J Korean Med Sci.  2021 Sep;36(35):e248. 10.3346/jkms.2021.36.e248.

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

• Moon Hj
• Kim K
• Kang EK
• Yang HJ
• Lee E

• KMID: 2519869
• DOI: http://doi.org/10.3346/jkms.2021.36.e248

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.

Keyword

COVID-19; Mortality; Nomogram; Underlying Diseases

Figure

• 

  Fig. 1 Prediction of mortality in COVID-19 patients. (A) Nomogram for predicting mortality in patients with COVID-19. (B) Calibration plot of the actual and predicted probabilities in the train and test sets. (C) AUC of the nomogram is 0.959 (95% CI, 0.945–0.973) in the train set and 0.926 (95% CI, 0.891–0.962) in the test set.COVID-19 = coronavirus disease 2019, AUC = the area under the curve, CI = confidence interval, DM = diabetes mellitus.

• 

  Fig. 2 Prediction of the 30-day and 60-day survival probabilities in COVID-19 patients. (A) Nomogram predicting the 30-day and 60-day survival probabilities in patients with COVID-19 in the train and test sets. Calibration plot of the actual and predicted probabilities for 30-day (B) and 60-day (C) survival in the train and test sets. The AUCs of the nomogram for predicting 30-day survival (D) and 60-day survival (E) in the train and test sets. DM = diabetes mellitus, ROC = receiver operating curve, AUC = area under the curve, COVID-19 = coronavirus disease 2019.

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