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: DeidentifiednSpO2 recordings froma total of 4,191 children
originating from13 pediatric sleep laboratories around the worldwere
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.
