Evidence map›Paper›PMID 36735189›Full record

ArticleJournal of clinical monitoring and computing2023

Software development to standardize the clinical diagnosis of exercise oscillatory ventilation in heart failure.

Gustavo Dos Santos Ribeiro, Thomas Beltrame, Luís Fernando Deresz, Dominique Hansen, Piergiuseppe Agostoni, Marlus Karsten

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Journal of clinical monitoring and computing, 2023. 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
1.6field-weighted citation impact, top 16% of its field
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, 4 citations in OpenAlex.

  1. Periodic breathing during exercise in heart failure: beyond the classic risk factors.European heart journal supplements : journal of the European Society of Cardiology · 2025
    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

6 authors at 5 institutions in 3 countries.

Gustavo Dos Santos RibeiroPrograma de Pós-Graduação em Ciências da Reabilitação, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil.
Thomas BeltrameSamsung R&D Institute Brazil - SRBR, Universidade Federal de São Carlos (UFSCAR), Campinas, SP, Brazil.
Luís Fernando DereszDepartamento de Educação Física, Universidade Federal de Juiz de Fora (UFJF), Governador Valadares, MG, Brazil.
Dominique HansenFaculty of Rehabilitation Sciences, Hasselt University (UHASSELT), BIOMED/REVAL, Hasselt, Belgium.
Piergiuseppe AgostoniCentro Cardiologico Monzino, IRCCS, Milan, Italy.
Marlus KarstenPrograma de Pós-Graduação em Ciências da Reabilitação, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil.
Universidade Federal de Ciências da Saúde de Porto Alegre · BRCentro Cardiologico Monzino · ITHasselt University · BEUniversidade Federal de Juiz de Fora · BRUniversidade Federal de São Carlos · BR

Funding

Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001
6 · The paper itself

Abstract

backgroundExercise oscillatory ventilation (EOV) is characterized by periodic oscillations of minute ventilation during cardiopulmonary exercise testing (CPET). Despite its prognostic value in chronic heart failure (HF), its diagnosis is complex due to technical limitations. An easier and more accurate way of EOV identification can contribute to a better approach and clinical diagnosis. This study aims to describe a software development to standardize the EOV diagnosis from CPET's raw data in heart failure patients and test its reliability (intra- and inter-rater).

methodsThe software was developed in the "drag-and-drop" G-language using LabVIEW

resultsOverall, 391 tests were analyzed in duplicate (inter-rater reliability) and 100 tests were randomized for new analysis (intra-rater reliability). High inter-rater (κ > 0.80) and intra-rater (κ > 0.80) reliability of the five EOV diagnoses were observed.

conclusionThe present study proposes novel semi-automated software to detect EOV in HF, with high inter and intra-rater agreements. The software project and its tutorial are freely available for download.

Indexed as

Heart FailurePulmonary VentilationCross-Sectional StudiesExercise TestHumansPrognosisReproducibility of ResultsRetrospective StudiesSoftwareCardiologyCardiopulmonary Exercise TestCardiovascular DiseasesPeriodic breathingPrognosis

Identifiers

PMID36735189
OpenAlexW4319060597

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.