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. Hazard ratios (HRs) and performance of the final model were determined. Results Neutrophil count >6.3×109/L, D-dimer level ≥1.00 mg/L, and temperature ≥37.3 °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 >350×109/L, and platelet count of 125-350×109/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.