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Identification of COVID-19 Clinical Phenotypes by Principal Component Analysis-Based Cluster Analysis
20 Pages Posted: 18 May 2020
More...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.
Findings: 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.
Interpretation: 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.
Funding Statement: The study was supported by Zhejiang University special scientific research fund for COVID-19 prevention and control, [grant number 2020XGZX065].
Declaration of Interests: The authors declared no competing interests.
Ethics Approval Statement: The National Health Commission of the People's Republic of China has determined that data collection and analysis of cases and close contacts are part of ongoing investigations into outbreaks of public health events and are therefore exempt from the approval requirements of the institutional review board. Informed consent was exempted with the approval of Medical Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China (No. XHEC-D-2020-052).
Keywords: COVID-19; phenotype; treatment; principal component analysis; cluster analysis
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