Nocturnal oximetry-based evaluation of habitually snoring children

Background: The vast majority of children around the world undergoing adenotonsillectomy for obstructive sleep apnea-hypopnea syndrome (OSA) are not objectively diagnosed with nocturnal polysomnography (NPSG) due to access availability and cost issues. Automated analysis of nocturnal oximetry (nSpO2), which is readily and globally available, could potentially provide a reliable and convenient diagnostic approach for pediatric OSA. Methods: De-identified nSpO2 recordings from a total of 4,191 children originating from 13 pediatric sleep laboratories around the world were prospectively evaluated after developing and validating an automated neural network algorithm using an initial set of single-channel nSpO2 recordings from 589 patients referred for suspected OSA. Results: The automatically estimated apnea-hypopnea index (AHI) showed high agreement with AHI from conventional polysomnography (intra-class correlation coefficient 0.785) when tested in 3,602 additional subjects. Further assessment on the widely-used AHI cut-off points 1, 5 and 10 events/hour revealed an incremental diagnostic ability (75.2%, 81.7%, and 90.2% accuracy; 0.788, 0.854, and 0.913 area under the receiver-operating characteristics curve, respectively). Conclusions: Neural network-based automated analyses of nSpO2 recordings provide accurate identification of OSA severity among habitually snoring children with a high pre-test probability of OSA. Thus, nocturnal oximetry may enable a simple and effective diagnostic alternative to NPSG leading to more timely interventions and potentially improved outcomes.

Jaar van publicatie:2017

Trefwoorden:A1 Journal article

BOF-keylabel:ja

BOF-publication weight:10

CSS-citation score:4

Auteurs:International

Authors from:Higher Education

Toegankelijkheid:Open