A Risk Model for 28-Day in-Hospital Mortality in 173 COVID-19 Patients Admission to ICU: A Retrospective Study.

BACKGROUND

The surge in the number of patients diagnosed with COVID-19 since China's open-door policy has placed a huge burden on the public healthcare system, especially the intensive care system. This study's objective was to discover possible clinical outcome predictors in COVID-19 patients treated in intensive care units (ICUs) and to provide useful information for future preventative efforts and therapies.

METHODS

This retrospective study included 173 COVID-19 critically ill patients and reviewed the 28-day survival outcome in the First Affiliated Hospital of Nanjing Medical University. Competing risk analysis was performed to predict the cumulative incidence function (CIF) of mortality in hospital. The independent prognostic factors were identified by applying the Fine-Gray proportional subdistribution hazard model. Receiver operating characteristic (ROC) curves were used to evaluate model efficacy, and calibration curves were used to validate the model. Finally, we compared the competing risk model with the traditional proportional hazards model (Cox regression model) using CIF.

RESULTS

Of these 173 patients, 66 (38.2%) survived, 55 (31.8%) died, and 52 (30.0%) discharged. In univariate analysis, 12 variables were significantly correlated with mortality. In multivariate analysis, Age, Neutrophil ratio, Direct Bilirubin (DBIL) and Renal disease were independent predictors of 28-day outcome. The ROC curve of the multivariate prediction model showed an AUC (area under the curve) of 0.790. The results of the calibration curve and the concordance index (C-index) show that the model has good discriminatory power. The competing risk model we applied was more accurate than the Cox model.

CONCLUSION

We presented a more accurate multivariate prediction model for 28-day in-hospital mortality for ICU COVID-19 patients using a competing risk model.