Evidence map›Paper›PMID 41059504›Full record

ArticleFrontiers in neurology2025

Pulse transit time respiratory swing as a diagnostic test for obstructive sleep apnoea in children-An observational study.

Michael P Yanney, Albert Essiam, Nicola J Rowbotham, Andrew P Prayle

Abstract read
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Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Michael P YanneySherwood Forest Hospitals Foundation Trust, Sutton-in-Ashfield, Nottinghamshire, United Kingdom.
Albert EssiamDepartment of Data Science, Post University, Waterbury, CT, United States.
Nicola J RowbothamLifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
Andrew P PrayleLifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pulse transit time (PTT) has been identified as a potentially useful tool for diagnosing obstructive sleep apnoea (OSA) due to its high sensitivity in detecting cortical arousals. However, its use in clinical practise has been disappointing as it appears to lack the ability to distinguish between individuals with or without OSA. The majority of studies evaluating PTT for sleep-disordered breathing (SDB) have assessed the pulse transit time arousal index (PTT-AI), and there are limited published data on PTT respiratory swing (PTTrs). We previously conducted an observational study of PTT in 368 children with SDB. Our findings indicated that depending on the cut-off used, PTTrs identified OSA with low-to-moderate sensitivity and moderate-to-high specificity, using limited multi-channel sleep studies (MCSS) as the comparator. Methods: We conducted this cross-sectional observational study in another cohort of 1,031 children with SDB who attended a secondary care centre consecutively for MCSS between July 2022 and November 2024. Polysomnography (PSG) is not available in UK secondary care centres, and our use of MCSS in this setting is novel. We analysed the data of 629 children using multinomial regression and machine learning. Results: We found a stepwise increase in PTTrs with increasing severity of SDB. Children with mild OSA had a mean PTTrs of 20.7 ms. Machine learning analysis indicated that the oxygen desaturation index (ODI3) and PTTrs were the most important predictors of SDB amongst the 15 variables studied. Conclusion: Our findings suggest that PTTrs could complement oximetry to improve the detection of OSA in children. A validation study comparing PTTrs with PSG is needed.

Indexed as

childrenmachine learningobstructive sleep apnoeaoximetrypulse transit timesleep-disordered breathingupper airway resistance syndrome

Identifiers

PMID41059504
PMCPMC12497600

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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.