A predictive model for respiratory distress in patients with COVID-19: a retrospective study

Background: Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis. We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19. Methods: We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission. These variables were analyzed using Kaplan-Meier and Cox proportional hazards (PHs) analyses. I Lazard ratios (HRs) and performance of the final model were determined. Results: Neutrophil count >6.3x10(9)/L, D-dimer level >= 1.00 mg/L, and temperature >= 37.3 degrees C at admission showed significant positive association with the outcome of respiratory distress in the final model. Complement C3 (C3) of 0.9-1.8 g/L, platelet count >350x10(9)/L, and platelet count of 125-350x10(9)/L showed a significant negative association with outcomes of respiratory distress in the final model. The final model had a C statistic of 0.891 (0.867-0.915), an Akaike's information criterion (AIC) of 567.65, and a bootstrap confidence interval (CI) of 0.866 (0.842-0.89). This five-factor model could help in early allocation of medical resources. Conclusions: The predictive model based on the five factors obtained at admission can be applied for calculating the risk of respiratory distress and classifying patients at an early stage. Accordingly, high-risk patients can receive timely and effective treatment, and health resources can be allocated effectively.