PT  - JOURNAL ARTICLE
AU  - Ali Azarbarzin
AU  - Melania Marques
AU  - Scott A. Sands
AU  - Sara Op de Beeck
AU  - Pedro R. Genta
AU  - Luigi Taranto-Montemurro
AU  - Camila M. de Melo
AU  - Ludovico Messineo
AU  - Olivier M. Vanderveken
AU  - David P. White
AU  - Andrew Wellman
TI  - Predicting epiglottic collapse in patients with obstructive sleep apnoea
AID  - 10.1183/13993003.00345-2017
DP  - 2017 Sep 01
TA  - European Respiratory Journal
PG  - 1700345
VI  - 50
IP  - 3
4099  - http://erj.ersjournals.com/content/50/3/1700345.short
4100  - http://erj.ersjournals.com/content/50/3/1700345.full
SO  - Eur Respir J2017 Sep 01; 50
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