Evidence map›Paper›PMID 41052875›Full record

ArticleBMJ open respiratory research2025

Bench evaluation of six non-invasive ventilation home ventilators: comparison with an ICU ventilator and unsupervised clustering.

Joris Pensier, Mathieu Capdevila, Dany Jaffuel, Abdelkebir Sabil, Fabrice Galia, Albert Prades, Aurélie Vonarb, Julien Boudjemaa, Audrey De Jong, Samir Jaber

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in BMJ open respiratory research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Joris PensierDepartment of Anesthesia and Intensive Care Unit, University Hospital of Montpellier, St-Eloi Hospital, University of Montpellier, Montpellier, France j-pensier@chu-montpellier.fr.ORCID http://orcid.org/0000-0002-3525-0128
Mathieu CapdevilaDepartment of Anesthesia and Intensive Care Unit, University Hospital of Montpellier, St-Eloi Hospital, University of Montpellier, Montpellier, France.
Dany JaffuelInserm U1046-CNRS 9214, Physiologie et Médecine Expérimentale Cœur et Muscle Université de Montpellier, Montpellier, France.
Abdelkebir SabilClinical Research, Cloud Sleep Lab, Paris, France.
Fabrice GaliaDepartment of Anesthesia and Intensive Care Unit, University Hospital of Montpellier, St-Eloi Hospital, University of Montpellier, Montpellier, France.
Albert PradesDepartment of Anesthesia and Intensive Care Unit, University Hospital of Montpellier, St-Eloi Hospital, University of Montpellier, Montpellier, France.
Aurélie VonarbDepartment of Anesthesia and Intensive Care Unit, University Hospital of Montpellier, St-Eloi Hospital, University of Montpellier, Montpellier, France.
Julien BoudjemaaDepartment of Anesthesia and Intensive Care Unit, University Hospital of Montpellier, St-Eloi Hospital, University of Montpellier, Montpellier, France.
Audrey De JongDepartment of Anesthesia and Intensive Care Unit, University Hospital of Montpellier, St-Eloi Hospital, University of Montpellier, Montpellier, France.
Samir JaberDepartment of Anesthesia and Intensive Care Unit, University Hospital of Montpellier, St-Eloi Hospital, University of Montpellier, Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionChronic hypercapnic respiratory failure often necessitates non-invasive ventilation (NIV) at home. Our study aimed to assess the static and dynamic performance of six modern NIV home ventilators and one intensive care unit (ICU) ventilator and to identify performance clusters among the devices.

methodsA two-compartment lung model was connected to seven NIV ventilators (Sefam Stan, Philips A40, Philips DreamStation, Resmed Lumis 150, Löwenstein PrismaVent 30C, Löwenstein PrismaVent 40 and BellaVista 1000) in pressure-support mode. Static and dynamic (triggering and pressurisation) performances were assessed through three distinct clinical phenotypes and four levels of unintentional leak. Clustering analysis was performed using K-means.

resultsFor each of the seven ventilators, 144 conditions were tested, and a total of 3024 cycles were analysed. Static and dynamic performances were good to excellent across home ventilators, significantly higher than the ICU ventilator. Clustering analysis identified three performance clusters. Cluster 1 (Sefam Stan and Philips A40) showed significantly more precise accuracy of inspiratory pressure than Cluster 2 (Philips DreamStation, Resmed Lumis 150, Löwenstein PrismaVent 30C and Löwenstein PrismaVent 40) and Cluster 3 (BellaVista 1000): mean error=4.3%±5.1% versus 8.5%±6.7% versus 10.6%±14.7% respectively, p<0.001. For the triggering delay, Cluster 1 displayed shorter delays than Cluster 2 and Cluster 3 (41±5 ms vs 58±11 ms vs 67±13 ms, respectively, p<0.001). For the pressurisation delay, Cluster 1 displayed shorter delays than Cluster 2 and Cluster 3 (42±6 ms vs 64±14 ms vs 87±14 ms, respectively, p<0.001). For the pressure-time product at 300 ms, Cluster 1 displayed higher area under the curve for the first 300 ms than Cluster 2 and Cluster 3 (2.1±1.1 cmH

conclusionThe six NIV home ventilators demonstrated superior performance compared with the tested ICU ventilator. The ventilators of Cluster 1 were identified as top performers in clustering analysis and compensated for unintentional continuous leaks.

Indexed as

Home Care ServicesIntensive Care UnitsNoninvasive VentilationRespiratory InsufficiencyVentilators, MechanicalCluster AnalysisEquipment DesignHumansAssisted VentilationMachine LearningNon invasive ventilation

Identifiers

PMID41052875
PMCPMC12551504

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LicenceCC BY-NC
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Registered trials

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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.