Two valves in the pharynx

Obstructive sleep apnoea (OSA) is caused by repetitive closure of the upper airway during sleep. While the retropalatal airway is reported to be the most collapsible site [1], any state-dependent segments within the upper airway are candidates for closure. Correct identification of the closure site in each OSA patient could lead to the development of individualised OSA treatment strategies [2]. In this issue of the European Respiratory Journal, Azarbarzin et al. [3] propose a model for prediction of epiglottic collapse for each breath by assessing the nasal airflow waveform in sleeping OSA patients. They employed a machine-learning approach to identify characteristic waveform features for constructing the predictive algorithm and validation of the final predictive model. They found that a nasal airflow signal with greater discontinuity index (rapid and marked reduction of inspiratory airflow immediately after achieving the maximum airflow) and greater jaggedness index (repeated deviations of the airflow from the mean value during inspiration) predicts epiglottic collapse. Furthermore, the non-calibrated nasal pressure signal is demonstrated to be equally usable for determining the epiglottic collapse, increasing applicability to clinical practice. However, it is noteworthy that this study does not completely clarify why the discontinuity and jaggedness features are produced by the epiglottic collapse. Whereas application of machine-learning and artificial intelligence te...
Source: European Respiratory Journal - Category: Respiratory Medicine Authors: Tags: Sleep medicine Editorials Source Type: research