The cardiopulmonary exercise (CPET) test is an essential tool to determine the severity, prognosis, and need for invasive treatments in heart failure with reduced ejection fraction (HFrEF) but disregards the exercise modality. The present study aimed at analyzing the differences between treadmill and cycle-ergometer exercises. This was a prospective study, involving 65 patients with HfrEF who performed treadmill exercise followed by cycle-ergometer exercise 72 h later. We enrolled 65 patients, aged 58 ± 9 years, with an ejection fraction of 29 ± 9%. Peak VO₂ was 20% greater (95% CI: 18–21%; p < 0.000) on the treadmill, and the ventilatory efficiency estimated by the VE/VCO₂ slope (32 ± 8 vs. 34 ± 9; p < 0.05). The ventilatory response was greater on the treadmill: maximum ventilation (55 ± 16 vs. 46 ± 11 L/min; p < 0.000) and ventilatory reserve at the maximum effort (28 ± 17 vs. 41 ± 15%; p < 0.000). These values led to a change in the functional class of 23 (51%) patients and ventilatory class of 28 (47%) patients. Differences in the main parameters, including peak VO₂ and VE/VCO₂, impact prognostic scales and possible advanced treatments; therefore, the results should be interpreted in accordance with the exercise modality.

Obesity hypoventilation syndrome (OHS) is treated with non-invasive ventilation (NIV) that is titrated during polysomnography. Auto-adjusted NIV could obviate the need for polysomnographic titration, thereby reducing costs and delays in care. However, non-inferiority long-term clinical trials comparing auto-adjusted NIV with manually-adjusted NIV are lacking. To determine the comparative effectiveness of automatic vs manual NIV modality in OHS. In this multicenter, blinded, parallel group, non-inferiority and cost-effectiveness trial, we randomly assigned treatment-naïve ambulatory patients with OHS to auto-adjusted NIV (volume-targeted pressure support with auto-EPAP) or manually-adjusted NIV (bilevel PAP ST mode). The primary outcome was change in daytime PaCO₂ at 12 months, with the non-inferiority premise set at -2 mmHg. Secondary outcomes included symptoms, quality of life, and healthcare resource utilization. Intention-to-treat and per-protocol analyses were performed. 205 ambulatory patients with OHS were randomized, 107 to auto-adjusted NIV and 89 to manually-adjusted NIV. The mean [95% CI] improvement in PaCO₂ was -9.2 [-9.7;-8.7] mmHg in the auto-adjusted group and -8.7 [-9.1;-8.3] mmHg in the manually-adjusted group, with mean adjusted difference of 0.15 mmHg between groups ([low confidence limit -1.4]; non-inferiority P = 0.01). Cost-effectiveness was favorable to auto-adjusted group with a saving of 15287€(95% CI: -2370; -6854) per patient. There were no significant differences in other secondary outcomes. In ambulatory patients with OHS, auto-adjusted NIV had a non-inferior long-term effectiveness compared to manually-adjusted NIV while being more cost-effective. Auto-adjusted NIV may be preferred in clinical practice given its lower complexity and cost.

Background: Heart failure with reduced ejection fraction (HFrEF) frequently coexists with chronic obstructive pulmonary disease (COPD), and both conditions share symptoms such as exertional dyspnea. The cardiopulmonary exercise test (CPET) is an essential tool for assessing ventilatory and cardiovascular function and plays a key role in the differential diagnosis of dyspnea. However, the impact of exercise modality on the ventilatory and cardiovascular parameters obtained remains unclear in these groups. Our aim is to compare the oxygen consumption (V^·O₂) and breathing reserve (BR) values obtained from CPET on a treadmill and a cycle ergometer in patients with HFrEF-COPD and those with HFrEF alone. Methods: A prospective observational study included 65 patients with HFrEF (LVEF ≤ 40%), 18 of whom had COPD. Two CPETs were performed, the first on a treadmill and the second 48–72 h later on a cycle ergometer. Results: In the group with HFrEF-COPD, peak oxygen consumption (VO₂/kg) and maximum ventilation (VE) values were significantly higher on the treadmill (20 ± 5 vs. 17 ± 4 mL/kg/min, p < 0.001 and 55 ± 19 vs. 45 ± 11 L/min, p < 0.001, respectively), while breathing reserve (BR%) was lower on the treadmill (16 ± 21 vs. 33 ± 20, p < 0.001). Compared to the HFrEF group, patients with HFrEF-COPD had a lower BR in both exercise modalities (p = 0.01). Conclusions: Treadmill CPET demonstrates greater oxygen consumption and a more pronounced ventilatory response. BR is consolidated as a differential parameter in ventilatory limitation. The choice of exercise modality should be considered based on the underlying pathologies and the objective of the test.

The common trait in the obstructive sleep apnoea syndrome (OSAS) is a certain degree of upper airway instability. The interrupter technique (RINT), measures the resistance in the upper airways (UA), it could be used to identify patients with a mayor impaired anatomy. The aim of this study is to investigate if a correlation between UA resistance and OSAS can be determined Method: A cross-sectional case-control study was carried out. All subjects who underwent through a polysomnography were included. Two groups were made: OSAS (AHI >10/h), and a control group (AHI ≤ 10/h). A RINT test was performed, being awake, in both seated and supine position. Two linear regressions were obtained between pressure/flow measurements in both positions. Impaired anatomy was ascertained by measuring the angle between the linear regressions. The wider angle the greater the impairement Results: 91 patients and 31 control subjects were recruited [table 1]. The mean value for the pressure/flow angle in OSAS was 7.5º(4), and 4º(2) in the control group (p<0.001). A significant association between pressure/flow angle and AHI was found(p=0.03). Setting the threshold at 10°, the specificity was 96% and predictive positive value 95% Conclusions: –RINT is a simple and wakefulness test, that has shown a high specificity and predictive positive value in the diagnosis of OSAS –These results may help to prioritize patients on waiting list and optimize resources