Quantifying the magnitude of pharyngeal obstruction during sleep using airflow shape

Abstract

Rationale and objectivesNon-invasive quantification of the severity of pharyngeal airflow obstruction would enable recognition of obstructive versus central manifestation of sleep apnoea, and identification of symptomatic individuals with severe airflow obstruction despite a low apnoea–hypopnoea index (AHI). Here we provide a novel method that uses simple airflow-versus-time (“shape”) features from individual breaths on an overnight sleep study to automatically and non-invasively quantify the severity of airflow obstruction without oesophageal catheterisation.Methods41 individuals with suspected/diagnosed obstructive sleep apnoea (AHI range 0–91 events·h−1) underwent overnight polysomnography with gold-standard measures of airflow (oronasal pneumotach: “flow”) and ventilatory drive (calibrated intraoesophageal diaphragm electromyogram: “drive”). Obstruction severity was defined as a continuous variable (flow:drive ratio). Multivariable regression used airflow shape features (inspiratory/expiratory timing, flatness, scooping, fluttering) to estimate flow:drive ratio in 136 264 breaths (performance based on leave-one-patient-out cross-validation). Analysis was repeated using simultaneous nasal pressure recordings in a subset (n=17).ResultsGold-standard obstruction severity (flow:drive ratio) varied widely across individuals independently of AHI. A multivariable model (25 features) estimated obstruction severity breath-by-breath (R2=0.58 versus gold-standard, p<0.00001; mean absolute error 22%) and the median obstruction severity across individual patients (R2=0.69, p<0.00001; error 10%). Similar performance was achieved using nasal pressure.ConclusionsThe severity of pharyngeal obstruction can be quantified non-invasively using readily available airflow shape information. Our work overcomes a major hurdle necessary for the recognition and phenotyping of patients with obstructive sleep disordered breathing.

Endotyping Sleep Apnea One Breath at a Time: An Automated Approach for Separating Obstructive from Central Sleep-disordered Breathing

“…Our findings are consistent with two previously published related approaches: a visual schema using ensemble of polysomnographic patterns to distinguish obstructive from central hypopneas 3 , and a breath-by-breath airflow shape-based algorithm that quantified the severity of upper airway obstruction. 16,17 The visual approach utilized known physiological features in a stepwise fashion to aid human scorers in classifying respiratory events. In a recent study by Dupuy-McCauley et.…”

“…al. in their study 16 , our approach expands by including multiple NPSG signals. Finally, compared to the two approaches described above, our approach does not require human scoring of respiratory events or sleep stages.…”

<p>A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea</p>

“…One study quantified the severity of pharyngeal obstruction by using a continuous variable termed the flow:drive ratio, based on the features of flow shape in an individual breath during PSG. 63 This feature varied widely among patients with OSA independent of the AHI score, although an association was observed between flow:drive and the AHI. Considering that this emerging metric concentrates mostly on the morphology of OSA without considering its major pathophysiological mechanisms, the role of the flow:drive ratio in the sequelae of OSA remains to be explored.…”

Upper airway stimulation for Obstructive Sleep Apnoea (OSA): A new paradigm of treatment

“…Another problem is the high cost of the device that could considerably limit its application in some healthcare systems. Further F I G U R E 1 Intraoperative photograph showing the placement of the stimulation cuff electrode around the protrusion branches of the hypoglossal nerve advancement of this innovative surgical approach will be possible improving technological components of the device, investigating optimal stimulus parameters and applying more specific inclusion criteria for implantation, that is: identification of best responders based on the profile of flow curves during overnight sleep studies (Genta et al, 2017;Mann et al, 2019). Moreover, a standardization of outcome data reported in clinical studies is mandatory to improve future device applications (Dedhia & Woodson, 2018).…”