Suprasternal pressure recording and respiratory inductance plethysmography for respiratory effort evaluation in patients with sleep-disordered breathing

Sleep-disordered breathing is characterized by airway dysfunction that can occur in any age, but most prevalent in children, caused by the occurrence of respiratory effort, snoring or even by apnea during sleep. Therefore, the aim of this study was to survey the prevalence of sleep disorders associated with malocclusion in children aged 3 to 12 years in Mineiros, State of Goiás, Brazil. Material and Methods: this is a field research with a sample of 99 children affected by some type of sleep-disordered breathing and malocclusions. Data were collected through a questionnaire about sleep-disordered breathing and a clinical record carried out through intraoral clinical examination. Results: among the 24 children with SDB, 17 had SDB and Malocclusion, which is 70.8% of the children had SDB associated with malocclusion. Of the 75 children without SDB, 11 (14.7%) had malocclusion. Conclusion: No significant differences were found between sleep-disordered breathing and sex-related malocclusions.

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Persistent respiratory effort after adenotonsillectomy in children with sleep disordered breathing

10.1183/23120541.sleepandbreathing-2017.p57 ◽

2017 ◽

Author(s):

Jean-Benoit Martinot ◽

Nhat-Nam Le-Dong ◽

Jean-Christian Borel ◽

Stéphane Denison ◽

David Gozal ◽

...

Keyword(s):

Sleep Disordered Breathing ◽

Respiratory Effort ◽

Disordered Breathing

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Persistent respiratory effort after adenotonsillectomy in children with sleep-disordered breathing

The Laryngoscope ◽

10.1002/lary.26830 ◽

2017 ◽

Vol 128 (5) ◽

pp. 1230-1237 ◽

Cited By ~ 7

Author(s):

Jean-Benoît Martinot ◽

N. Nam Le-Dong ◽

Stéphane Denison ◽

Hervé Jean-Pierre Guénard ◽

Jean-Christian Borel ◽

...

Keyword(s):

Sleep Disordered Breathing ◽

Respiratory Effort ◽

Disordered Breathing

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Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography

Sleep And Breathing ◽

10.1007/s11325-021-02316-0 ◽

2021 ◽

Author(s):

Sarah Dietz-Terjung ◽

Amelie Ricarda Martin ◽

Eysteinn Finnsson ◽

Jón Skínir Ágústsson ◽

Snorri Helgason ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Disease Severity ◽

Sleep Disordered Breathing ◽

Pearson Correlation ◽

Sleep Stages ◽

The Novel ◽

Respiratory Inductance Plethysmography ◽

Proof Of Principle ◽

Novel Algorithm ◽

Disordered Breathing

Abstract Purpose In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleepTM 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB). Methods Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0). Results We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen’s kappa of 0.62 was found for this 3-class classification problem. Conclusion The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.

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