Feasibility of Single Channel Oximetry for Mass Screening of Obstructive Sleep Apnea
Open accessAbstract
BACKGROUND: The growing awareness for the high prevalence of obstructive sleep apnea (OSA) coupled with the dramatic proportion of undiagnosed individuals motivates the elaboration of a simple but accurate screening test. This study assesses, for the first time, the performance of oximetry combined with demographic information as a screening tool for identifying OSA in a representative (i.e. non-referred) population sample. METHODS: A polysomnography (PSG) clinical database of 887 individuals from a representative population sample of São Paulo's city (Brazil) was used. Using features derived from the oxygen saturation signal during sleep periods and demographic information, a logistic regression model (termed OxyDOSA) was trained to distinguish between non-OSA and OSA individuals (mild, moderate, and severe). The OxyDOSA model performance was assessed against the PSG-based diagnosis of OSA (AASM 2017) and compared to the NoSAS and STOP-BANG questionnaires. FINDINGS: The OxyDOSA model had mean AUROC = 0.94 ± 0.02, Se = 0.87 ± 0.04 and Sp = 0.85 ± 0.03. In particular, it did not miss any of the 75 severe OSA individuals. In comparison, the NoSAS questionnaire had AUROC = 0.83 ± 0.03, and missed 23/75 severe OSA individuals. The STOP-BANG had AUROC = 0.77 ± 0.04 and missed 14/75 severe OSA individuals. INTERPRETATION: We provide strong evidence on a representative population sample that oximetry biomarkers combined with few demographic information, the OxyDOSA model, is an effective screening tool for OSA. Our results suggest that sleep questionnaires should be used with caution for OSA screening as they fail to identify many moderate and even some severe cases. The OxyDOSA model will need to be further validated on data recorded using overnight portable oximetry.
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A model has been developed to identify OSA using data that could be obtained from a wearable user (pulse oximetry biomarkers and demographic information).
“a model has been developed to identify OSA using data, which could be obtained from a wearable user (pulse oximetry biomarkers and demographic information)”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [206]
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- Authors
- Behar, J. A., Palmius, N., Li, Q., Garbuio, S., Rizzatti, F. P. G., Bittencourt, L., Tufik, S., & Clifford, G. D.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- University of Oxford (GB)
- Georgia Institute of Technology (US)
- Universidade Federal de São Carlos (BR)
- Universidade Federal de São Paulo (BR)
- Corresponding author
- Behar, Joachim A.
- Venue
- EClinicalMedicine
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 11
- Pages
- 81-88
- ISSN
- 2589-5370 (ISSN-L)
- PMC
- PMC6611093
- OpenAlex
- W2950062353
- Cited by
- 37
- References
- 34
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4 · 2023: 9 · 2022: 5 · 2021: 6 · 2020: 5
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EClinicalMedicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (306138/2017-4, 309336/2017-1, 401569/2016-0)
- Associação Fundo de Incentivo à Pesquisa
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Topics
- Health Sciences › Medicine › Physiology › Obstructive Sleep Apnea Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- obstructive sleep apnea, sleep apnea screening, oxygen saturation, logistic regression, polysomnography, STOP-BANG questionnaire, NoSAS, sleep questionnaires, demographic factors, undiagnosed sleep apnea
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