not replace, clinical judgment and virological monitoring, aiding early risk identification and targeted interventions.
构建并验证基于慢性乙型肝炎患者临床特征和肠道菌群分布的列线图预测模型。
采用完全随机法，将慢性乙型肝炎患者按7:3的比例分为训练集（n = 175）和验证集（n = 75）。在训练集中，采用多因素logistic回归分析抗病毒治疗失败的危险因素，并构建列线图预测模型。绘制ROC曲线和校准曲线评估列线图模型的预测效能，并在验证集中进行验证。
训练集与验证集在肠道菌群的发生率、临床特征及分布参数方面差异无统计学意义（P>0.05）。单因素分析显示，训练集治疗无效组与有效组在ALT、AST、乙肝病毒DNA定量、香农-威纳指数、辛普森指数、Chao1指数、ACE指数、厚壁菌门相对丰度、侵袭拟杆菌相对丰度及主成分分析聚类分离方面存在统计学差异（P<0.05）。多因素logistic回归分析确定AST、乙肝病毒DNA定量、香农-威纳指数、辛普森指数以及厚壁菌门和拟杆菌门的相对丰度为抗病毒治疗失败的独立危险因素（P<0.05）。进一步构建列线图预测模型，该模型在训练集和验证集的预测与实际情况之间具有良好的校准和拟合度（训练集和验证集均显示ROC曲线）；列线图模型预测抗病毒治疗效果的AUC分别为0.869和0.829。
列线图模型在预测抗病毒反应欠佳方面显示出良好的判别能力，但需要多中心验证。它应补充而非取代临床判断和病毒学监测，有助于早期风险识别和针对性干预。
