A novel simple scoring model for predicting severity of patients with SARS-CoV-2 infection

An outbreak of pneumonia caused by a novel coronavirus (COVID-19) began in Wuhan, China in December 2019 and quickly spread throughout the country and world. An efficient and convenient method based on clinical characteristics was needed to evaluate the potential deterioration in patients. We aimed to develop a simple and practical risk scoring system to predict the severity of COVID-19 patients on admission. We retrospectively investigated the clinical information of confirmed COVID-19 patients from 10 February 2020 to 29 February 2020 in Wuhan Union Hospital. Predictors of severity were identified by univariate and multivariate logistic regression analysis. A total of 147 patients with confirmed SARS-CoV-2 infection were grouped into non-severe (94 patients) and severe (53 patients) groups. We found that an increased level of white blood cells (WBC), neutrophils, D-dimer, fibrinogen (FIB), IL-6, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), alanine aminotransferase (ALT), aspartate aminotransferase (AST), α-hydroxybutyrate dehydrogenase (HBDH), serum amyloid A (SAA) and a decreased level of lymphocytes were important risk factors associated with severity. Furthermore, three variables were used to formulate a clinical risk scoring system named COVID-19 index = 3 × D-dimer (µg/L) + 2 × lgESR (mm/hr) - 4 × lymphocyte (×10⁹ /L) + 8. The area under the receiver operating characteristic (ROC) curve was 0.843 (95% CI, 0.771-0.914). We propose an effective scoring system to predict the severity of COVID-19 patients. This simple prediction model may provide healthcare workers with a practical method and could positively impact decision-making with regard to deteriorating patients.