COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm.
Giulia Lorenzoni1, Nicolò Sella2, Annalisa Boscolo3
1Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Padova, Italy.
Journal of Anesthesia, Analgesia and Critical Care
|June 29, 2023
Summary
Machine learning models accurately predict intensive care unit (ICU) mortality in coronavirus disease 2019 (COVID-19) patients. Age was the most significant predictor across all developed models, offering a reliable tool for clinical assessment.
Area of Science:
- Critical Care Medicine
- Machine Learning in Healthcare
- Infectious Diseases Epidemiology
Background:
- The early stages of the COVID-19 pandemic highlighted the need for predictive models due to uncertainties in diagnosis, treatment, and prognosis.
- Developing reliable tools to forecast patient outcomes is crucial for effective resource allocation and clinical decision-making in intensive care units (ICUs).
Purpose of the Study:
- To develop and validate a machine learning model for predicting ICU mortality in COVID-19 patients.
- To identify key clinical parameters that significantly influence mortality risk in critically ill COVID-19 patients.
Main Methods:
- An observational multicenter cohort study enrolled adult COVID-19 patients admitted to 25 ICUs within the VENETO ICU network.
- A SuperLearner machine learning algorithm was employed for model development, utilizing clinical variables such as age, comorbidities, and organ support.
- Internal validation used a training set (n=1293), and external validation was performed on two independent test sets (n=124 and n=199).
Main Results:
- Three distinct predictive models were developed, demonstrating comparable predictive performance with training balanced accuracy ranging from 0.72 to 0.90.
- Cross-validation performance varied between 0.75 and 0.85, indicating robust model generalizability.
- Age emerged as the most influential predictor of ICU mortality across all developed models.
Conclusions:
- The study successfully developed a reliable machine learning tool for predicting ICU mortality in COVID-19 patients.
- Age is identified as the primary clinical variable impacting mortality risk, underscoring its importance in risk stratification.
Related Concept Videos
01:07End Point Prediction: Gran Plot
388
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
388
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:21Cancer Survival Analysis
397
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
397
01:24Kaplan-Meier Approach
190
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
190
01:15Assumptions of Survival Analysis
157
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
157
02:07Improving Translational Accuracy
11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K