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Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

Harry Magunia1, Simone Lederer2, Raphael Verbuecheln2

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Critical Care (London, England)
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Machine learning accurately predicts COVID-19 ICU patient survival and outcomes, identifying age, inflammation, and ARDS severity as key factors. This approach overcomes limitations of traditional models for better risk stratification.

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Area of Science:

  • Critical Care Medicine
  • Machine Learning in Healthcare
  • Infectious Disease Epidemiology

Background:

  • Intensive Care Units (ICUs) faced significant strain during the COVID-19 pandemic.
  • Existing methods for stratifying SARS-CoV-2 patient risk and predicting ICU outcomes were insufficient.
  • Accurate prediction models are crucial for managing critical COVID-19 cases.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting clinical outcomes in ICU patients with COVID-19.
  • To stratify patient risk upon ICU admission.
  • To predict ICU survival and specific outcomes like need for ECMO or renal replacement therapy.

Main Methods:

  • Established a Germany-wide electronic registry for SARS-CoV-2 ICU patients, collecting retrospective and prospective data.
  • Evaluated various machine learning approaches for predictive accuracy and interpretability.
  • Selected the Explainable Boosting Machine (EBM) as the optimal method, reporting individual parameter functions and interactions.

Main Results:

  • Included 1039 patients (596 retrospective, 443 prospective) in the EBM model.
  • The EBM model reliably predicted patient survival, with age, inflammatory markers, thrombotic activity, and ARDS severity identified as key predictors.
  • Predictors for ECMO therapy included patient age, pulmonary dysfunction, and external transfer; predictors for renal replacement therapy involved age, creatinine, and SOFA score interactions.

Conclusions:

  • Explainable Boosting Machine analysis confirmed known and identified novel predictors for outcomes in critically ill COVID-19 patients.
  • This machine learning strategy effectively models COVID-19 ICU patient outcomes, surpassing limitations of linear regression.
  • Trial registration: NCT04455451.