Nocturnal Oximetry–based Evaluation of Habitually Snoring Children

• Published in: American journal of respiratory and critical care medicine Vol. 196; no. 12; pp. 1591 - 1598
• Main Authors: Hornero, Roberto, Kheirandish-Gozal, Leila, Gutiérrez-Tobal, Gonzalo C, Philby, Mona F, Alonso-Álvarez, María Luz, Álvarez, Daniel, Dayyat, Ehab A, Xu, Zhifei, Huang, Yu-Shu, Kakazu, Maximiliano Tamae, Li, Albert M, Van Eyck, Annelies, Brockmann, Pablo E, Ehsan, Zarmina, Simakajornboon, Narong, Kaditis, Athanasios G, Vaquerizo-Villar, Fernando, Sedano, Andrea Crespo, Capdevila, Oscar Sans, von Lukowicz, Magnus, Terán-Santos, Joaquín, Del Campo, Félix, Poets, Christian F, Ferreira, Rosario, Bertran, Katalina, Zhang, Yamei, Schuen, John, Verhulst, Stijn, Gozal, David
• Format: Journal Article
• Language: English
• Published: New York American Thoracic Society 15.12.2017
• Subjects: Artificial intelligence; Automation; Laboratories; Medicine; Neural networks; Pattern recognition; Pediatrics; Sleep apnea; Sleep disorders; New York; United States > US
• ISSN: 1073-449X; 1535-4970
• DOI: 10.1164/rccm.201705-09300C

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: DeidentifiednSp^ recordings from atotal 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 nSpOj 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 nSpO 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.