Quantifying the Arousal Threshold Using Polysomnography in Obstructive Sleep Apnea

Sands, Scott A.; Terrill, Philip I.; Edwards, Bradley A.; Taranto‐Montemurro, Luigi; Azarbarzin, Ali; Marques, Melania; Melo, Camila Maria de; Loring, Stephen H.; Butler, James P.; White, David P.; Wellman, Andrew

doi:10.1093/sleep/zsx183

Cited by 126 publications

(97 citation statements)

References 41 publications

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“…Ten patients completed a prospective validation polysomnography(18, 21) to assess the validity of using nasal pressure as a clinical surrogate of ventilatory flow in our method. To simultaneously measure nasal pressure (nasal cannula) and ventilatory flow (pneumotachograph with oronasal mask), a modified cannula (Hudson RCI “over-the-ear”, cut to fit under the mask) provided a nasal pressure signal that was referenced to mask pressure to reflect the pressure signal available clinically.…”

Section: Methodsmentioning

confidence: 99%

Palatal prolapse as a signature of expiratory flow limitation and inspiratory palatal collapse in patients with obstructive sleep apnoea

et al. 2018

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Objectives In some individuals with obstructive sleep apnea (OSA), the palate prolapses into the velopharynx during expiration, limiting airflow through the nose or shunting it out the mouth. We hypothesized that this phenomenon causes expiratory flow limitation (EFL) and is associated with inspiratory “isolated” palatal collapse. We also provide a robust non-invasive means to identify this mechanism of obstruction. Methods Using natural sleep endoscopy, 1211 breaths from 22 OSA patients were scored as having or not having palatal prolapse. The patient-level site of collapse (tongue-related, isolated palate, pharyngeal lateral walls, and epiglottis) was also characterized. EFL was quantified using expiratory resistance at maximal epiglottic pressure. A non-invasive expiratory flow limitation index (EFLI) was developed to detect the presence of palatal prolapse and EFL using the flow signal alone. In addition, the validity of using nasal pressure was assessed. Results A cutoff value of EFLI>0.8 detected the presence of palatal prolapse and EFL with an accuracy of >95% and 82%, respectively. The proportion of breaths with palatal prolapse predicted isolated inspiratory palatal collapse with 90% accuracy. Conclusions This study demonstrates that expiratory palatal prolapse can be quantified non-invasively, is associated with EFL, and predicts the presence of inspiratory isolated palatal collapse.

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Section: Methodsmentioning

confidence: 99%

Palatal prolapse as a signature of expiratory flow limitation and inspiratory palatal collapse in patients with obstructive sleep apnoea

et al. 2018

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“…These multichannel recordings generate huge amounts of data and there are an ever increasing number of AI initiatives, including neural networks, to automate sleep stage scoring and describe specific patterns characteristic of sleep disorders, such as narcolepsy . Polysomnography data sets have also been used to develop automated techniques to identify pathophysiological OSA ‘traits’ and to separate among phenotypes of pharyngeal dysfunction, increase in loop gain or different arousal thresholds . These traits could be recognized, thanks to Big Data analyses of spontaneous changes in ventilation and ventilatory drive during sleep, in the future enabling simpler non‐invasive pathophysiological phenotyping in the clinic.…”

Section: Opportunities Of Big Data In Understanding Sleep Breathing Dmentioning

confidence: 99%

Big Data in sleep apnoea: Opportunities and challenges

2019

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Sleep apnoea is now regarded as a highly prevalent systemic, multimorbid, chronic disease requiring a combination of long-term home-based treatments. Optimization of personalized treatment strategies requires accurate patient phenotyping. Data to describe the broad variety of phenotypes can come from electronic health records, health insurance claims, socio-economic administrative databases, environmental monitoring, social media, etc. Connected devices in and outside homes collect vast amount of data amassed in databases. All this contributes to 'Big Data' that, if used appropriately, has great potential for the benefit of health, well-being and therapeutics. Sleep apnoea is particularly well placed with regards to Big Data because the primary treatment is positive airway pressure (PAP). PAP devices, used every night over long periods by millions of patients across the world, generate an enormous amount of data. In this review, we discuss how different types of Big Data have, and could be, used to improve our understanding of sleep-disordered breathing, to identify undiagnosed sleep apnoea, to personalize treatment and to adapt health policies and better allocate resources. We discuss some of the challenges of Big Data including the need for appropriate data management, compilation and analysis techniques employing innovative statistical approaches alongside machine learning/artificial intelligence; closer collaboration between data scientists and physicians; and respect of the ethical and regulatory constraints of collecting and using Big Data. Lastly, we consider how Big Data can be used to overcome the limitations of randomized clinical trials and advance real-life evidence-based medicine for sleep apnoea.Big Data and sleep apnoea tory, Grenoble Alpes University) for the literature search and contribution to the pharmacovigilance and environmentalRespirology (2020) 25, 486-494

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“…A more sophisticated analysis processed the flow signal during NREM periods to yield a breath‐to‐breath ventilation time series. This was then further processed to determine the arousal threshold for the patient . Other algorithms have been published for measuring loop gain as a measure of ventilatory control, pharyngeal collapsibility and muscle compensation with strong correlation to values obtained from experimental physiological studies (correlation coefficient: ~0.7).…”

Section: Polysomnography As a Window Into Osa Pathophysiologymentioning

confidence: 99%

“…This was then further processed to determine the arousal threshold for the patient. 63 Other algorithms have been published for measuring loop gain as a measure of ventilatory control, 64 pharyngeal collapsibility and muscle compensation 65,66 with strong correlation to values obtained from experimental physiological studies (correlation coefficient:~0.7). Pharyngeal site of collapse has been predicted from inspiratory flow shape features.…”

Section: Polysomnography As a Window Into Osa Pathophysiologymentioning

confidence: 99%

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

2019

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Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co‐morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information‐rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co‐morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

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Quantifying the Arousal Threshold Using Polysomnography in Obstructive Sleep Apnea

Abstract: The arousal threshold in patients with OSA can be estimated using polysomnographic signals and may enable more personalized therapeutic interventions for patients with a low arousal threshold.

Cited by 126 publications

References 41 publications

Palatal prolapse as a signature of expiratory flow limitation and inspiratory palatal collapse in patients with obstructive sleep apnoea

Palatal prolapse as a signature of expiratory flow limitation and inspiratory palatal collapse in patients with obstructive sleep apnoea

Big Data in sleep apnoea: Opportunities and challenges

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

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