ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2022
Correlations between home sleep apnea tests and polysomnography outcomes do not fully reflect the diagnostic accuracy of these tests.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review.Journal of medical systems · 2025Pooled it
- Non-contact on-device detection of obstructive sleep apnea from infrared video.Nature communications · 2026Article
- Clinical Validation of Artificial Intelligence Algorithms for the Diagnosis of Adult Obstructive Sleep Apnea and Sleep Staging From Oximetry and Photoplethysmography-SleepAI.Journal of sleep research · 2026Article
- Comparative efficacy of two types polysomnography in diagnosing and monitoring OSA patients at COVID-19 pandemic: a retrospective study.Sleep & breathing = Schlaf & Atmung · 2025Article
- Evaluating Obstructive Sleep Apnea Utilizing Arterial Tonometry in Individuals with Cystic Fibrosis.Advances in respiratory medicine · 2025Article
- Intraoperative insights: can wearable devices leverage intraoperative data to advance pediatric OSA diagnostics?Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Context-aware analysis enhances autoscoring accuracy of home sleep apnea testing.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- SASBLS: An Advanced Model for Sleep Apnea Detection Based on Single-Channel SpO2.Sensors (Basel, Switzerland) · 2025Article
- Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach.Sleep & breathing = Schlaf & Atmung · 2025Article
- Article
- Home Sleep Apnoea Testing: Advances, Challenges and Considerations in Heart Failure.Cardiac failure review · 2025Review
- Redefining telemedicine in obstructive sleep apnea management through artificial intelligence.Frontiers in sleep · 2025Article
- OSA diagnosis goes wearable: are the latest devices ready to shine?Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Review
- Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning.Scientific reports · 2024Article
- The Correlation of Computerized Scoring in Home Sleep Apnea Tests with Technician Visual Scoring for Assessing the Severity of Obstructive Sleep Apnea.Journal of clinical medicine · 2024Article
- Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing.Sleep & breathing = Schlaf & Atmung · 2024Article
- Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices.Scientific reports · 2024Article
- Screening Tools for Obstructive Sleep Apnea in Pregnant Women: An Extended and Updated Systematic Review and Meta-analysis.International journal of preventive medicine · 2024Review
- Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
- Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.Nature communications · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesThe clinical performance of home sleep apnea tests (HSATs) can be described by their (diagnostic) accuracy, defined as the percentage agreement with the obstructive sleep apnea severity category (normal, mild, moderate, and severe) based on polysomnography. Rather than reporting on accuracy, there has been a strong reliance in the literature to report correlation coefficients between the apnea-hypopnea index of HSATs and polysomnography to support claims of diagnostic performance. This is surprising, as it has been well described that correlation coefficients are inadequate to evaluate equivalence between 2 parameters. The aim of this study was to systematically investigate the magnitude of the discrepancies between correlation coefficients and diagnostic accuracy reported in or retrievable from HSAT validation studies.
methodsWe compared the discrepancy between accuracy and apnea-hypopnea index correlation coefficients of all validation papers that met the inclusion criteria. A total of 20 papers were retained, representing a participant pool of 1,652.
resultsThe weighted average apnea-hypopnea index correlation across all 20 papers was 0.82 and the weighted average accuracy was 0.61, highlighting a discrepancy of 0.21 and an overall misdiagnosis rate of 39%.
conclusionsThe results of our study confirm the need for increased scientific rigor in selecting primary performance endpoints to support clinical performance claims of HSATs. CITATION: Massie F, Van Pee B, Bergmann J. Correlations between home sleep apnea tests and polysomnography outcomes do not fully reflect the diagnostic accuracy of these tests.
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