Ulysses J Magalang1,2, Jennica N Johns3, Katherine A Wood3, Jesse W Mindel3, Diane C Lim4, Lia R Bittencourt5, Ning-Hung Chen6, Peter A Cistulli7,8, Thorarinn Gíslason9,10, Erna S Arnardottir9,10, Thomas Penzel11, Sergio Tufik5, Allan I Pack4. 1. Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, The Ohio State University Wexner Medical Center, 201 Davis Heart and Lung Research Institute, 473 West 12th Avenue, Columbus, OH, 43210, USA. ulysses.magalang@osumc.edu. 2. Neuroscience Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH, USA. ulysses.magalang@osumc.edu. 3. Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, The Ohio State University Wexner Medical Center, 201 Davis Heart and Lung Research Institute, 473 West 12th Avenue, Columbus, OH, 43210, USA. 4. Center for Sleep and Circadian Neurobiology, Division of Sleep Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. 5. Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil. 6. Division of Pulmonary, Critical Care, and Sleep Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan. 7. Charles Perkins Centre, University of Sydney, Camperdown, Australia. 8. Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, Sydney, Australia. 9. Department of Sleep Medicine, Landspitali University Hospital, Reykjavik, Iceland. 10. Medical Faculty, University of Iceland, Reykjavik, Iceland. 11. Interdisciplinary Center of Sleep Medicine, Charité University Hospital, Berlin, Germany.
Abstract
PURPOSE: To determine the agreement between the manual scoring of home sleep apnea tests (HSATs) by international sleep technologists and automated scoring systems. METHODS: Fifteen HSATs, previously recorded using a type 3 monitor, were saved in European Data Format. The studies were scored by nine experienced technologists from the sleep centers of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC) using the locally available software. Each study was scored separately by human scorers using the nasal pressure (NP), flow derived from the NP signal (transformed NP), or respiratory inductive plethysmography (RIP) flow. The same procedure was followed using two automated scoring systems: Remlogic (RLG) and Noxturnal (NOX). RESULTS: The intra-class correlation coefficients (ICCs) of the apnea-hypopnea index (AHI) scoring using the NP, transformed NP, and RIP flow were 0.96 [95% CI 0.93-0.99], 0.98 [0.96-0.99], and 0.97 [0.95-0.99], respectively. Using the NP signal, the mean differences in AHI between the average of the manual scoring and the automated systems were - 0.9 ± 3.1/h (AHIRLG vs AHIMANUAL) and - 1.3 ± 2.6/h (AHINOX vs AHIMANUAL). Using the transformed NP, the mean differences in AHI were - 1.9 ± 3.3/h (AHIRLG vs AHIMANUAL) and 1.6 ± 3.0/h (AHINOX vs AHIMANUAL). Using the RIP flow, the mean differences in AHI were - 2.7 ± 4.5/h (AHIRLG vs AHIMANUAL) and 2.3 ± 3.4/h (AHINOX vs AHIMANUAL). CONCLUSIONS: There is very strong agreement in the scoring of the AHI for HSATs between the automated systems and experienced international technologists. Automated scoring of HSATs using commercially available software may be useful to standardize scoring in future endeavors involving international sleep centers.
PURPOSE: To determine the agreement between the manual scoring of home sleep apnea tests (HSATs) by international sleep technologists and automated scoring systems. METHODS: Fifteen HSATs, previously recorded using a type 3 monitor, were saved in European Data Format. The studies were scored by nine experienced technologists from the sleep centers of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC) using the locally available software. Each study was scored separately by human scorers using the nasal pressure (NP), flow derived from the NP signal (transformed NP), or respiratory inductive plethysmography (RIP) flow. The same procedure was followed using two automated scoring systems: Remlogic (RLG) and Noxturnal (NOX). RESULTS: The intra-class correlation coefficients (ICCs) of the apnea-hypopnea index (AHI) scoring using the NP, transformed NP, and RIP flow were 0.96 [95% CI 0.93-0.99], 0.98 [0.96-0.99], and 0.97 [0.95-0.99], respectively. Using the NP signal, the mean differences in AHI between the average of the manual scoring and the automated systems were - 0.9 ± 3.1/h (AHIRLG vs AHIMANUAL) and - 1.3 ± 2.6/h (AHINOX vs AHIMANUAL). Using the transformed NP, the mean differences in AHI were - 1.9 ± 3.3/h (AHIRLG vs AHIMANUAL) and 1.6 ± 3.0/h (AHINOX vs AHIMANUAL). Using the RIP flow, the mean differences in AHI were - 2.7 ± 4.5/h (AHIRLG vs AHIMANUAL) and 2.3 ± 3.4/h (AHINOX vs AHIMANUAL). CONCLUSIONS: There is very strong agreement in the scoring of the AHI for HSATs between the automated systems and experienced international technologists. Automated scoring of HSATs using commercially available software may be useful to standardize scoring in future endeavors involving international sleep centers.
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Authors: Ulysses J Magalang; Ning-Hung Chen; Peter A Cistulli; Annette C Fedson; Thorarinn Gíslason; David Hillman; Thomas Penzel; Renaud Tamisier; Sergio Tufik; Gary Phillips; Allan I Pack Journal: Sleep Date: 2013-04-01 Impact factor: 5.849