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Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients

Qiao Yang1, Jixi Li2, Zhijia Zhang3

  • 1Department of Ultrasound, The 941st Hospital of the PLA Joint Logistic Support Force, Xining, People's Republic of China.

BMC Infectious Diseases
|August 10, 2021
PubMed

Insights

Identifying high-risk COVID-19 patients is crucial. A decision tree model using neutrophil-to-lymphocyte ratio, C-reactive protein, and lactic dehydrogenase accurately predicts death in severe cases.

Area of Science:

  • Infectious Diseases
  • Critical Care Medicine
  • Biomarkers

Background:

  • The COVID-19 pandemic necessitates identifying patients at high risk of mortality.
  • Early identification of severe cases improves clinical outcomes and resource allocation.

Purpose of the Study:

  • To develop a predictive model for mortality in COVID-19 patients.
  • To identify key clinical and laboratory factors associated with severe illness and death.

Main Methods:

  • Analysis of medical records from 2169 adult COVID-19 patients in Wuhan, China.
  • Development of a decision tree model using neutrophil-to-lymphocyte ratio, C-reactive protein, and lactic dehydrogenase.
  • Validation of the model on training and test datasets.

Main Results:

  • Severe illness and mortality were associated with older age and higher proportion of males.
  • Significant differences in clinical and laboratory markers were observed between severe/non-severe and survivor/non-survivor groups.
  • The decision tree model achieved 0.98 accuracy in predicting death in severe COVID-19 patients.

Conclusions:

  • A simple, clinically operable decision tree model can rapidly identify COVID-19 patients at high risk of death.
  • Prioritized treatment and intensive care for high-risk patients can improve survival rates.
  • The model aids clinicians in timely decision-making for critical COVID-19 cases.
Abstract

Keywords:
C-reactive proteinCOVID-19Decision treeLactic dehydrogenaseNeutrophil-to-lymphocyte ratio

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