Jing Zhang, Yuntao Liu, Danwen Zheng, Gangfu Ye, Qiumin Chen, Jianshan Huang, Jiamei Wang, Zengming Ma, Zhongde Zhang
Department of Pulmonary and Critical Care Medicine, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, China.
Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Front Med (Lausanne). 2025 Apr 15;12:1500605. doi: 10.3389/fmed.2025.1500605. eCollection 2025.
This study aims to identify early warning indicators of COVID-19 severity by integrating modern medical biomarkers with traditional Chinese medicine (TCM) tongue features.
A retrospective observational study was conducted on 409 hospitalized COVID-19 patients from two centers in China. Patients were stratified into severe ( = 50) and non-severe ( = 359) groups based on the 10th edition of China's diagnostic guidelines. Data included demographics, clinical symptoms, tongue characteristics, and laboratory parameters. Univariate analyses (chi-square/Fisher's exact tests) and stepwise logistic regression were performed to identify key predictors.
Age ( < 0.001), fever ( < 0.001), elevated procalcitonin (PCT, < 0.001), thick tongue fur ( = 0.003), and fat tongue shape ( = 0.002) were significant predictors of severity. The combined model integrating these factors demonstrated superior predictive performance (Nagelkerke = 0.741).
Integrating TCM tongue features (thick fur and fat shape) with clinical biomarkers (age, fever, and PCT) enhances early identification of severe COVID-19, particularly in resource-limited settings.
本研究旨在通过整合现代医学生物标志物与中医舌象特征，确定新型冠状病毒肺炎（COVID-19）严重程度的早期预警指标。
对来自中国两个中心的409例住院COVID-19患者进行回顾性观察研究。根据中国第10版诊断指南，将患者分为重症组（n = 50）和非重症组（n = 359）。数据包括人口统计学、临床症状、舌象特征和实验室参数。进行单因素分析（卡方检验/费舍尔精确检验）和逐步逻辑回归以确定关键预测因素。
年龄（P < 0.001）、发热（P < 0.001）、降钙素原（PCT）升高（P < 0.001）、舌苔厚（P = 0.003）和舌形胖（P = 0.002）是严重程度的显著预测因素。整合这些因素的联合模型显示出更好的预测性能（Nagelkerke R2 = 0.741）。
将中医舌象特征（舌苔厚和舌形胖）与临床生物标志物（年龄、发热和PCT）相结合，可增强对重症COVID-19的早期识别，特别是在资源有限的环境中。
