ArticleAnnals of intensive care2022
Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.
Article in Annals of intensive care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 15 citations in OpenAlex.
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence: A Narrative Review.CHEST critical care · 2026Article
- Respiratory physiology after resupination following prone ventilation to predict 28-day mortality in mechanically ventilated patients: a machine learning analysis.Scientific reports · 2026Article
- Predicting Outcome and Duration of Mechanical Ventilation in Acute Hypoxemic Respiratory Failure: The PREMIER Study.Journal of clinical medicine · 2025Article
- Subphenotyping prone position responders with machine learning.Critical care (London, England) · 2025Observational
- Advances in Machine Learning for Mechanically Ventilated Patients.International journal of general medicine · 2025Review
- Article
- Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study.Journal of clinical medicine · 2024Article
- Changes in Driving Pressure vs Oxygenation as Predictor of Mortality in Moderate to Severe Acute Respiratory Distress Syndrome Patients Receiving Prone Position Ventilation.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2024Article
- Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study.Frontiers in immunology · 2024Article
- Validity of the total SOFA score in patients ≥ 80 years old acutely admitted to intensive care units: a post-hoc analysis of the VIP2 prospective, international cohort study.Annals of intensive care · 2023Article
- Dead space ventilation-related indices: bedside tools to evaluate the ventilation and perfusion relationship in patients with acute respiratory distress syndrome.Critical care (London, England) · 2023Review
Corrections and comments
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Authors and funding
52 authors at 20 institutions in 2 countries.
Funding
Abstract
backgroundFor mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources.
methodsFrom the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO
resultsThe median duration of prone episodes was 17 h (11-20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO
conclusionsIn mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.