Evidence map›Paper›PMID 41120717›Full record

ArticleScientific reports2025

An objective method to detect sighs during cardio-pulmonary exercise testing.

Léon Genecand, Thomas Nibler, Ivan Guerreiro, Chloé Cantero, Cyril Jaksic, Sara Thorens, Marco Altarelli, Isabelle Frésard, Antoine Beurnier, David Montani and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Léon GenecandService de Pneumologie, Département de Médecine, Hôpitaux Universitaires de Genève, Genève, Switzerland. leon.genecand@hug.ch.ORCID http://orcid.org/0000-0001-6965-0480
Thomas NiblerDépartement d'Informatique, Faculté de Sciences, Université de Genève, Genève, Switzerland.
Ivan GuerreiroService de Pneumologie, Département de Médecine, Hôpitaux Universitaires de Genève, Genève, Switzerland.
Chloé CanteroService de Pneumologie, Département de Médecine, Hôpitaux Universitaires de Genève, Genève, Switzerland.
Cyril JaksicCentre de Recherche Clinique, Hôpitaux Universitaires de Genève, Genève, Switzerland.
Sara ThorensFaculté de Médecine, Université de Genève, Genève, Switzerland.
Marco AltarelliService de Pneumologie, Centre Hospitalier du Valais Romand, Hôpital du Valais, Sion, Switzerland.
Isabelle FrésardService de Pneumologie, Centre Hospitalier du Valais Romand, Hôpital du Valais, Sion, Switzerland.
Antoine BeurnierUniversité Paris-Saclay, School of Medicine, Le Kremlin-Bicêtre, France.
David MontaniUniversité Paris-Saclay, School of Medicine, Le Kremlin-Bicêtre, France.ORCID http://orcid.org/0000-0002-9358-6922
Anne BergeronService de Pneumologie, Département de Médecine, Hôpitaux Universitaires de Genève, Genève, Switzerland.
Frédéric LadorFaculté de Médecine, Université de Genève, Genève, Switzerland.
Pierre-Olivier BridevauxFaculté de Médecine, Université de Genève, Genève, Switzerland.ORCID http://orcid.org/0000-0002-8021-0950

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Excessive sighs have been described as one type of dysfunctional breathing (DB). Cardiopulmonary exercise testing (CPET) is one of the diagnostic options for DB and allows for a subjective evaluation of sighs. However, no validated method exists to automatically quantify sighs during CPET. We aimed to develop such a method. We used two Swiss cohorts of patients with persistent dyspnea after SARS-CoV-2 infection using CPET. In the derivation cohort (n = 48), we tested different filters to find the one that was the least influenced by outliers of tidal volume (VT) using a subjective approach. The selected filter (rolling median of 15 values) was applied in the validation cohort (n = 77) to detect spikes of VT above 2 times the value of the associated centered filtered value. Every automatically detected spike of VT from the cohort was analyzed by two experienced raters using continuous volume and flow-over-time graphs reconstructed from high resolution data acquisition. In the validation cohort, 203 automatically detected spikes of VT were visually analyzed by two raters. Of the 203 detected spikes, 199 corresponded to a sigh. The Cohen's Kappa (95% CI) between the raters was 0.89 (0.67-1). In conclusion, we developed a simple automated method for the objective quantification of sighs during CPET. It could be used to establish normative values of sighs during CPET and explore the associations between sighs and symptoms of patients with DB.

Indexed as

COVID-19DyspneaExercise TestAgedFemaleHumansMaleMiddle AgedSARS-CoV-2Tidal VolumeAbnormal breathing patternCardio-pulmonary exercise testingDysfunctional breathingSighs

Identifiers

PMID41120717
PMCPMC12540664

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.