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Development of a Predictive Model for Mortality in Hospitalized Patients With COVID-19

Yuanyuan Niu1, Zan Zhan2, Jianfeng Li3

  • 1Department of Respiratory Medicine, The Eastern Hospital of The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China.

Disaster Medicine and Public Health Preparedness
|January 8, 2021
PubMed

Insights

Early identification of high-risk COVID-19 patients is crucial. A nomogram using SOFA score, diabetes, and LDH levels accurately predicts mortality risk, aiding clinical decisions for novel coronavirus disease 2019.

Area of Science:

  • Medical research
  • Infectious diseases
  • Critical care medicine

Background:

  • Early identification of patients with high mortality risk for novel coronavirus disease 2019 (COVID-19) is critical.
  • Clinical characteristics and outcomes of COVID-19 patients require detailed analysis to identify mortality predictors.

Purpose of the Study:

  • To identify risk factors associated with in-hospital death in COVID-19 patients.
  • To develop a predictive model (nomogram) for assessing the risk of mortality in COVID-19 patients.

Main Methods:

  • Retrospective study of 150 COVID-19 patients (January 23 - March 5, 2020).
  • Comparison of clinical characteristics and outcomes between survivors and non-survivors.
  • Univariable and multivariable logistic regression analysis to identify risk factors for in-hospital death, followed by nomogram construction.

Main Results:

  • Multivariable analysis identified higher Sequential Organ Failure Assessment (SOFA) score, diabetes, and lactate dehydrogenase (LDH) > 245 U/L as significant predictors of in-hospital death.
  • The developed nomogram demonstrated good accuracy in predicting mortality risk, with an Area Under the Curve (AUC) of 0.970.

Conclusions:

  • The study successfully developed a nomogram for early identification of COVID-19 patients at high risk of fatal outcomes.
  • This predictive tool can aid clinicians in timely intervention and management of critically ill COVID-19 patients.
Abstract

Keywords:
COVID-19in-hospital deathmortalitypredictive modelrisk factors

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