Background: To investigate and select the useful prognostic parameters to develop and validate a model to predict the mortality risk for severely and critically ill patients with the coronavirus disease 2019 (COVID-19).
Methods: We established a retrospective cohort of patients with laboratory-confirmed COVID-19 (≥ 18 years old) from two tertiary hospitals: the People's hospital of Wuhan University and Leishenshan Hospital between February 16 , 2020, and April 14 , 2020 . The diagnosis of the cases was confirmed according to the WHO interim guidance . The data of consecutive severely and critically ill patients with COVID-19 admitted to these hospitals were analyzed . A total of 566 patients from the People's Hospital of Wuhan University were included in the training cohort and 436 patients from Leishenshan Hospital were included in the validation cohort . The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the mortality risk prediction model .
Results: The prediction model was resented as a nomograph and developed based on identified predictors, including age, chronic lung disease, C-reactive protein (CRP), D-dimer levels, neutrophil-to-lymphocyte ratio (NLR), creatine, and total bilirubin . In the training cohort, the model displayed good discrimination with an AUC of 0.912 [95% confidence interval (CI): 0.884-0.940] and good calibration (intercept = 0; slope = 1). In the validation cohort, the model had an AUC of 0.922 [95% confidence interval (CI): 0.891-0.953] and a good calibration (intercept = 0.056; slope = 1.161). The decision curve analysis (DCA) demonstrated that the nomogram was clinically useful . Conclusion: A risk score for severely and critically ill COVID-19 patients' mortality was developed and externally validated . This model can help clinicians to identify individual patients at a high mortality risk.
Index: COVID-19, Critical, Mortality, Predictive model, Risk, Severe