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J Clin Sleep Med. 2017 May 15;13(5):693-702. doi: 10.5664/jcsm.6586.

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings.

Author information

1
Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, Valladolid, Spain.
2
Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
3
Unidad Multidisciplinar de Sueño, CIBER Respiratorio, Hospital Universitario de Burgos, Burgos, Spain.
4
Section of Sleep Medicine, Department of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, The University of Chicago, Chicago, Illinois.

Abstract

STUDY OBJECTIVES:

Nocturnal oximetry has become known as a simple, readily available, and potentially useful diagnostic tool of childhood obstructive sleep apnea (OSA). However, at-home respiratory polygraphy (HRP) remains the preferred alternative to polysomnography (PSG) in unattended settings. The aim of this study was twofold: (1) to design and assess a novel methodology for pediatric OSA screening based on automated analysis of at-home oxyhemoglobin saturation (SpO2), and (2) to compare its diagnostic performance with HRP.

METHODS:

SpO2 recordings were parameterized by means of time, frequency, and conventional oximetric measures. Logistic regression models were optimized using genetic algorithms (GAs) for three cutoffs for OSA: 1, 3, and 5 events/h. The diagnostic performance of logistic regression models, manual obstructive apnea-hypopnea index (OAHI) from HRP, and the conventional oxygen desaturation index ≥ 3% (ODI3) were assessed.

RESULTS:

For a cutoff of 1 event/h, the optimal logistic regression model significantly outperformed both conventional HRP-derived ODI3 and OAHI: 85.5% accuracy (HRP 74.6%; ODI3 65.9%) and 0.97 area under the receiver operating characteristics curve (AUC) (HRP 0.78; ODI3 0.75) were reached. For a cutoff of 3 events/h, the logistic regression model achieved 83.4% accuracy (HRP 85.0%; ODI3 74.5%) and 0.96 AUC (HRP 0.93; ODI3 0.85) whereas using a cutoff of 5 events/h, oximetry reached 82.8% accuracy (HRP 85.1%; ODI3 76.7) and 0.97 AUC (HRP 0.95; ODI3 0.84).

CONCLUSIONS:

Automated analysis of at-home SpO2 recordings provide accurate detection of children with high pretest probability of OSA. Thus, unsupervised nocturnal oximetry may enable a simple and effective alternative to HRP and PSG in unattended settings.

KEYWORDS:

at-home respiratory polygraphy; automated pattern recognition; blood oxygen saturation; genetic algorithms; nocturnal oximetry

PMID:
28356177
PMCID:
PMC5406958
DOI:
10.5664/jcsm.6586
[Indexed for MEDLINE]
Free PMC Article

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