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近年来，发热伴血小板减少综合征（Severe fever with thrombocytopenia syndrome, SFTS）传播范围不断扩大，给公众健康构成严重威胁。目前，尚无特异性治疗药物或已上市疫苗，主要靠对症支持治疗。早期识别重症患者并实施有针对性的干预，往往是降低病死率的关键。我们将91例SFTS患者的急性期血清抗体、细胞因子和相关实验室指标联合分析，通过机器学习的方法，构建出多因素Logistic回归、随机森林、支持向量机和XGBoost四种早期患者轻重症分类模型。在变量筛选阶段，我们依次使用单因素Logistic回归，多重共线性诊断和LASSO回归筛选变量，得到IL-10、IFN-γ、IFN-α、IL-8、AST、IgM OD450、TP和LY%共8个建模变量。结果显示，在早期轻重症分类中表现最佳的是随机森林（AUC=0.889），接下来是多因素Logistic回归（AUC=0.822）以及XGBoost(AUC=0.811)，而表现相对较差的是支持向量机（AUC=0.744）。此外，我们借助多因素Logistic回归识别出IFN-γ、IL-8、IgM OD450和LY%四种SFTS重症化独立预测因子。随机森林和XGBoost的变量重要性分析均表明，IL-10可能是最重要的预测因子，且IFN-γ、IL-8和AST也有较高的重要性。本研究基于血清学指标与机器学习方法开展SFTS患者轻重症分类模型的研究，为SFTS早期风险评估提供了新思路。
Abstract：In recent years, the geographical spread of severe fever with thrombocytopenia syndrome(SFTS)has continued to expand, posing a significant threat to public health. Currently, there are no specific antiviraltherapies or approved vaccines available, and management primarily relies on symptomatic and supportive care.Early identification of patients at risk of developing severe disease and timely targeted interventions are critical for reducing mortality. In this study, acute-phase serum antibodies, cytokines, and laboratory parameters from91 patients with SFTS were analyzed. Using machine learning approaches, four early-stage classificationmodels for distinguishing mild and severe cases were constructed, including multivariate logistic regression,random forest, support vector machine(SVM), and XGBoost models. During variable selection, univariatelogistic regression, multicollinearity diagnostics, and LASSO regression were sequentially applied, ultimatelyidentifying eight variables for model development: IL-10, IFN-γ, IFN-α, IL-8, AST, IgM OD450, TP, andLY%. Among the models, the random forest exhibited the best performance(AUC = 0.889), followed bymultivariate logistic regression(AUC = 0.822) and XGBoost(AUC = 0.811), whereas the support vectormachine demonstrated relatively lower performance(AUC = 0.744). In addition, multivariate logisticregression analysis identified IFN-γ, IL-8, IgM OD450, and LY% as independent predictors of progression tosevere SFTS. Variable importance analyses from both the random forest and XGBoost models indicated that IL-10 was likely the most critical predictive factor, with IFN-γ, IL-8, and AST also demonstrating highimportance. This study establishes an early-stage classification model for SFTS severity based on serological indicators combined with machine learning methods, offering new perspectives for early risk stratification and clinical management of SFTS.
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基本信息:
DOI：10.13242/j.cnki.bingduxuebao.250108
中图分类号:R512.8
引用信息:
[1]闻闫瀚,田婷婷,黄晓霞等.基于SFTS患者血清学指标与机器学习的轻重症分类模型研究[J].病毒学报,2025,41(03):702-712.DOI:10.13242/j.cnki.bingduxuebao.250108.
基金信息:
国家重点研发计划任务（项目号：2022YFC2303402）,题目：病原宿主互作机制研究及广谱新靶点发现~~