ABSTRACT
Background: The capacity to diagnose obstructive sleep apnoea (OSA) must be expanded to meet an estimated disease burden of nearly one billion people worldwide. Validated alternatives to the gold standard polysomnography (PSG) will improve access to testing and treatment. This study aimed to evaluate the diagnosis of OSA, using measurements of mandibular movement (MM) combined with automated machine learning analysis, compared to in-home PSG. Methods: 40 suspected OSA patients underwent single overnight in-home sleep testing with PSG (Nox A1, ResMed, Australia) and simultaneous MM monitoring (Sunrise, Sunrise SA, Belgium). PSG recordings were manually analysed by two expert sleep centres (Grenoble and London); MM analysis was automated. The Obstructive Respiratory Disturbance Index calculated from the MM monitoring (MM-ORDI) was compared to the PSG (PSG-ORDI) using intraclass correlation coefficient and Bland-Altman analysis. Receiver operating characteristic curves (ROC) were constructed to optimise the diagnostic performance of the MM monitor at different PSG-ORDI thresholds (5, 15, and 30 events/hour). Results: 31 patients were included in the analysis (58% men; mean (SD) age: 48 (15) years; BMI: 30.4 (7.6) kg/m2). Good agreement was observed between MM-ORDI and PSG-ORDI (median bias 0.00; 95% CI -23.25 to + 9.73 events/hour). However, for 15 patients with no or mild OSA, MM monitoring overestimated disease severity (PSG-ORDI < 5: MM-ORDI mean overestimation + 5.58 (95% CI + 2.03 to + 7.46) events/hour; PSG-ORDI > 5-15: MM-ORDI overestimation + 3.70 (95% CI -0.53 to + 18.32) events/hour). In 16 patients with moderate-severe OSA (n = 9 with PSG-ORDI 15-30 events/h and n = 7 with a PSG-ORD > 30 events/h), there was an underestimation (PSG-ORDI > 15: MM-ORDI underestimation -8.70 (95% CI -28.46 to + 4.01) events/hour). ROC optimal cut-off values for PSG-ORDI thresholds of 5, 15, 30 events/hour were: 9.53, 12.65 and 24.81 events/hour, respectively. These cut-off values yielded a sensitivity of 88, 100 and 79%, and a specificity of 100, 75, 96%. The positive predictive values were: 100, 80, 95% and the negative predictive values 89, 100, 82%, respectively. Conclusion: The diagnosis of OSA, using MM with machine learning analysis, is comparable to manually scored in-home PSG. Therefore, this novel monitor could be a convenient diagnostic tool that can easily be used in the patients' own home. Clinical Trial Registration: https://clinicaltrials.gov, identifier NCT04262557.
ABSTRACT
The aim of this study was to assess the impact of the nationwide total lockdown (LD) in France on weight loss and body composition modifications in subjects participating in a weight loss program and to evaluate the impact of remote consultations on participants' adherence to the weight loss program. The CO-RNPC study was a prospective multicentre cohort study including participants undergoing a two to six months program. The rate of weight loss in kg/week was computed before (15 days), during (99 days) and after LD (15 days). In the 1550 completing participants, body weight decreased from 87.1 kg [IQR 77.0; 100.2] to 82.3 kg [72.1; 94.3] resulting in a difference of -4.79 kg [-4.48; -5.10] (p < 0.01), with a corresponding reduction in waist circumference by 4 cm ([0; 9], p < 0.01). The median weight loss was 4.4 kg [0.5; 9.4] in those who used remote consultations, and 1.4 kg [0.8; 5.7] in the no remote consultation group (p < 0.01). In this large prospective cohort, we observed that the rate of weight loss was reduced during LD. This reduction was counterbalanced in participants involved in a remote consultation follow-up with a dose-effect response based on the number of remote consultations.