Observational studyCritical care (London, England)2025
Subphenotyping prone position responders with machine learning.
Observational study in Critical care (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Prone positioning in ARDS.Intensive care medicine · 2026Review
- Advances in Imaging and Physiology-Guided Personalized Care in Acute Respiratory Distress Syndrome.Medicina (Kaunas, Lithuania) · 2026Review
- Physiology-guided personalized mechanical ventilation to prevent ventilator-induced lung injury.Frontiers in medicine · 2026Review
- Subphenotypes of mechanically ventilated acute respiratory distress syndrome patients based on multi-dimensional pathophysiological parameters.Critical care (London, England) · 2025Observational
- Clinical Phenotyping in Acute Respiratory Distress Syndrome: Steps Towards Personalized Medicine.Journal of clinical medicine · 2025Article
- Imaging and pulmonary function techniques in ARDS diagnosis and management: current insights and challenges.Critical care (London, England) · 2025Review
- Can we predict the future of respiratory failure prediction?Critical care (London, England) · 2025Review
- Artificial intelligence and machine learning in acute respiratory distress syndrome management: recent advances.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundAcute respiratory distress syndrome (ARDS) is a heterogeneous condition with varying response to prone positioning. We aimed to identify subphenotypes of ARDS patients undergoing prone positioning using machine learning and assess their association with mortality and response to prone positioning.
methodsIn this retrospective observational study, we enrolled 353 mechanically ventilated ARDS patients who underwent at least one prone positioning cycle. Unsupervised machine learning was used to identify subphenotypes based on respiratory mechanics, oxygenation parameters, and demographic variables collected in supine position. The primary outcome was 28-day mortality. Secondary outcomes included response to prone positioning in terms of respiratory system compliance, driving pressure, PaO
resultsThree distinct subphenotypes were identified. Cluster 1 (22.9% of whole cohort) had a higher PaO
conclusionsDistinct ARDS subphenotypes with varying mortality were identified in patients undergoing prone positioning; however, predicting which patients benefited from this intervention based on available data was not possible. These findings underscore the need for continued efforts in phenotyping ARDS through multimodal data to better understand the heterogeneity of this population.
Indexed as
Identifiers
What OpenQuestion holds
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.