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Two novel nomograms for predicting the risk of hospitalization or mortality due to COVID-19 by the naïve Bayesian

Eda Karaismailoglu1, Serkan Karaismailoglu2

  • 1Department of Medical Informatics, Gulhane Faculty of Medicine, University of Health Sciences, Ankara, Turkey.

Journal of Medical Virology
|February 18, 2021
PubMed

Insights

This study identified key risk factors for COVID-19 hospitalization and mortality using naive Bayesian nomograms. Pneumonia, age, and chronic conditions like kidney failure significantly predict severe outcomes in COVID-19 patients.

Area of Science:

  • Epidemiology
  • Medical Informatics
  • Public Health

Background:

  • Coronavirus disease 2019 (COVID-19) poses a global health challenge with complex risk factors impacting patient outcomes.
  • Effective management of COVID-19 requires accurate prediction of hospitalization and mortality risks to optimize resource allocation.

Purpose of the Study:

  • To identify significant risk factors associated with hospitalization and mortality in COVID-19 patients.
  • To develop and validate novel naive Bayesian nomograms for predicting COVID-19 patient outcomes.

Main Methods:

  • Analysis of a large national COVID-19 dataset (979,430 patients) from Mexico.
  • Utilized univariable logistic regression to identify potential risk factors.
  • Implemented naive Bayesian classifier for nomogram development and validated using AUC, CA, F1 score, precision, recall, and calibration plots.

Main Results:

  • Pneumonia, advanced age, chronic kidney failure, chronic obstructive respiratory disease, and diabetes were identified as primary risk factors for hospitalization and mortality.
  • The developed nomograms demonstrated strong predictive performance: hospitalization (AUC=0.896, CA=0.880) and mortality (AUC=0.903, CA=0.899).
  • 22.3% of patients required hospitalization, and 9.8% died during the study period.

Conclusions:

  • Novel naive Bayesian nomograms effectively predict COVID-19 hospitalization and mortality risk.
  • These tools can aid in individualized decision-making for newly diagnosed COVID-19 patients.
  • Identifying key risk factors like pneumonia and chronic diseases is crucial for managing the pandemic.

Keywords:
COVID-19hospitalizationmortalitynomogrampredictionrisk factor

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