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A Structured Approach to Extubation in Mechanically Ventilated Rats
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Predictors for extubation failure in COVID-19 patients using a machine learning approach
Lucas M Fleuren1, Tariq A Dam2, Michele Tonutti3
1Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands. l.fleuren@amsterdamumc.nl.
Critical Care (London, England)
|December 28, 2021
Summary
Predictors for extubation failure in critically ill COVID-19 patients were identified. Ventilatory settings, inflammatory markers, neurological status, and BMI are key indicators for successful extubation.
Area of Science:
- Intensive Care Medicine
- Pulmonology
- Infectious Diseases
Background:
- Extubation timing in intensive care is challenging.
- Identifying predictors for extubation failure in COVID-19 patients is crucial.
Purpose of the Study:
- To identify predictors for extubation failure in critically ill COVID-19 patients.
Main Methods:
- Utilized granular data from 3464 COVID-19 patients.
- Applied machine learning algorithms with nested cross-validation.
- Estimated predictor importance using Shapley additive explanations.
Main Results:
- Gradient-boost model showed an AUC of 0.70.
- Key predictors include ventilatory settings (controlled mode duration, FiO2, tidal volume), inflammatory markers (CRP, leukocytes, thrombocytes), neurological status (GCS), and BMI.
- Reintubation rate was 13.4% within 48 hours and 18.9% at day 7.
Conclusions:
- Ventilatory settings, inflammatory parameters, neurological status, and BMI are significant predictors of extubation failure.
- Routine capture of these predictors in electronic health records is recommended.
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