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      Assessment of automated analysis of portable oximetry as a screening test for moderate-to-severe sleep apnea in patients with chronic obstructive pulmonary disease

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          Abstract

          Background

          The coexistence of obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality. The development of home-based screening tests is essential to expedite diagnosis. Nevertheless, there is still very limited evidence on the effectiveness of portable monitoring to diagnose OSAS in patients with pulmonary comorbidities.

          Objective

          To assess the influence of suffering from COPD in the performance of an oximetry-based screening test for moderate-to-severe OSAS, both in the hospital and at home.

          Methods

          A total of 407 patients showing moderate-to-high clinical suspicion of OSAS were involved in the study. All subjects underwent (i) supervised portable oximetry simultaneously to in-hospital polysomnography (PSG) and (ii) unsupervised portable oximetry at home. A regression-based multilayer perceptron (MLP) artificial neural network (ANN) was trained to estimate the apnea-hypopnea index (AHI) from portable oximetry recordings. Two independent validation datasets were analyzed: COPD versus non-COPD.

          Results

          The portable oximetry-based MLP ANN reached similar intra-class correlation coefficient (ICC) values between the estimated AHI and the actual AHI for the non-COPD and the COPD groups either in the hospital (non-COPD: 0.937, 0.909–0.956 CI95%; COPD: 0.936, 0.899–0.960 CI95%) and at home (non-COPD: 0.731, 0.631–0.808 CI95%; COPD: 0.788, 0.678–0.864 CI95%). Regarding the area under the receiver operating characteristics curve (AUC), no statistically significant differences ( p >0.01) between COPD and non-COPD groups were found in both settings, particularly for severe OSAS (AHI ≥30 events/h): 0.97 (0.92–0.99 CI95%) non-COPD vs. 0.98 (0.92–1.0 CI95%) COPD in the hospital, and 0.87 (0.79–0.92 CI95%) non-COPD vs. 0.86 (0.75–0.93 CI95%) COPD at home.

          Conclusion

          The agreement and the diagnostic performance of the estimated AHI from automated analysis of portable oximetry were similar regardless of the presence of COPD both in-lab and at-home. Particularly, portable oximetry could be used as an abbreviated screening test for moderate-to-severe OSAS in patients with COPD.

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          Most cited references41

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          Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. Portable Monitoring Task Force of the American Academy of Sleep Medicine.

          Based on a review of literature and consensus, the Portable Monitoring Task Force of the American Academy of Sleep Medicine (AASM) makes the following recommendations: unattended portable monitoring (PM) for the diagnosis of obstructive sleep apnea (OSA) should be performed only in conjunction with a comprehensive sleep evaluation. Clinical sleep evaluations using PM must be supervised by a practitioner with board certification in sleep medicine or an individual who fulfills the eligibility criteria for the sleep medicine certification examination. PM may be used as an alternative to polysomnography (PSG) for the diagnosis of OSA in patients with a high pretest probability of moderate to severe OSA. PM is not appropriate for the diagnosis of OSA in patients with significant comorbid medical conditions that may degrade the accuracy of PM. PM is not appropriate for the diagnostic evaluation of patients suspected of having comorbid sleep disorders. PM is not appropriate for general screening of asymptomatic populations. PM may be indicated for the diagnosis of OSA in patients for whom in-laboratory PSG is not possible by virtue of immobility, safety, or critical illness. PM may also be indicated to monitor the response to non-CPAP treatments for sleep apnea. At a minimum, PM must record airflow, respiratory effort, and blood oxygenation. The airflow, effort, and oximetric biosensors conventionally used for in-laboratory PSG should be used in PM. The Task Force recommends that PM testing be performed under the auspices of an AASM-accredited comprehensive sleep medicine program with written policies and procedures. An experienced sleep technologist/technician must apply the sensors or directly educate patients in sensor application. The PM device must allow for display of raw data with the capability of manual scoring or editing of automated scoring by a qualified sleep technician/technologist. A board certified sleep specialist, or an individual who fulfills the eligibility criteria for the sleep medicine certification examination, must review the raw data from PM using scoring criteria consistent with current published AASM standards. Under the conditions specified above, PM may be used for unattended studies in the patient's home. Afollow-up visit to review test results should be performed for all patients undergoing PM. Negative or technically inadequate PM tests in patients with a high pretest probability of moderate to severe OSA should prompt in-laboratory polysomnography.
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            Pattern Recognition and Machine Learning

