AVAPS-AE versus ST mode: A randomized controlled trial in patients with obesity hypoventilation syndrome.

BACKGROUND AND OBJECTIVE: Average volume-assured pressure support-automated expiratory positive airway pressure (AVAPS-AE) combines an automated positive expiratory pressure to maintain upper airway patency to an automated pressure support with a targeted tidal volume. The aim of this study was to compare the effects of 2-month AVAPS-AE ventilation versus pressure support (ST) ventilation on objective sleep quality in stable patients with OHS. Secondary outcomes included arterial blood gases, health-related quality of life, daytime sleepiness, subjective sleep quality and compliance to NIV. METHODS: This is a prospective multicentric randomized controlled trial. Consecutive OHS patients included had daytime Pa CO2 > 6 kPa, BMI ≥ 30 kg/m2 , clinical stability for more than 2 weeks and were naive from home NIV. PSG were analysed centrally by two independent experts. Primary endpoint was sleep quality improvement at 2 months. RESULTS: Among 69 trial patients, 60 patients had successful NIV setup. Baseline and follow-up PSG were available for 26 patients randomized in the ST group and 30 in the AVAPS-AE group. At baseline, Pa CO2 was 6.94 ± 0.71 kPa in the ST group and 6.61 ± 0.71 in the AVAPS-AE group (P = 0.032). No significant between-group difference was observed for objective sleep quality indices. Improvement in Pa CO2 was similar between groups with a mean reduction of -0.87 kPa (95% CI: -1.12 to -0.46) in the ST group versus -0.87 kPa (95% CI: -1.14 to -0.50) in the AVAPS-AE group (P = 0.984). Mean NIV use was 6.2 h per night in both groups (P = 0.93). NIV setup duration was shorter in the AVAPS-AE group (P = 0.012). CONCLUSION: AVAPS-AE and ST ventilation for 2 months had similar impact on sleep quality and gas exchange.

Automatic sleep staging from ventilator signals in non-invasive ventilation.

Non-invasive ventilation (NIV), a recognized treatment for chronic hypercapnic respiratory failure, is predominantly applied at night. Nevertheless, the quality of sleep is rarely evaluated due to the required technological complexity. A new technique for automatic sleep staging is here proposed for patients treated by NIV. This new technique only requires signals (airflow and hemoglobin oxygen saturation) available in domiciliary ventilators plus a photo-plethysmogram, a signal already managed by some ventilators. Consequently, electroencephalogram, electrooculogram, electromyogram, and electrocardiogram recordings are not needed. Cardiorespiratory features are extracted from the three selected signals and used as input to a Support Vector Machine (SVM) multi-class classifier. Two different types of sleep scoring were investigated: the first type was used to distinguish three stages (wake, REM sleep and nonREM sleep), and the second type was used to evaluate five stages (wake, REM sleep, N1, N2 and N3 stages). Patient-dependent and patient-independent classifiers were tested comparing the resulting hypnograms with those obtained from visual/manual scoring by a sleep specialist. An average accuracy of 91% (84%) was obtained with three-stage (five-stage) patient-dependent classifiers. With patient-independent classifiers, an average accuracy of 78% (62%) was obtained when three (five) sleep stages were scored. Also if the PPG-based and flow features are left out, a reduction of 4.5% (resp. 5%) in accuracy is observed for the three-stage (resp. five-stage) cases. Our results suggest that long-term sleep evaluation and nocturnal monitoring at home is feasible in patients treated by NIV. Our technique could even be integrated into ventilators.