ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2025
Context-aware analysis enhances autoscoring accuracy of home sleep apnea testing.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04191668 (A Validation Study of the NightOwl PAT-based Home Sleep Apnea Test), which is not on this map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Validation Study of the NightOwl PAT-based Home Sleep Apnea Test
Who cites it
2 citing papers in PubMed.
- Advancements, challenges, and prospects of explainable AI in sleep disordered breathing.Sleep & breathing = Schlaf & Atmung · 2026Review
- Multimodal physiological signal learning for clinical sleep respiratory event recognition.Frontiers in neurology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesHome sleep apnea testing based on peripheral arterial tonometry is increasingly being deployed because of its ability to test for multiple nights. However, home sleep apnea tests based on peripheral arterial tonometry do not have access to modalities such as airflow and cortical arousals and instead rely on alternative sources of information to detect respiratory events. This results in an a priori performance disadvantage. In this study, we describe the Panorama algorithm, which aims to reduce this disadvantage by incorporating information from characteristically repetitive sequences in physiological changes associated with respiratory events. These include changes in peripheral arterial tone, pulse rate, and oxygen saturation. The method was designed to facilitate manual review by providing the scoring rationale for each respiratory event.
methodsThe method was developed and evaluated using a dataset of 266 participants from a multicentric cohort suspected of having obstructive sleep apnea. All participants underwent simultaneous polysomnography and home sleep apnea testing based on peripheral arterial tonometry, and all polysomnography data were double-scored. Scoring was performed according to the 3% and 4% rules for hypopnea scoring. Clinical endpoint parameters, including the obstructive sleep apnea severity categorization accuracy and Cohen's kappa, were selected to compare the algorithm to a conventional context-unaware algorithm. Data analysis and reporting followed the TRIPOD+AI reporting guidance for prediction models that use machine learning.
resultsRegarding obstructive sleep apnea severity categorization accuracy, the Panorama algorithm significantly outperformed context-unaware autoscoring by 9% using 3% rule scoring and 7% using 4% rule scoring.
conclusionsThe context-aware method significantly improves the performance of home sleep apnea tests based on peripheral arterial tonometry while still facilitating scoring review by providing event-specific scoring rationale. CLINICAL
trial registrationRegistry: ClinicalTrials.gov; Name: A Validation Study of the NightOwl PAT-based Home Sleep Apnea Test; URL: https://clinicaltrials.gov/ct2/show/NCT04191668; Identifier: NCT04191668. CITATION: Massie F, Vits S, Verbraecken J, Bergmann J. Context-aware analysis enhances autoscoring accuracy of home sleep apnea testing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.