Clinical validation of a mandibular movement signal based system for the diagnosis of pediatric sleep apnea.
machine learning
mandibular movement
pediatric obstructive sleep apnea
Journal
Pediatric pulmonology
ISSN: 1099-0496
Titre abrégé: Pediatr Pulmonol
Pays: United States
ID NLM: 8510590
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
revised:
08
02
2021
received:
27
11
2020
accepted:
09
02
2021
pubmed:
2
3
2021
medline:
14
7
2022
entrez:
1
3
2021
Statut:
ppublish
Résumé
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™). 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. 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. MM automated analysis shows significant promise to diagnose moderate-to-severe pediatric OSA.
Sections du résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1904-1913Informations de copyright
© 2021 Wiley Periodicals LLC.
Références
Gipson K, Lu M, Kinane TB. Sleep-disordered breathing in children. Pediatr Rev. 2019;40:3-13.
Marcus CL, Brooks LJ, Ward SD, et al. Diagnosis and management of childhood obstructive sleep apnea syndrome. Pediatrics. 2012;130:e714-e755.
Gileles-Hillel A, Philby M, Lapping-Carr G. Insights into selected aspects of pediatric sleep medicine. Am J Respir Crit Care Med. 2015;191:1459-1461.
Gozal D, Kheirandish-Gozal L. Obesity and excessive daytime sleepiness in prepubertal children with obstructive sleep apnea. Pediatrics. 2009;123:13-18.
Hunter SJ, Gozal D, Smith DL, Philby MF, Kaylegian J, Kheirandish-Gozal L. Effect of sleep-disordered breathing severity on cognitive performance measures in a large community cohort of young school-aged children. Am J Respir Crit Care Med. 2016;194:739-747.
Kirk V, Baughn J, D'Andrea L, et al. American Academy of Sleep Medicine Position Paper for the use of a home sleep apnea test for the diagnosis of OSA in children. J Clin Sleep Med. 2017;13:1199-1203.
Weatherly RA, Mai EF, Ruzicka DL, Chervin RD. Identification and evaluation of obstructive sleep apnea prior to adenotonsillectomy in children: a survey of practice patterns. Sleep Med. 2003;4:297-307.
Spruyt K, Gozal D. Pediatric sleep questionnaires as diagnostic or epidemiological tools: a review of currently available instruments. Sleep Med Rev. 2011;15:19-32.
Spruyt K, Gozal D. Screening of pediatric sleep-disordered breathing: a proposed unbiased discriminative set of questions using clinical severity scales. Chest. 2012;142:1508-1515.
Kaditis AG, Kheirandish-Gozal L, Gozal D. Pediatric OSAS: oximetry can provide answers when polysomnography is not available. Sleep Med Rev. 2015;27:96-105.
Xu Z, Gutiérrez-Tobal GC, Wu Y, et al. Cloud algorithm-driven oximetry-based diagnosis of obstructive sleep apnoea in symptomatic habitually snoring children. Eur Respir J. 2019;53:1801788.
Hornero R, Kheirandish-Gozal L, Gutiérrez-Tobal GC, et al. Nocturnal oximetry-based evaluation of habitually snoring children. Am J Respir Crit Care Med. 2017;196:1591-1598.
Alonso-Álvarez ML, Canet T, Cubell-Alarco M, et al. Consensus document on sleep apnea-hypopnea syndrome in children. Arch Bronconeumol. 2011;47:1-18.
Kaditis AG, Alvarez Alonso, Boudewyns ML, et al. Sleep disordered breathing in 2- to 18-year-old children: diagnosis and management. Eur Respir J. 2016;47:69-94.
Kaditis AG, Kheirandish-Gozal L, Gozal D. Algorithm for the diagnosis and management of pediatric OSA: a proposal. Sleep Med. 2012;13:217-227.
Ayas NT, Drager LF, Morrell MJ, Polotsky VY. Update in sleep disordered breathing 2016. Am J Respir Crit Care Med. 2017;195:1561-1566.
Tarasiuk A, Simon T, Tal A, Reuveni H. Adenotonsillectomy in children with obstructive sleep apnea syndrome reduces health care utilization. Pediatrics. 2004;113:351-356.
Bandla HP, Gozal D. Dynamic changes in EEG spectra during obstructive apnea in children. Pediatr Pulmonol. 2000;29:359-365.
Paruthi S, Chervin RD. Approaches to the assessment of arousals and sleep disturbance in children. Sleep Med. 2010;11:622-627.
