• Gustavo Dos Santos Ribeiro, Thomas Beltrame, Luís Fernando Deresz, Dominique Hansen, Piergiuseppe Agostoni, and Marlus Karsten.
• Programa de Pós-Graduação em Ciências da Reabilitação, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil.
• J Clin Monit Comput. 2023 Oct 1; 37 (5): 124712531247-1253.

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®. Five EOV definitions (Ben-Dov, Corrà, Kremser, Leite, and Sun definitions), two alternative approaches, one smoothing technique, and some basic statistics were incorporated into the interface to visualize four charts of the ventilatory response. EOV identification was based on a set of criteria verified from the interaction between amplitude, cycle length, and oscillation time. Two raters analyzed the datasets. In addition, repeated measurements were verified after six months using about 25% of the initial data. Cohen's kappa coefficient (κ) was used to investigate the reliability.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.© 2023. The Author(s), under exclusive licence to Springer Nature B.V.

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