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Mayoral Sanz P, Lagravère Vich M. Oral Appliances for Obstructive Sleep Apnea. Dent Clin North Am 2024; 68:495-515. [PMID: 38879283 DOI: 10.1016/j.cden.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The use of mandibular repositioning devices (MRDs) in the management of patients with obstructive sleep apnea (OSA) has gained extensive recognition with relevant clinical evidence of its effectiveness. MRDs are designed to advance and hold the mandible in a protrusive position to widen the upper airway and promote air circulation. This review of the MRD aims to provide an evidence-based update on the optimal design features of an MRD, an analysis of the variety of appliances available, and the current understanding of the action mechanism.
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Affiliation(s)
- Pedro Mayoral Sanz
- Catholic University of Murcia UCAM, Calle Conde de Peñalver 61 - 1º, Madrid 28029, Spain.
| | - Manuel Lagravère Vich
- School of Dentistry, 5-488 Edmonton Clinic Health Academy, University of Alberta, 11405-87 Avenue Northwest, Edmonton, Alberta T6G 1C9, Canada
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Malhotra A, Martinot JB, Pépin JL. Insights on mandibular jaw movements during polysomnography in obstructive sleep apnea. J Clin Sleep Med 2024; 20:151-163. [PMID: 37767856 PMCID: PMC10758568 DOI: 10.5664/jcsm.10830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
A strong and specific comprehensive physiological association has been documented between mandibular jaw movements and related periods of normal or disturbed breathing across different sleep stages. The mandibular jaw movement biosignal can be incorporated in the polysomnography, displayed on the screen as a function of time like any standard polysomnography signal (eg, airflow, oxygen saturation, respiratory inductance plethysmography bands) and interpreted in the context of the target period of breathing and its associated respiratory effort level. Overall, the mandibular jaw movement biosignal that depicts the muscular trigeminal respiratory drive is a highly effective tool for differentiating between central and obstructive sleep episodes including hypopneas and for providing clinicians with valuable insights into wake/sleep states, arousals, and sleep stages. These fundamental characteristics of the mandibular jaw movement biosignal contrast with photoplethysmography, airflow, or oxygen saturation signals that provide information more about the consequence of the disturbed breathing episode than about the event itself. CITATION Malhotra A, Martinot J-B, Pépin J-L. Insights on mandibular jaw movements during polysomnography in obstructive sleep apnea. J Clin Sleep Med. 2024;20(1):151-163.
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Affiliation(s)
- Atul Malhotra
- University of California San Diego, La Jolla, California
| | - Jean-Benoit Martinot
- Sleep Laboratory, CHU Université catholique de Louvain Namur Site Sainte-Elisabeth, Namur, Belgium
- Institute of Experimental and Clinical Research, Université catholique de Louvain Bruxelles Woluwe, Brussels, Belgium
| | - Jean-Louis Pépin
- HP2 Laboratory, Inserm U1300, University Grenoble Alpes, Grenoble, France
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Martinot JB, Cuthbert V, Le-Dong NN, Coumans N, De Marneffe D, Letesson C, Pépin JL, Gozal D. Clinical validation of a mandibular movement signal based system for the diagnosis of pediatric sleep apnea. Pediatr Pulmonol 2022; 57:1904-1913. [PMID: 33647188 DOI: 10.1002/ppul.25320] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Given the high prevalence and risk for outcomes associated with pediatric obstructive sleep apnea (OSA), there is a need for simplified diagnostic approaches. A prospective study in 140 children undergoing in-laboratory polysomnography (PSG) evaluates the accuracy of a recently developed system (Sunrise) to estimate respiratory efforts by monitoring sleep mandibular movements (MM) for the diagnosis of OSA (Sunrise™). METHODS Diagnosis and severity were defined by an obstructive apnea/hypopnea index (OAHI) ≥ 1 (mild), ≥ 5 (moderate), and ≥ 10 events/h (severe). Agreement between PSG and Sunrise™ was assessed by Bland-Altman method comparing respiratory disturbances hourly index (RDI) (obstructive apneas, hypopneas, and respiratory effort-related arousals) during PSG (PSG_RDI), and Sunrise RDI (Sr_RDI). Performance of Sr_RDI was determined via ROC curves evaluating the device sensitivity and specificity at PSG_OAHI ≥ 1, 5, and 15 events/h. RESULTS A median difference of 1.57 events/h, 95% confidence interval: -2.49 to 8.11 was found between Sr_RDI and PSG_RDI. Areas under the ROC curves of Sr_RDI were 0.75 (interquartile range [IQR]: 0.72-0.78), 0.90 (IQR: 0.86-0.92) and 0.95 (IQR: 0.90-0.99) for detecting children with PSG_OAHI ≥ 1, PSG_OAHI ≥ 5, or PSG_ OAHI ≥ 10, respectively. CONCLUSION MM automated analysis shows significant promise to diagnose moderate-to-severe pediatric OSA.
