Chang Ruijie, Li Chenrui, Shi Dake, Hu Fan, Cai Yong, Wang Ying, Shen Tian
Public Health Research Center, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Shanghai, 200336, China.
Center for Community Health Care, China Hospital Development Institute, Shanghai Jiao Tong University, No. 227, South Chongqing Road, Shanghai, 200025, China.
Sci Rep. 2025 Sep 1;15(1):32074. doi: 10.1038/s41598-025-09470-5.
Patients with infectious diseases are often at increased risk of anxiety during treatment. The prevalence of anxiety and depression in infected people increased significantly during the COVID-19 pandemic, and the risk factors for these mental health problems need to be urgently investigated. In this study, a cross-sectional study was conducted in Shanghai in 2022, which included 1283 patients and systematically assessed their sociodemographic characteristics and mental health status. A random forest classifier combined with the Boruta algorithm was used to screen predictors, and a nomogram was constructed based on the screening results. The results of the study showed that entrapment (OR 1.07, 95% CI 1.05-1.09, P < 0.001), defeat (OR 1.04, 95% CI 1.01-1.07, P < 0.01) and stigma (OR 1.05, 95% CI 1.03-1.06, P < 0.001) were positively associated with anxiety, whereas social support (OR 0.97, 95% CI 0.96-0.98, P < 0.001) was negatively associated with anxiety. The C-index of the model was 0.858, the area under the ROC curve (AUC) was 0.861 (95% CI 0.834-0.888), and the P value of the Hosmer-Lemeshow test was 0.07, indicating that the model fit well. Based on the Random Forest machine learning method, this study successfully constructed a prediction model for anxiety risk in COVID-19 patients, screening out key risk factors such as feeling trapped, frustration, stigma and social support, providing a scientific basis for clinical practice and public health, and helping to promote personalized interventions for anxiety and the building of a mental health support system.
传染病患者在治疗期间往往焦虑风险增加。在新冠疫情期间，感染者中焦虑和抑郁的患病率显著上升，这些心理健康问题的风险因素亟待研究。本研究于2022年在上海进行了一项横断面研究，纳入1283例患者，并系统评估了他们的社会人口学特征和心理健康状况。采用随机森林分类器结合Boruta算法筛选预测因素，并根据筛选结果构建列线图。研究结果显示，被困感（比值比1.07，95%置信区间1.05 - 1.09，P < 0.001）、挫败感（比值比1.04，95%置信区间1.01 - 1.07，P < 0.01）和污名化（比值比1.05，95%置信区间1.03 - 1.06，P < 0.001）与焦虑呈正相关，而社会支持（比值比0.97，95%置信区间0.96 - 0.98，P < 0.001）与焦虑呈负相关。该模型的C指数为0.858，ROC曲线下面积（AUC）为0.861（95%置信区间0.834 - 0.888），Hosmer-Lemeshow检验的P值为0.07，表明模型拟合良好。基于随机森林机器学习方法，本研究成功构建了新冠患者焦虑风险预测模型，筛选出被困感、挫败感、污名化和社会支持等关键风险因素，为临床实践和公共卫生提供了科学依据，有助于推动焦虑症的个性化干预及心理健康支持体系的建立。