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              Global Strategy for the Diagnosis, Management and Prevention of Chronic Obstructive Lung Disease 2017 Report: GOLD Executive Summary.

              This Executive Summary of the Global Strategy for the Diagnosis, Management and Prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017 Report focuses primarily on the revised and novel parts of the document. The most significant changes include: (i) the assessment of chronic obstructive pulmonary disease has been refined to separate the spirometric assessment from symptom evaluation. ABCD groups are now proposed to be derived exclusively from patient symptoms and their history of exacerbations; (ii) for each of the groups A to D, escalation strategies for pharmacological treatments are proposed; (iii) the concept of de-escalation of therapy is introduced in the treatment assessment scheme; (iv)non-pharmacological therapies are comprehensively presented and (v) the importance of co-morbid conditions in managing COPD is reviewed.
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                Author and article information

                Contributors
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draft
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draft
                Role: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draft
                Role: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                27 November 2017
                2017
                : 12
                : 11
                : e0188094
                Affiliations
                [1 ] Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
                [2 ] Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
                Istituti Clinici Scientifici Maugeri, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-4554-2167
                Article
                PONE-D-17-27039
                10.1371/journal.pone.0188094
                5703515
                29176802
                5239f336-c838-4fe9-86b9-1de9d76ec29c
                © 2017 Andrés-Blanco et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 July 2017
                : 31 October 2017
                Page count
                Figures: 5, Tables: 9, Pages: 21
                Funding
                Funded by: Sociedad Española de Neumología y Cirugía Torácica (ES)
                Award ID: 265/2012
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: RTC-2015-3446-1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: TEC2014-53196-R
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008530, European Regional Development Fund;
                Award ID: 0378_AD_EEGWA_2_P
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008431, Consejería de Educación, Junta de Castilla y León;
                Award ID: VA037U16
                Award Recipient :
                Funded by: Consejería de Sanidad, Junta de Castilla y León
                Award ID: GRS 752/A/13
                Award Recipient :
                Funded by: Ministerio de Economía y Competitividad (ES)
                Award ID: IJCI-2014-22664
                Award Recipient :
                This research has been partially supported by the project 265/2012 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), projects RTC-2015-3446-1 and TEC2014-53196-R from the Ministerio de Economía y Competitividad and the European Regional Development Fund (FEDER), and the projects VA037U16 and GRS 752/A/13 from Junta de Castilla y León and FEDER. D. Álvarez was in receipt of a Juan de la Cierva grant IJCI-2014-22664 from the Ministerio de Economía y Competitividad.
                Categories
                Research Article
                Medicine and Health Sciences
                Pulmonology
                Chronic Obstructive Pulmonary Disease
                Computer and Information Sciences
                Artificial Intelligence
                Artificial Neural Networks
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Artificial Neural Networks
                Biology and Life Sciences
                Neuroscience
                Computational Neuroscience
                Artificial Neural Networks
                Physical Sciences
                Chemistry
                Chemical Elements
                Oxygen
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Sleep
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Sleep
                Medicine and Health Sciences
                Clinical Medicine
                Clinical Neurophysiology
                Polysomnography
                Medicine and Health Sciences
                Diagnostic Medicine
                Medicine and Health Sciences
                Pulmonology
                Apnea
                Sleep Apnea
                Medicine and Health Sciences
                Neurology
                Sleep Disorders
                Sleep Apnea
                Medicine and Health Sciences
                Neurology
                Sleep Disorders
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