Single-channel oximetry monitor versus in-lab polysomnography oximetry analysis: does it make a difference? Does it make a difference?

Joachim A. Behar, Niclas Palmius, Sroussi Zacharie, Armand Chocron, Thomas Penzel, Lia Bittencourt, Sergio Tufik

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Objective: Portable oximetry has been shown to be a promising candidate for large-scale obstructive sleep apnea screening. In polysomnography (PSG), the gold standard OSA diagnosis test, the oxygen desaturation index (ODI) is usually computed from desaturation events occurring during sleep periods only, i.e. overnight desaturations occurring during or overlapping with a wake state are excluded. However, for unattended home oximetry, all desaturations are taken into account since no reference electroencephalogram is available for sleep staging. We aim to evaluate the hypothesis that the predictive power of oximetry for OSA screening is not impaired when reference sleep stages are not available. Approach: We used a PSG clinical database of 887 individuals from a representative S o Paulo (Brazil) population sample. Using features derived from the oxygen saturation time series and demographic information, OxyDOSA, a published machine learning model, was trained to distinguish between non-OSA and OSA individuals using the ODI computed while including versus excluding overnight desaturations overlapping with a wake period, thus mimicking portable and PSG oximetry analyses, respectively. Main results: When excluding wake desaturations, the OxyDOSA model had an AUROC = 94.9 ± 1.6, Se = 85.9 ± 2.8, Sp = 90.1 ± 2.6 and F1 = 86.4 ± 2.7. When considering wake desaturations, the OxyDOSA model had an AUROC = 94.4 ± 1.6, Se = 88.0 ± 2.0, Sp = 87.7 ± 2.9 and F1 = 86.2 ± 2.4. Non-inferiority was demonstrated (p = 0.049) at a tolerance level of 3%. In addition, analysis of the desaturations excluded by PSG oximetry analysis suggests that up to 21% of the total number of desaturations might actually be related to apneas or hypopneas. Significance: This analysis of a large representative population sample provided strong evidence that the predictive power of oximetry for OSA screening using the OxyDOSA model is not impaired when reference sleep stages are not available. This finding motivates the usage of portable oximetry for OSA screening.

Original languageEnglish
Article number044007
JournalPhysiological Measurement
Volume41
Issue number4
DOIs
StatePublished - 12 May 2020

Keywords

  • Obstructive sleep apnea screening
  • machine learning
  • mobile health
  • oxygen saturation

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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