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Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

Purpose

A variety of statistical models based on overnight oximetry has been proposed to simplify the detection of children with suspected obstructive sleep apnea syndrome (OSAS). Despite the usefulness reported, additional thorough comparative analyses are required. This study was aimed at assessing common binary classification models from oximetry for the detection of childhood OSAS.

Methods

Overnight oximetry recordings from 176 children referred for clinical suspicion of OSAS were acquired during in-lab polysomnography. Several training and test datasets were randomly composed by means of bootstrapping for model optimization and independent validation. For every child, blood oxygen saturation (SpO2) was parameterized by means of 17 features. Fast correlation-based filter (FCBF) was applied to search for the optimum features. The discriminatory power of three statistical pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR). The performance of each automated model was evaluated for the three common diagnostic polysomnographic cutoffs in pediatric OSAS: 1, 3, and 5 events/h.

Results

Best screening performances emerged using the 1 event/h cutoff for mild-to-severe childhood OSAS. LR achieved 84.3% accuracy (95% CI 76.8–91.5%) and 0.89 AUC (95% CI 0.83–0.94), while QDA reached 96.5% PPV (95% CI 90.3–100%) and 0.91 AUC (95% CI 0.85–0.96%). Moreover, LR and QDA reached diagnostic accuracies of 82.7% (95% CI 75.0–89.6%) and 82.1% (95% CI 73.8–89.5%) for a cutoff of 5 events/h, respectively.

Conclusions

Automated analysis of overnight oximetry may be used to develop reliable as well as accurate screening tools for childhood OSAS.

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Funding

This research has been partially supported by the project 153/2015 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León and European Regional Development Fund (FEDER), and projects RTC-2015-3446-1 and TEC2014-53196-R from the Ministerio de Economía y Competitividad and FEDER. L. Kheirandish-Gozal is supported by NIH grant 1R01HL130984-01. D. Álvarez was in receipt of a Juan de la Cierva grant IJCI-2014-22664 from the Ministerio de Economía y Competitividad.

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Authors and Affiliations

  1. Servicio de Neumología, Hospital Universitario Río Hortega de Valladolid, c/ Dulzaina 2, 47012, Valladolid, Spain

    Andrea Crespo, Daniel Álvarez, Ana Cerezo-Hernández & Félix del Campo

  2. Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain

    Andrea Crespo, Daniel Álvarez, Gonzalo C. Gutiérrez-Tobal, Roberto Hornero & Félix del Campo

  3. Section of Sleep Medicine, Department of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, The University of Chicago, Chicago, IL, 60637, USA

    Leila Kheirandish-Gozal & David Gozal

Authors
  1. Andrea Crespo
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  2. Daniel Álvarez
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  3. Leila Kheirandish-Gozal
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  4. Gonzalo C. Gutiérrez-Tobal
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  5. Ana Cerezo-Hernández
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  6. David Gozal
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  7. Roberto Hornero
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  8. Félix del Campo
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Correspondence to Félix del Campo.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Crespo, A., Álvarez, D., Kheirandish-Gozal, L. et al. Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea. Sleep Breath 22, 1063–1073 (2018). https://doi.org/10.1007/s11325-018-1637-3

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  • DOI: https://doi.org/10.1007/s11325-018-1637-3

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