Identification of COVID-19 Clinical Phenotypes by Principal Component Analysis-Based Cluster Analysis
Wenjing Ye, Weiwei Lu, Yanping Tang, Guoxi Chen, Xiaopan Li, Chen Ji, Min Hou, Guangwang Zeng, Xing Lan, Yaling Wang, Xiaoqin Deng, Yuyang Cai, Hai Huang, Ling Yang
Abstract
Background: COVID-19 has been quickly spreading, making it a serious public health threat. It is important to identify phenotypes to predict the severity of disease and design an individualized treatment. Methods: We collected data from 213 COVID-19 patients in Wuhan Pulmonary Hospital from January 1 to March 30, 2020. Principal component analysis (PCA) and cluster analysis were used to classify patients. Results: We identified three distinct subgroups of COVID-19. Cluster 1 was the largest group (52.6%) and characterized by oldest age, lowest cellular immune function and albumin levels. 38.5% of subjects were grouped into Cluster 2. Most of the lab results in Cluster 2 fell between those of Clusters 1 and 3. Cluster 3 was the smallest cluster (8.9%), characterized by youngest age and highest cellular immune function. The incidence of respiratory failure, acute respiratory distress syndrome (ARDS), heart failure and usage of non-invasive mechanical ventilation in Cluster 1 was significantly higher than others (P<0.05). Cluster 1 had the highest death rate of 30.4% (P=0.005). Although there were significant differences in age between Clusters 2 and 3 (P<0.001), we found that there was no difference in demand for medical resources. Conclusion: We identified three distinct clusters of the COVID-19 patients. The results show that age alone could not be used to assess a patient's condition. Specifically, management of albumin and immune function are important in reducing the severity of disease.