Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study

• Main Authors: Yalan Yu, Tao Liu, Liang Shao, Xinyi Li, Colin K. He, Muhammad Jamal, Yi Luo, Yingying Wang, Yanan Liu, Yufeng Shang, Yunbao Pan, Xinghuan Wang, Fuling Zhou
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-01-01
• Series: Virulence
• Subjects: covid-19; serum amyloid a protein; disease progression; risk factor; predictor; biomarker
• Online Access: http://dx.doi.org/10.1080/21505594.2020.1840108
• ISSN: 2150-5594; 2150-5608

A pandemic designated as Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. Up to date, there is no efficient biomarker for the timely prediction of the disease progression in patients. To analyze the inflammatory profiles of COVID-19 patients and demonstrate their implications for the illness progression of COVID-19. Retrospective analysis of 3,265 confirmed COVID-19 cases hospitalized between 10 January 2020, and 26 March 2020 in three medical centers in Wuhan, China. Patients were diagnosed as COVID-19 and hospitalized in Leishenshan Hospital, Zhongnan Hospital of Wuhan University and The Seventh Hospital of Wuhan, China. Univariable and multivariable logistic regression models were used to determine the possible risk factors for disease progression. Moreover, cutoff values, the sensitivity and specificity of inflammatory parameters for disease progression were determined by MedCalc Version 19.2.0. Age (95%CI, 1.017 to 1.048; P < 0.001), serum amyloid A protein (SAA) (95%CI, 1.216 to 1.396; P < 0.001) and erythrocyte sedimentation rate (ESR) (95%CI, 1.006 to 1.045; P < 0.001) were likely the risk factors for the disease progression. The Area under the curve (AUC) of SAA for the progression of COVID-19 was 0.923, with the best predictive cutoff value of SAA of 12.4 mg/L, with a sensitivity of 83.9% and a specificity of 97.67%. SAA-containing parameters are novel promising ones for predicting disease progression in COVID-19.