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A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine

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This study developed an effective COVID-19 risk prediction model using machine learning, creating a user-friendly tool to help clinicians assess ICU admission or mortality risk in hospitalized patients.

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Area of Science:

  • * Medical Informatics
  • * Computational Epidemiology
  • * Clinical Decision Support

Background:

  • * COVID-19 pandemic led to significant hospitalizations and mortality.
  • * Accurate risk stratification is crucial for optimal patient management and resource allocation.
  • * Existing predictive models may not fully capture the complexity of disease progression across pandemic waves.

Purpose of the Study:

  • * To develop and validate a predictive model for Intensive Care Unit (ICU) admission or mortality in hospitalized COVID-19 patients.
  • * To create a user-friendly clinical decision support tool based on the predictive model.
  • * To analyze a comprehensive dataset encompassing multiple pandemic waves.

Main Methods:

  • * Retrospective cohort study of 3623 hospitalized COVID-19 patients from February 2020 to January 2021.
  • * Analysis of up to 165 variables including demographics, comorbidities, medications, vital signs, and laboratory data.
  • * Machine learning algorithms (multilayer perceptron, random forest, XGBoost) applied after dimensionality reduction to 20 features.

Main Results:

  • * Extreme Gradient Boosting (XGBoost) demonstrated the best performance.
  • * The final model achieved strong external validation with an Area Under the Curve (AUC) of 0.821 (95% CI 0.787-0.854).
  • * The model exhibited accurate calibration (slope=1, intercept=-0.12) and a cut-off of 0.4 yielded 71% sensitivity and 78% specificity.

Conclusions:

  • * A robust COVID-19 risk prediction model was successfully developed using extensive data from multiple pandemic waves.
  • * The model demonstrates good calibration and discrimination, proving effective for clinical application.
  • * A web-based application was created to facilitate rapid clinical decision-making regarding patient risk stratification.