Lu Xinyu, Yue Qing, Jing He, Zhong Gangliang, Ning Zhen, Du Jiang, Zhao Min
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Division of Tuberculosis and AIDS Control and Prevent, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
BMJ Public Health. 2025 Aug 28;3(2):e002290. doi: 10.1136/bmjph-2024-002290. eCollection 2025.
Hepatitis C virus (HCV) infection is a substantial public health concern, particularly among individuals with opioid addiction. The methadone maintenance treatment (MMT) programmes serve as a harm reduction strategy to mitigate HIV disease spread, yet the risk of HCV infection remains high within these settings. Accurate risk prediction for HCV seroconversion is therefore crucial for improving patient outcomes.
We collected data from 1547 individuals with opioid use disorder who entered the MMT programme from May 2005 to October 2023 in Shanghai, China, and 283 individuals from July 2006 to October 2023 in Mianyang, China, whose HCV infection status was monitored. Shanghai data were divided into training and validation sets in a 7:3 ratio, with 70% of the Shanghai samples used for model training and the remaining 30% reserved for internal validation. Additionally, the Mianyang dataset was employed as an independent external validation cohort to assess the model's generalisability. Four machine learning models were developed. We then validated the predictive performance of the model using C-index, receiver-operating characteristic curves, calibration plots and decision curve analysis.
13 predictive factors, including sex, age, ethnicity, education, occupation status, marriage status, living status, financial resource, drug use method in the past half year, injected drug last month, condom use, average methadone dosage and positive rate of drug urine tests, were all incorporated into the predictive model. The eXtreme Gradient Boosting (XGBoost) model exhibited superior performance in both discrimination and calibration compared with the other three models. Specifically, it achieved C-indices of 0.793 (95% CI: 0.771 to 0.813) in the training cohort, 0.744 (0.709 to 0.779) in the internal validation cohort and 0.756 (0.712 to 0.799) in the external validation cohort for predicting HCV seroconversion. A publicly accessible web tool was generated for the model.
The developed XGBoost model has the potential to accurately predict individuals on MMT programmes at high risk of HCV seroconversion.
丙型肝炎病毒（HCV）感染是一个重大的公共卫生问题，在阿片类药物成瘾者中尤为突出。美沙酮维持治疗（MMT）项目是一种减少危害的策略，旨在减轻HIV疾病传播，但在这些环境中，HCV感染风险仍然很高。因此，准确预测HCV血清转化风险对于改善患者预后至关重要。
我们收集了2005年5月至2023年10月在中国上海进入MMT项目的1547名阿片类药物使用障碍患者的数据，以及2006年7月至2023年10月在中国绵阳的283名患者的数据，对其HCV感染状况进行了监测。上海的数据按7:3的比例分为训练集和验证集，其中70%的上海样本用于模型训练，其余30%留作内部验证。此外，绵阳数据集被用作独立的外部验证队列，以评估模型的通用性。开发了四种机器学习模型。然后，我们使用C指数、受试者操作特征曲线、校准图和决策曲线分析来验证模型的预测性能。
13个预测因素，包括性别、年龄、种族、教育程度、职业状况、婚姻状况、生活状况、财务状况、过去半年的吸毒方式、上个月注射吸毒、使用避孕套、平均美沙酮剂量和药物尿检阳性率，均被纳入预测模型。与其他三个模型相比，极端梯度提升（XGBoost）模型在区分度和校准方面均表现出卓越性能。具体而言，在预测HCV血清转化方面，它在训练队列中的C指数为0.793（95%CI：0.771至0.813），在内部验证队列中为0.744（0.709至0.779），在外部验证队列中为0.756（0.712至0.799）。为该模型生成了一个可公开访问的网络工具。
所开发的XGBoost模型有潜力准确预测MMT项目中HCV血清转化高风险个体。
