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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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

Hui Jeong Moon1,2, Kyunghoon Kim3, Eun Kyeong Kang4

  • 1SCH Biomedical Informatics Research Unit, Soonchunhyang University Seoul Hospital, Seoul, Korea.

Journal of Korean Medical Science
|September 7, 2021
PubMed

Insights

This study identified key factors for predicting COVID-19 mortality, developing a nomogram to assess patient risk. The model accurately predicts mortality, aiding clinical decisions and policy-making during the pandemic.

Area of Science:

  • Medical research
  • Epidemiology
  • Public Health

Background:

  • The COVID-19 pandemic has overwhelmed healthcare systems globally.
  • Predicting mortality in patients with coronavirus disease 2019 (COVID-19) is crucial for improving clinical outcomes.
  • Identifying risk factors and developing predictive models for COVID-19 mortality is essential.

Purpose of the Study:

  • To identify factors associated with COVID-19 mortality.
  • To develop a nomogram for predicting mortality in COVID-19 patients.
  • To utilize clinical parameters and underlying diseases for accurate mortality prediction.

Main Methods:

  • A Cox proportional hazards model and logistic regression were used.
  • A nomogram was constructed to predict 30-day and 60-day survival probabilities and overall mortality.
  • Model validation was performed using calibration and discrimination in a test set of 5,626 patients.

Main Results:

  • Age ≥ 70, male sex, fever, dyspnea, diabetes, cancer, and dementia were significant predictors of mortality.
  • The nomogram demonstrated good calibration and discrimination, with high Areas Under the Curve (AUCs) for predicting survival and mortality.
  • AUCs in the train set were 0.914 (30-day survival), 0.954 (60-day survival), and 0.959 (mortality). Test set AUCs were 0.876 (30-day survival), 0.660 (60-day survival), and 0.926 (mortality).

Conclusions:

  • The developed prediction model accurately predicts COVID-19-related mortality.
  • The nomogram is a valuable tool for identifying high-risk patients.
  • This tool can inform medical policies and improve clinical outcomes during the pandemic.
Abstract

Keywords:
COVID-19MortalityNomogramUnderlying Diseases

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