Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Observational Study
. 2025 Mar 14;29(1):116.
doi: 10.1186/s13054-025-05340-8.

Subphenotyping prone position responders with machine learning

Maxime Fosset #  1   2   3   4 , Dario von Wedel #  1   2   5 , Simone Redaelli #  1   2   6   7 , Daniel Talmor  1   2 , Nicolas Molinari  4 , Julie Josse  4 , Elias N Baedorf-Kassis  8 , Maximilian S Schaefer  1   2   9 , Boris Jung  10   11   12   13
Affiliations
Observational Study

Subphenotyping prone position responders with machine learning

Maxime Fosset et al. Crit Care. .

Abstract

Background: Acute 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.

Methods: In 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, PaO2/FiO2 ratio, ventilatory ratio, and mechanical power.

Results: Three distinct subphenotypes were identified. Cluster 1 (22.9% of whole cohort) had a higher PaO2/FiO2 ratio and lower Positive End-Expiratory Pressure (PEEP). Cluster 2 (51.3%) had a higher proportion of COVID-19 patients, lower driving pressure, higher PEEP, and higher respiratory system compliance. Cluster 3 (25.8%) had a lower pH, higher PaCO2, and higher ventilatory ratio. Mortality differed significantly across clusters (p = 0.03), with Cluster 3 having the highest mortality (56%). There were no significant differences in the proportions of responders to prone positioning for any of the studied parameters. Transpulmonary pressure measurements in a subcohort did not improve subphenotype characterization.

Conclusions: Distinct 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.

Keywords: ARDS; Clustering; Machine Learning; Phenotypes; Precision Medicine; Prone Position.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethical approval and consent to participate: This study was approved by the Institutional Review Board of Beth Israel Deaconess Medical Center (2024P000813), and the requirement for informed consent was waived. Consent for publication: Not applicable. Competing interests: M.S.S. received funding for investigator-initiated studies from Merck & Co., which do not pertain to this manuscript. He is an associate editor for BMC Anesthesiology. He received honoraria for lectures from Fisher & Paykel Healthcare and Mindray Medical International Limited. He received an unrestricted philanthropic grant from Jeffrey and Judith Buzen. E.N.B-K. has received lecturing fees from Hamilton Medical Inc. outside the submitted work and has received a KL2 award from Harvard Catalyst; The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health award No. KL2 TR002542). The funders had no role in the design and conduct of the study, the collection, management, analysis, and interpretation of the data, the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. All authors declare no conflicts of interest related to this publication.

Figures

Fig. 1
Fig. 1
Flowchart
Fig. 2
Fig. 2
Profiles of different phenotypes according to the most important variables used to determine clusters. Data were normalized to have a mean of 0 and a standard deviation of 1 across all phenotypes. When multiple values were present for a given variable, they were summarized by its median value. Abbreviations: VR ventilatory ratio; Vt tidal volume; BMI body mass index; Crs respiratory system compliance, PF PaO2/FiO2 ratio; DP driving pressure; pH arterial pH; PaCO2 arterial pressure in CO2; PEEP positive end-expiratory pressure; MP mechanical power; Pplat plateau pressure; RR respiratory rate
See this image and copyright information in PMC

References

    1. Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315:788–800. - DOI - PubMed
    1. Matthay MA, Arabi Y, Arroliga AC, Bernard G, Bersten AD, Brochard LJ, et al. A new global definition of acute respiratory distress syndrome. Am J Respir Crit Care Med. 2024;209:37–47. - DOI - PMC - PubMed
    1. Thompson BT, Chambers RC, Liu KD. Acute respiratory distress syndrome. N Engl J Med. 2017;377:562–72. - DOI - PubMed
    1. Grasselli G, Calfee CS, Camporota L, Poole D, Amato MBP, Antonelli M, et al. ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies. Intensive Care Med. 2023;49:727–59. - DOI - PMC - PubMed
    1. Reddy K, Calfee CS, McAuley DF. Acute respiratory distress syndrome subphenotypes beyond the syndrome: a step toward treatable traits? Am J Respir Crit Care Med. 2021;203:1449–51. - DOI - PMC - PubMed

Publication types

LinkOut - more resources