ArticleCritical care (London, England)2022
Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes.
Article in Critical care (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Subphenotypic classification of COVID-19 survivors and response to telerehabilitation: a latent class analysis.Journal of rehabilitation medicine · 2025Trial
- The Acute Respiratory Distress Syndrome (ARDS): epidemiology, etiology, molecular mechanisms, diagnosis and therapeutic strategies.Molecular biomedicine · 2026Review
- Right ventricular-protective ventilation in acute respiratory distress syndrome: phenotypes, monitoring and ventilatory management.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Sex-related differences in lung injury distribution and outcomes in COVID-19 acute respiratory failure: insights from the CT-COVID19 multicenter study group.Intensive care medicine experimental · 2026Article
- Computed tomography in ARDS, from morphological insights to AI-powered multi-modal analysis: a narrative review.Journal of intensive care · 2026Review
- Characterizing heterogeneity and subphenotyping acute respiratory distress syndrome with computed tomography.Intensive care medicine experimental · 2026Review
- Beyond One-Size-Fits-All: Precision Mechanical Ventilation in ARDS.Journal of clinical medicine · 2026Review
- Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence: A Narrative Review.CHEST critical care · 2026Article
- Advances in Imaging and Physiology-Guided Personalized Care in Acute Respiratory Distress Syndrome.Medicina (Kaunas, Lithuania) · 2026Review
- Multimodal phenotyping of ARDS: design and preliminary insights from the prospective BIOWARE cohort for precision critical management.Respiratory research · 2026Article
- Morphological subphenotypes of acute pancreatitis-related acute respiratory distress syndrome.Critical care (London, England) · 2026Article
- Multimorbidity patterns are associated with postoperative delirium in older patients undergoing non-cardiac surgery: an observational study.Frontiers in medicine · 2026Article
- Clinical Phenotyping in Acute Respiratory Distress Syndrome: Steps Towards Personalized Medicine.Journal of clinical medicine · 2025Article
- Two Physiologic Latent Classes of Acute Hypoxemic Respiratory Failure in Sepsis Are Distinguished by Lung Mechanics and Gas Exchange.Critical care explorations · 2025Article
- Imaging and pulmonary function techniques in ARDS diagnosis and management: current insights and challenges.Critical care (London, England) · 2025Review
- Artificial intelligence and machine learning in acute respiratory distress syndrome management: recent advances.Frontiers in medicine · 2025Review
- Determination of positive end-expiratory pressure in COVID-19-related acute respiratory distress syndrome: A systematic review.European journal of anaesthesiology and intensive care · 2024Article
- Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position.Annals of intensive care · 2024Article
- Identification of novel sub-phenotypes of severe ARDS requiring ECMO using latent class analysis.Critical care (London, England) · 2024Observational
- Highlights from the Respiratory Failure and Mechanical Ventilation Conference 2024.Breathe (Sheffield, England) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with COVID-19-related acute respiratory distress syndrome (ARDS) require respiratory support with invasive mechanical ventilation and show varying responses to recruitment manoeuvres. In patients with ARDS not related to COVID-19, two pulmonary subphenotypes that differed in recruitability were identified using latent class analysis (LCA) of imaging and clinical respiratory parameters. We aimed to evaluate if similar subphenotypes are present in patients with COVID-19-related ARDS.
methodsThis is the retrospective analysis of mechanically ventilated patients with COVID-19-related ARDS who underwent CT scans at positive end-expiratory pressure of 10 cmH
results99 patients were included. Using 12 variables, a two-class LCA model was identified as best fitting. Subphenotype 2 (recruitable) was characterized by a lower PaO
conclusionsA recruitable and non-recruitable subphenotype were identified in patients with COVID-19-related ARDS. These findings are in line with previous studies in non-COVID-19-related ARDS and suggest that a combination of imaging and clinical respiratory parameters could facilitate the identification of recruitable lungs before the manoeuvre.
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.