Predicting epiglottic collapse in patients with obstructive sleep apnoea

Abstract

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.

Figure 1

In this simplified example classification scheme (support vector machine), we take 22 breaths, 13 of which have epiglottic collapse. a) Two characteristics (features) are highlighted, the discontinuity index (D1) and respiratory parameter ( $\frac{{\stackrel{.}{\mathrm{V}}\text{max}}_{\mathrm{E}}}{\text{VT}}$: the ratio of peak expiratory flow and tidal volume), and overlaid on the flow trace. b) Plot of characteristics for breaths with epiglottic collapse (circles) versus without epiglottic collapse (triangles). The classifier finds a linear boundary between groups that maximizes the margin of error (arrows, dashed lines).

Figure 2

(a) Epiglottic collapse accompanied by distinct flow characteristics (e.g. discontinuities in inspiratory flow). The pressure above the epiglottis (P₅ and P₆, downstream to P₅) closely follows the mask pressure, confirming that the airway above the epiglottis is patent. (b) Examples of epiglottic collapse associated with sudden flow change.

Figure 3

Non-epiglottic pharyngeal collapse often produces a “flat-top” flow shape. (a) The multi-tip pressure tracings suggest that there is a choke point between P₃ and P₄. (b) Example traces of non-Epiglottic collapse.

Figure 4

Large value of the discontinuity index (D1) and the inspiratory jaggedness index (JI_i) predict epiglottic collapse. The left column displays the flow patterns associated with high values of the discontinuity index and jaggedness index whereas the right column represents the flow patterns associated with low values of these features.

Figure 5

The discontinuity and jaggedness features associated with epiglottis collapse were reliably captured by nasal cannula. These features were preserved in these example breaths that were simultaneously collected using pneumotachograph (first (top) panel, V̇) and a nasal cannula (second panel, PN). To estimate the pneumotach flow, the nasal pressure signal was passed through a square root transformation (third panel, V̇_PN^0.5). The bottom panel shows the pressure above the epiglottis (Pepi), indicating epiglottic collapse.

Figure 6

The discontinuity and jaggedness features obtained from pneumotachograph measured flow (V̇) were strongly correlated with their corresponding values obtained from nasal pressure (PN). A stronger correlation was obtained when nasal pressure was transformed (V̇_PN^0.5, bottom row) compared with untransformed nasal pressure (PN, top row).