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. 2017 Dec 15;196(12):1591-1598.
doi: 10.1164/rccm.201705-0930OC.

Nocturnal Oximetry-based Evaluation of Habitually Snoring Children

Roberto Hornero  1 , Leila Kheirandish-Gozal  2 , Gonzalo C Gutiérrez-Tobal  1 , Mona F Philby  2 , María Luz Alonso-Álvarez  3 , Daniel Álvarez  1   4 , Ehab A Dayyat  5 , Zhifei Xu  6 , Yu-Shu Huang  7 , Maximiliano Tamae Kakazu  8 , Albert M Li  9 , Annelies Van Eyck  10   11 , Pablo E Brockmann  12 , Zarmina Ehsan  13 , Narong Simakajornboon  13 , Athanasios G Kaditis  14 , Fernando Vaquerizo-Villar  1 , Andrea Crespo Sedano  4 , Oscar Sans Capdevila  15 , Magnus von Lukowicz  16 , Joaquín Terán-Santos  3 , Félix Del Campo  1   4 , Christian F Poets  16 , Rosario Ferreira  17 , Katalina Bertran  15 , Yamei Zhang  6 , John Schuen  8 , Stijn Verhulst  10   11 , David Gozal  2
Affiliations

Nocturnal Oximetry-based Evaluation of Habitually Snoring Children

Roberto Hornero et al. Am J Respir Crit Care Med. .

Abstract

Rationale: The vast majority of children around the world undergoing adenotonsillectomy for obstructive sleep apnea-hypopnea syndrome (OSA) are not objectively diagnosed by nocturnal polysomnography because of 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: Deidentified 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.

Measurements and main results: The automatically estimated apnea-hypopnea index (AHI) showed high agreement with AHI from conventional polysomnography (intraclass correlation coefficient, 0.785) when tested in 3,602 additional subjects. Further assessment on the widely used AHI cutoff points of 1, 5, and 10 events/h 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 characteristic curve, respectively).

Conclusions: Neural network-based automated analyses of nSpO2 recordings provide accurate identification of OSA severity among habitually snoring children with a high pretest probability of OSA. Thus, nocturnal oximetry may enable a simple and effective diagnostic alternative to nocturnal polysomnography, leading to more timely interventions and potentially improved outcomes.

Keywords: automated pattern recognition; blood oxygen saturation; childhood obstructive sleep apnea–hypopnea syndrome; neural network; nocturnal oximetry.

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Figures

Figure 1.
Figure 1.
Bland-Altman plot comparing NPSG AHI with the estimated AHI, using the neural network–based algorithm. AHI = apnea–hypopnea index; ICC = intraclass correlation coefficient; NPSG = nocturnal polysomnography.
Figure 2.
Figure 2.
Receiver-operating characteristic curves of neural network–based AHI estimate in the test set for 1, 5, and 10 events per hour (e/h). AHI = apnea–hypopnea index; AUC = area under the curve.
See this image and copyright information in PMC

Comment in

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