RT Journal Article
SR Electronic
T1 Predicting epiglottic collapse in patients with obstructive sleep apnoea
JF European Respiratory Journal
JO Eur Respir J
FD European Respiratory Society
SP 1700345
DO 10.1183/13993003.00345-2017
VO 50
IS 3
A1 Ali Azarbarzin
A1 Melania Marques
A1 Scott A. Sands
A1 Sara Op de Beeck
A1 Pedro R. Genta
A1 Luigi Taranto-Montemurro
A1 Camila M. de Melo
A1 Ludovico Messineo
A1 Olivier M. Vanderveken
A1 David P. White
A1 Andrew Wellman
YR 2017
UL http://erj.ersjournals.com/content/50/3/1700345.abstract
AB Obstructive sleep apnoea (OSA) is characterised by pharyngeal obstruction occurring at different sites. Endoscopic studies reveal that epiglottic collapse renders patients at higher risk of failed oral appliance therapy or accentuated collapse on continuous positive airway pressure. Diagnosing epiglottic collapse currently requires invasive studies (imaging and endoscopy). As an alternative, we propose that epiglottic collapse can be detected from the distinct airflow patterns it produces during sleep.23 OSA patients underwent natural sleep endoscopy. 1232 breaths were scored as epiglottic/nonepiglottic collapse. Several flow characteristics were determined from the flow signal (recorded simultaneously with endoscopy) and used to build a predictive model to distinguish epiglottic from nonepiglottic collapse. Additionally, 10 OSA patients were studied to validate the pneumotachograph flow features using nasal pressure signals.Epiglottic collapse was characterised by a rapid fall(s) in the inspiratory flow, more variable inspiratory and expiratory flow and reduced tidal volume. The cross-validated accuracy was 84%. Predictive features obtained from pneumotachograph flow and nasal pressure were strongly correlated.This study demonstrates that epiglottic collapse can be identified from the airflow signal measured during a sleep study. This method may enable clinicians to use clinically collected data to characterise underlying physiology and improve treatment decisions.Epiglottic collapse can be identified from airflow characteristics during sleep http://ow.ly/IafB30dbD60