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    Original Investigation
    Pulmonary Medicine
    January 22, 2020

    Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea

    Author Affiliations
    • 1Pôle Thorax et Vaisseaux, Centre Hospitalier Universitaire (CHU) de Grenoble-Alpes (CHUGA), Université Grenoble Alpes, Institut National de la Santé et de la Recherche Medicale, Grenoble, France
    • 2Sunrise, Namur, Belgium
    • 3Sleep Laboratory, CHU Université Catholique de Louvain (UCL) Namur Site Sainte-Elisabeth, Namur, Belgium
    • 4Institute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, Belgium
    • 5Department of Child Health, University of Missouri, Columbia
    • 6Child Health Research Institute, University of Missouri, Columbia
    JAMA Netw Open. 2020;3(1):e1919657. doi:10.1001/jamanetworkopen.2019.19657
    Key Points español 中文 (chinese)

    Question  How does the performance of an automated mandibular movement analysis for the diagnosis of obstructive sleep apnea compare with that of polysomnography?

    Findings  In this diagnostic study of 376 adults with suspected obstructive sleep apnea, the mandibular movement–derived respiratory disturbance index identified patients with a polysomnography respiratory disturbance index of at least 5 events/h or at least 15 events/h with accuracy of 0.92 and 0.88, respectively.

    Meaning  Automatic analysis of mandibular movement patterns reliably calculated respiratory disturbance index, and the use of this approach in obstructive sleep apnea diagnosis appears to be promising.


    Importance  Given the high prevalence of obstructive sleep apnea (OSA), there is a need for simpler and automated diagnostic approaches.

    Objective  To evaluate whether mandibular movement (MM) monitoring during sleep coupled with an automated analysis by machine learning is appropriate for OSA diagnosis.

    Design, Setting, and Participants  Diagnostic study of adults undergoing overnight in-laboratory polysomnography (PSG) as the reference method compared with simultaneous MM monitoring at a sleep clinic in an academic institution (Sleep Laboratory, Centre Hospitalier Universitaire Université Catholique de Louvain Namur Site Sainte-Elisabeth, Namur, Belgium). Patients with suspected OSA were enrolled from July 5, 2017, to October 31, 2018.

    Main Outcomes and Measures  Obstructive sleep apnea diagnosis required either evoking signs or symptoms or related medical or psychiatric comorbidities coupled with a PSG-derived respiratory disturbance index (PSG-RDI) of at least 5 events/h. A PSG-RDI of at least 15 events/h satisfied the diagnosis criteria even in the absence of associated symptoms or comorbidities. Patients who did not meet these criteria were classified as not having OSA. Agreement analysis and diagnostic performance were assessed by Bland-Altman plot comparing PSG-RDI and the Sunrise system RDI (Sr-RDI) with diagnosis threshold optimization via receiver operating characteristic curves, allowing for evaluation of the device sensitivity and specificity in detecting OSA at 5 events/h and 15 events/h.

    Results  Among 376 consecutive adults with suspected OSA, the mean (SD) age was 49.7 (13.2) years, the mean (SD) body mass index was 31.0 (7.1), and 207 (55.1%) were men. Reliable agreement was found between PSG-RDI and Sr-RDI in patients without OSA (n = 46; mean difference, 1.31; 95% CI, −1.05 to 3.66 events/h) and in patients with OSA with a PSG-RDI of at least 5 events/h with symptoms (n = 107; mean difference, −0.69; 95% CI, −3.77 to 2.38 events/h). An Sr-RDI underestimation of −11.74 (95% CI, −20.83 to −2.67) events/h in patients with OSA with a PSG-RDI of at least 15 events/h was detected and corrected by optimization of the Sunrise system diagnostic threshold. The Sr-RDI showed diagnostic capability, with areas under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.96) and 0.93 (95% CI, 0.90-0.93) for corresponding PSG-RDIs of 5 events/h and 15 events/h, respectively. At the 2 optimal cutoffs of 7.63 events/h and 12.65 events/h, Sr-RDI had accuracy of 0.92 (95% CI, 0.90-0.94) and 0.88 (95% CI, 0.86-0.90) as well as posttest probabilities of 0.99 (95% CI, 0.99-0.99) and 0.89 (95% CI, 0.88-0.91) at PSG-RDIs of at least 5 events/h and at least 15 events/h, respectively, corresponding to positive likelihood ratios of 14.86 (95% CI, 9.86-30.12) and 5.63 (95% CI, 4.92-7.27), respectively.

    Conclusions and Relevance  Automatic analysis of MM patterns provided reliable performance in RDI calculation. The use of this index in OSA diagnosis appears to be promising.