A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea

Study Objectives: To derive and validate a clinical decision rule that can help to prioritize patients who are on waiting lists for polysomnography. Design: Prospective data collection on consecutive patients referred to a sleep center. Setting: The Newcastle Sleep Disorders Centre, University of Ne...

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Main Authors: Julvit Rodsutti, Michael Hensley, Ammarin Thakkinstian, Catherine D'Este, John Attia
Other Authors: Bhumibol Adulyadej Hospital
Format: Article
Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/21622
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spelling th-mahidol.216222018-07-24T10:50:43Z A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea Julvit Rodsutti Michael Hensley Ammarin Thakkinstian Catherine D'Este John Attia Bhumibol Adulyadej Hospital University of Newcastle, Australia Mahidol University Medicine Study Objectives: To derive and validate a clinical decision rule that can help to prioritize patients who are on waiting lists for polysomnography. Design: Prospective data collection on consecutive patients referred to a sleep center. Setting: The Newcastle Sleep Disorders Centre, University of Newcastle, NSW, Australia. Patients: Consecutive adult patients who had been scheduled for initial diagnostic polysomnography. Measurements and Results: Eight hundred and thirty-seven patients were used for derivation of the decision rule. An apnea-hypopnoea index of at least 5 was used as the cutoff point to diagnose sleep apnea. Fifteen clinical features were included in the analyses using logistic regression to construct a model from the derivation data set. Only 5 variables-age, sex, body mass index, snoring, and stopping breathing during sleep-were significantly associated with sleep apnea. A scoring scheme based on regression coefficients was developed, and the total score was trichotomized into low-, moderate-, and high-risk groups with prevalence of sleep apnea of 8%, 51%, and 82%, respectively. Color-coded tables were developed for ease of use. The clinical decision rule was validated on a separate set of 243 patients. Receiver operating characteristic analysis confirmed that the decision rule performed well, with the area under the curve being similar for both the derivation and validation sets: 0.81 and 0.79, P =.612. Conclusion: We conclude that this decision rule was able to accurately classify the risk of sleep apnea and will be useful for prioritizing patients with suspected sleep apnea who are on waiting lists for polysomnography. 2018-07-24T03:50:43Z 2018-07-24T03:50:43Z 2004-06-15 Article Sleep. Vol.27, No.4 (2004), 694-699 10.1093/sleep/27.4.694 01618105 2-s2.0-3042675833 https://repository.li.mahidol.ac.th/handle/123456789/21622 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=3042675833&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Medicine
spellingShingle Medicine
Julvit Rodsutti
Michael Hensley
Ammarin Thakkinstian
Catherine D'Este
John Attia
A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea
description Study Objectives: To derive and validate a clinical decision rule that can help to prioritize patients who are on waiting lists for polysomnography. Design: Prospective data collection on consecutive patients referred to a sleep center. Setting: The Newcastle Sleep Disorders Centre, University of Newcastle, NSW, Australia. Patients: Consecutive adult patients who had been scheduled for initial diagnostic polysomnography. Measurements and Results: Eight hundred and thirty-seven patients were used for derivation of the decision rule. An apnea-hypopnoea index of at least 5 was used as the cutoff point to diagnose sleep apnea. Fifteen clinical features were included in the analyses using logistic regression to construct a model from the derivation data set. Only 5 variables-age, sex, body mass index, snoring, and stopping breathing during sleep-were significantly associated with sleep apnea. A scoring scheme based on regression coefficients was developed, and the total score was trichotomized into low-, moderate-, and high-risk groups with prevalence of sleep apnea of 8%, 51%, and 82%, respectively. Color-coded tables were developed for ease of use. The clinical decision rule was validated on a separate set of 243 patients. Receiver operating characteristic analysis confirmed that the decision rule performed well, with the area under the curve being similar for both the derivation and validation sets: 0.81 and 0.79, P =.612. Conclusion: We conclude that this decision rule was able to accurately classify the risk of sleep apnea and will be useful for prioritizing patients with suspected sleep apnea who are on waiting lists for polysomnography.
author2 Bhumibol Adulyadej Hospital
author_facet Bhumibol Adulyadej Hospital
Julvit Rodsutti
Michael Hensley
Ammarin Thakkinstian
Catherine D'Este
John Attia
format Article
author Julvit Rodsutti
Michael Hensley
Ammarin Thakkinstian
Catherine D'Este
John Attia
author_sort Julvit Rodsutti
title A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea
title_short A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea
title_full A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea
title_fullStr A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea
title_full_unstemmed A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea
title_sort clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/21622
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