PT  - JOURNAL ARTICLE
AU  - Dwayne L. Mann
AU  - Philip I. Terrill
AU  - Ali Azarbarzin
AU  - Sara Mariani
AU  - Angelo Franciosini
AU  - Alessandra Camassa
AU  - Thomas Georgeson
AU  - Melania Marques
AU  - Luigi Taranto-Montemurro
AU  - Ludovico Messineo
AU  - Susan Redline
AU  - Andrew Wellman
AU  - Scott A. Sands
TI  - Quantifying the magnitude of pharyngeal obstruction during sleep using airflow shape
AID  - 10.1183/13993003.02262-2018
DP  - 2019 Jul 01
TA  - European Respiratory Journal
PG  - 1802262
VI  - 54
IP  - 1
4099  - http://erj.ersjournals.com/content/54/1/1802262.short
4100  - http://erj.ersjournals.com/content/54/1/1802262.full
SO  - Eur Respir J2019 Jul 01; 54
AB  - Rationale and objectives Non-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.Methods 41 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).Results Gold-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.Conclusions The 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.The degree of pharyngeal airflow obstruction varies widely for any given OSA severity (apnoea–hypopnoea index) and is challenging to measure. Here we combine information from automated flow shape to accurately estimate the severity of airflow obstruction. http://bit.ly/2uYD0rf