Search

Background Although adaptive servo-ventilation (ASV) effectively supresses central sleep apnoea (CSA), little is known about real-world indications of ASV therapy and its effects on quality of life (QoL). Methods This report details the design, baseline characteristics, indications for ASV and symptom burden in patients enrolled in the Registry on the Treatment of Central and Complex Sleep-Disordered Breathing with Adaptive Servo-Ventilation (READ-ASV). This multicentre, European, non-interventional trial enrolled participants prescribed ASV in clinical practice between September 2017 and March 2021. An expert review board assigned participants to ASV indications using a guideline-based semi-automated algorithm. The primary end-point was change in disease-specific QoL based on the Functional Outcomes of Sleep Questionnaire (FOSQ) from baseline to 12-month follow-up. Results The registry population includes 801 participants (age 67±12 years, 14% female). Indications for ASV were treatment-emergent or persistent CSA (56%), CSA in cardiovascular disease (31%), unclassified CSA (2%), coexisting obstructive sleep apnoea and CSA (4%), obstructive sleep apnoea (3%), CSA in stroke (2%) and opioid-induced CSA (1%). Baseline mean apnoea­–hypopnoea index was 48±23 events·h−1 (≥30 events·h−1 in 78%), FOSQ score was 16.7±3.0 (10 in 34%); 62% of patients were symptomatic (FOSQ score 10). Conclusion The most common indications for ASV were treatment-emergent or persistent CSA or CSA in cardiovascular disease (excluding systolic heart failure). Patients using ASV in clinical practice had severe sleep-disordered breathing and were often symptomatic. One-year follow-up will provide data on the effects of ASV on QoL, respiratory parameters and clinical outcomes in these patients.

Rett syndrome, a rare genetic neurodevelopmental disorder in humans, does not have an effective cure. However, multiple therapies and medications exist to treat symptoms and improve patients’ quality of life. As research continues to discover and evaluate new medications for Rett syndrome patients, there remains a lack of objective physiological and motor activity-based (physio-motor) biomarkers that enable the measurement of the effect of these medications on the change in patients’ Rett syndrome severity. In our work, using a commercially available wearable chest patch, we recorded simultaneous electrocardiogram and three-axis acceleration from 20 patients suffering from Rett syndrome along with the corresponding Clinical Global Impression—Severity score, which measures the overall disease severity on a 7-point Likert scale. We derived physio-motor features from these recordings that captured heart rate variability, activity metrics, and the interactions between heart rate and activity. Further, we developed machine learning (ML) models to classify high-severity Rett patients from low-severity Rett patients using the derived physio-motor features. For the best-trained model, we obtained a pooled area under the receiver operating curve equal to 0.92 via a leave-one-out-patient cross-validation approach. Finally, we computed the feature popularity scores for all the trained ML models and identified physio-motor biomarkers for Rett syndrome.