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

• Published in: Journal of clinical monitoring and computing 2023-10, Vol.37 (5), p.1247-1253
• Main Authors: dos Santos Ribeiro, Gustavo, Beltrame, Thomas, Fernando Deresz, Luís, Hansen, Dominique, Agostoni, Piergiuseppe, Karsten, Marlus
• Format: Article
• Language: English
• Subjects: Anesthesiology; Critical Care Medicine; Diagnosis; Health Sciences; Heart failure; Intensive; Medical diagnosis; Medicine; Medicine & Public Health; Original Research; Reliability analysis; Software; Software development; Statistics for Life Sciences; Ventilation
• ISSN: 1387-1307; 1573-2614
• DOI: 10.1007/s10877-023-00976-9

Background Exercise 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). Methods The 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. Results Overall, 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. Conclusion The 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.