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

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.

Diagnostic performance of SARC-F and SARC-CalF in screening for sarcopenia in older adults in Northern Brazil.

To compare the performance of SARC-F and SARC-CalF as screening tools for sarcopenia. Cross-sectional study with a convenience sample of 312 community-dwelling older people. Sarcopenia was defined as low handgrip strength (HGS) or low gait speed (GS ≤ 0.8 m/s). HGS was measured by dynamometry and GS by the 4-m walking speed test. For HGS, six criteria (C) were used to identify sarcopenia in men/women: CI: < 27 kg/16 kg; CII: < 35.5 kg/20.0 kg; CIII: grip over body mass index < 1.05/< 0.79; CIV: grip strength over total body fat < 1.66/< 0.65; CV: grip over bodyweight < 0.45/< 0.34; CVI: < 27 kg/16 kg and low skeletal muscle mass index (SMMI); CI and CVI defined according to the European Working Group on sarcopenia in older people and the rest according to the sarcopenia definition and outcomes Consortium. For sarcopenia screening, the SARC-F (≥ 4 points) and the SARC-CalF (≥ 11 points) were used. The kappa analysis revealed no agreement between the SARC-F and the various criteria for the identification of sarcopenia in men. The same lack of agreement was observed in women with some exceptions: CI = 0.161 ± 0.074, p = 0.020; GS = 0.209 ± 0.076, p = 0.003. Concerning the Cohen's kappa between the SARC-Calf and the reference criteria of sarcopenia, the following coefficients were observed as significant for women: CI = 0.201 ± 0.069, p = 0.003; CII = 0.186 ± 0.064, p = 0.005; GS = 0.273 ± 0.068, p = 0.0001; and for men: CII = 0.139 ± 0.053, p = 0.021; GS = 0.223 ± 0.099, p = 0.011. ROC curves revealed the SARC-Calf with acceptable discrimination and reasonable sarcopenia predictive capacity considering a cutoff value of 10.5 in both men (AUC: 67.5%, p = 0.022; Se = 52.9%; Sp = 76.8%) and women (AUC: 72.4%, p < 0.001; Se = 63%; Sp = 68.5%) concerning GS. The SARC-CalF performed better than the SARC-F for screening sarcopenia in the population ≥ 60 years of age in the Amazonas, measured through walking slowness.