Prediction of COVID-19-related mortality and 30-day and 60-day survival probabilities using a nomogram (Preprint)

Abstract

BACKGROUND Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. OBJECTIVE This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. METHODS This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. RESULTS Age ≥70 years; male; presence of fever and dyspnea at the time of COVID-19 diagnosis; and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively (C-index, 0.906; 95% CI, 0.883-0.929); the AUC for mortality of 0.926. The nomogram showed good calibration for survival probabilities and mortality. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/. CONCLUSIONS The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes. CLINICALTRIAL not applicable