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Affiliation(s)
- Jean-Benoit Martinot
- Sleep Laboratory, CHU UCL Namur Site Sainte-Elisabeth, Belgium
- Institute of Experimental and Clinical Research, UCL, Bruxelles Woluwe, Belgium
| | | | | | | | | | | | - Jean L Pépin
- Inserm, CHU Grenoble Alpes, HP2, Université Grenoble Alpes, Grenoble, France
| | - David Gozal
- Department of Child Health, University of Missouri, Columbia, Missouri, USA
- Child Health Research Institute, University of Missouri, Columbia, Missouri, USA
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Kelly JL, Ben Messaoud R, Joyeux-Faure M, Terrail R, Tamisier R, Martinot JB, Le-Dong NN, Morrell MJ, Pépin JL. Diagnosis of Sleep Apnoea Using a Mandibular Monitor and Machine Learning Analysis: One-Night Agreement Compared to in-Home Polysomnography. Front Neurosci 2022; 16:726880. [PMID: 35368281 PMCID: PMC8965001 DOI: 10.3389/fnins.2022.726880] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 02/22/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundThe 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.Methods40 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).Results31 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.ConclusionThe 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 Registrationhttps://clinicaltrials.gov, identifier NCT04262557
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Affiliation(s)
- Julia L. Kelly
- National Heart and Lung Institute, Imperial College London, Royal Brompton Hospital, London, United Kingdom
| | - Raoua Ben Messaoud
- HP2 Laboratory, Inserm U1300, Grenoble Alpes University, Grenoble, France
| | - Marie Joyeux-Faure
- HP2 Laboratory, Inserm U1300, Grenoble Alpes University, Grenoble, France
- EFCR Laboratory, Thorax and Vessels division, Grenoble Alpes University Hospital, Grenoble, France
| | - Robin Terrail
- HP2 Laboratory, Inserm U1300, Grenoble Alpes University, Grenoble, France
- EFCR Laboratory, Thorax and Vessels division, Grenoble Alpes University Hospital, Grenoble, France
| | - Renaud Tamisier
- HP2 Laboratory, Inserm U1300, Grenoble Alpes University, Grenoble, France
- EFCR Laboratory, Thorax and Vessels division, Grenoble Alpes University Hospital, Grenoble, France
| | - Jean-Benoît Martinot
- Sleep Laboratory, CHU Université catholique de Louvain (UCL) Namur Site Sainte-Elisabeth, Namur, Belgium
- Institute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, Belgium
| | | | - Mary J. Morrell
- National Heart and Lung Institute, Imperial College London, Royal Brompton Hospital, London, United Kingdom
| | - Jean-Louis Pépin
- HP2 Laboratory, Inserm U1300, Grenoble Alpes University, Grenoble, France
- EFCR Laboratory, Thorax and Vessels division, Grenoble Alpes University Hospital, Grenoble, France
- *Correspondence: Jean-Louis Pépin,
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Martinot JB, Le-Dong NN, Cuthbert V, Denison S, Gozal D, Lavigne G, Pépin JL. Artificial Intelligence Analysis of Mandibular Movements Enables Accurate Detection of Phasic Sleep Bruxism in OSA Patients: A Pilot Study. Nat Sci Sleep 2021; 13:1449-1459. [PMID: 34466045 PMCID: PMC8397703 DOI: 10.2147/nss.s320664] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/05/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Sleep bruxism (SBx) activity is classically identified by capturing masseter and/or temporalis masticatory muscles electromyographic activity (EMG-MMA) during in-laboratory polysomnography (PSG). We aimed to identify stereotypical mandibular jaw movements (MJM) in patients with SBx and to develop rhythmic masticatory muscles activities (RMMA) automatic detection using an artificial intelligence (AI) based approach. PATIENTS AND METHODS This was a prospective, observational study of 67 suspected obstructive sleep apnea (OSA) patients in whom PSG with masseter EMG was performed with simultaneous MJM recordings. The system used to collect MJM consisted of a small hardware device attached on the chin that communicates to a cloud-based infrastructure. An extreme gradient boosting (XGB) multiclass classifier was trained on 79,650 10-second epochs of MJM data from the 39 subjects with a history of SBx targeting 3 labels: RMMA episodes (n=1072), micro-arousals (n=1311), and MJM occurring at the breathing frequency (n=77,267). RESULTS Validated on unseen data from 28 patients, the model showed a very good epoch-by-epoch agreement (Kappa = 0.799) and balanced accuracy of 86.6% was found for the MJM events when using RMMA standards. The RMMA episodes were detected with a sensitivity of 84.3%. Class-wise receiver operating characteristic (ROC) curve analysis confirmed the well-balanced performance of the classifier for RMMA (ROC area under the curve: 0.98, 95% confidence interval [CI] 0.97-0.99). There was good agreement between the MJM analytic model and manual EMG signal scoring of RMMA (median bias -0.80 events/h, 95% CI -9.77 to 2.85). CONCLUSION SBx can be reliably identified, quantified, and characterized with MJM when subjected to automated analysis supported by AI technology.
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Affiliation(s)
- Jean-Benoit Martinot
- Sleep Laboratory, CHU Université Catholique de Louvain (UCL) Namur Site Sainte-Elisabeth, Namur, 5000, Belgium.,Institute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, 1200, Belgium
| | | | - Valérie Cuthbert
- Sleep Laboratory, CHU Université Catholique de Louvain (UCL) Namur Site Sainte-Elisabeth, Namur, 5000, Belgium
| | | | - David Gozal
- Department of Child Health and Child Health Research Institute, University of Missouri, Columbia, MO, 65201, USA
| | - Gilles Lavigne
- Faculté de médecine dentaire, Université de Montréal, Montréal, Québec, H3C 3J7, Canada
| | - Jean-Louis Pépin
- HP2 Laboratory, Inserm U1042, University Grenoble Alpes, Grenoble, 38000, France
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