Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram
10.3346/jkms.2021.36.e248
- Author:
Hui jeong MOON
1
;
Kyunghoon KIM
;
Eun Kyeong KANG
;
Hyeon-Jong YANG
;
Eun LEE
Author Information
1. SCH Biomedical Informatics Research Unit, Soonchunhyang University Seoul Hospital, Seoul, Korea
- Publication Type:Original Article
- From:Journal of Korean Medical Science
2021;36(35):e248-
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. 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 nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/.
Conclusion: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.