Chervin RD, Garetz SL, Ruzicka DL, et al. Do respiratory cycle-related EEG changes or arousals from sleep predict neurobehavioral deficits and response to adenotonsillectomy in children? J Clin Sleep Med. 2014;10:903-911.
O'Brien LM, Tauman R, Gozal D. Sleep pressure correlates of cognitive and behavioral morbidity in snoring children. Sleep. 2004;27:279-282.
Mikkelsen KB, Ebajemito JK, Bonmati-Carrion MA, et al. Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy. J Sleep Res. 2019;28:e12786.
Goldstein CA, Berry RB, Kent DT, et al. Artificial intelligence in sleep medicine: background and implications for clinicians. J Clin Sleep Med. 2020;16:609-618.
Alvarez-Estevez D, Moret-Bonillo V. Computer-assisted diagnosis of the sleep apnea-hypopnea syndrome: a review. Sleep Disord. 2015;2015:237878-33.
Stephansen JB, Olesen AN, Olsen M, et al. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nat Commun. 2018;9:5229.
Pépin JL, Letesson C, Le-Dong NN, et al. Automated sleep apnea diagnosis through mandibular movement monitoring coupled with machine learning analysis. JAMA Netw Open. 2020;3:e1919657.
Ingram DG, Ruiz A, Friedman NR. Friedman tongue position: age distribution and relationship to sleep-disordered breathing. Int J Pediatr Otorhinolaryngol. 2015;79:666-670.
Berry RB, Budhiraja R, Gottlieb DJ, et al. Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. Deliberations of the sleep apnea definitions task force of the American Academy of Sleep Medicine. J Clin Sleep Med. 2012;8:597-619.
Martinot JB, Le-Dong NN, Cuthbert V, et al. The key role of the mandible in modulating airflow amplitude during sleep. Respir Physiol Neurobiol. 2020;279:103447.
R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1989;1:307-310.
Collop N. Breathing related arousals: call them what you want, but please count them. J Clin Sleep Med. 2014;10:125-126.
Lin CH, Guilleminault C. Current hypopnea scoring criteria underscore pediatric sleep disordered breathing. Sleep Med. 2011;12:720-729.
Nixon GM, Hyde M, Biggs SN, Walter LM, Horne RS, Davey MJ. The impact of recent changes to the respiratory scoring rules in pediatrics. J Clin Sleep Med. 2014;10:1217-1221.
Marcus CL, Moore RH, Rosen CL, et al. Childhood Adenotonsillectomy Trial (CHAT). A randomized trial of adenotonsillectomy for childhood sleep apnea. N Engl J Med. 2013;368:2366-2376.
Jacob SV, Morielli A, Mograss MA, Ducharme FM, Schloss MD, Brouillette RT. Home testing for pediatric obstructive sleep apnea syndrome secondary to adenotonsillar hypertrophy. Pediatr Pulmonol. 1995;20:241-252.
Smith DL, Gozal D, Hunter SJ, Kheirandish-Gozal L. Frequency of snoring, rather than apnea-hypopnea index, predicts both cognitive and behavioral problems in young children. Sleep Med. 2017;34:170-178.
Brooks DM, Kelly A, Sorkin JD, et al. The relationship between sleep disordered breathing, blood pressure, and urinary cortisol and catecholamines children. J Clin Sleep Med. 2020;16:907-916.
Amin R, Somers VK, McConnell K, et al. Activity-adjusted 24-hour ambulatory blood pressure and cardiac remodeling in children with sleep disordered breathing. Hypertension. 2008;51:84-91.
Marcus CL, Omlin KJ, Basinki DJ, et al. Normal polysomnographic values for children and adolescents. Am Rev Respir Dis. 1992;146:1235-1239.
Brooks DM, Brooks LJ. Reevaluating norms for childhood obstructive sleep apnea. J Clin Sleep Med. 2019;15:1557-1558.
Martinot JB, Le-Dong NN, Denison S, et al. Persistent respiratory effort after adenotonsillectomy in children with sleep-disordered breathing. Laryngoscope. 2018;128:1230-1237.
Martinot JB, Senny F, Denison S, et al. Mandibular movements identify respiratory effort in pediatric obstructive sleep apnea. J Clin Sleep Med. 2015;11:567-574.
Tan HL, Kheirandish-Gozal L, Gozal D. Pediatric home sleep apnea testing. Chest. 2015;148:1382-1395.