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Survival prediction algorithms for COVID-19 patients admitted to a UK district general hospital

Ancy Fernandez1, Nonyelum Obiechina1, Justin Koh1

  • 1Medicine Department, Queen's Hospital, Burton-on-Trent, UK.

International Journal of Clinical Practice
|December 28, 2020
PubMed

Insights

This study developed a predictive algorithm using patient data to identify individuals likely to survive COVID-19. Key factors include age, oxygen levels, CRP, platelets, and lung consolidation.

Area of Science:

  • Medical research
  • Clinical informatics
  • Epidemiology

Background:

  • COVID-19 poses a significant threat, necessitating tools to predict patient outcomes.
  • Accurate prognostication aids in resource allocation and treatment strategies for severe infections.

Purpose of the Study:

  • To develop a predictive algorithm for COVID-19 patient survival.
  • To identify key clinical markers associated with mortality in hospitalized COVID-19 patients.

Main Methods:

  • Retrospective analysis of 487 consecutive COVID-19 patient admissions.
  • Manual extraction of data from electronic patient records.
  • Development of a logistic regression model incorporating clinical variables.

Main Results:

  • Older age, lower oxygen saturation (SpO2), higher inspired oxygen (IpO2), and elevated C-reactive protein (CRP) were associated with mortality.
  • Platelet count, while not significant alone, improved the predictive model.
  • The developed 5-parameter algorithm demonstrated strong predictive accuracy (AUC = 0.8129).

Conclusions:

  • A predictive model incorporating age, IpO2, CRP, platelets, and lung consolidation effectively identifies patients likely to survive COVID-19.
  • This algorithm can assist clinicians in risk stratification and management decisions.
  • Further validation of the predictive model in diverse populations is warranted.
Abstract

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