Literature DB >> 30783913

Interrater agreement between American and Chinese sleep centers according to the 2014 AASM standard.

Shujian Deng1,2,3,4, Xin Zhang1,2,3,4, Ying Zhang1,2,3,4, He Gao5, Eric I-Chao Chang6, Yubo Fan1,2,3,4, Yan Xu7,8,9,10,11,12.   

Abstract

OBJECTIVES: To determine inter-lab reliability in sleep stage scoring using the 2014 American Academy of Sleep Medicine (AASM) manual. To understand in-depth reasons for disagreement and provide suggestions for improvement.
METHODS: This study consisted of 40 all-night polysomnographys (PSGs) from different samples. PSGs were segmented into 37,642 30-s epochs. Five doctors from China and two doctors from America scored the epochs following the 2014 AASM standard. Scoring disagreement between two centers was evaluated using Cohen's kappa (κ). After visual inspection of PSGs of deviating scorings, potential disagreement reasons were analyzed.
RESULTS: Inter-lab reliability yielded a substantial degree (κ = 0.75 ± 0.01). Scoring for stage W (κ = 0.89) and R (κ = 0.87) achieved the highest agreement, while stage N1 (κ = 0.45) reflected the lowest. Considering the relative disagreement ratio, N2-N3 (22.09%), W-N1 (19.68%), and N1-N2 (18.75%) were the most frequent combinations of discrepancy. American and Chinese doctors showed certain characteristics in the scoring of discrepancy combination W-N1, N1-N2, and N2-N3. There are seven reasons for disagreement, namely "on-threshold characteristic" (29.21%), "context influence" (18.06%), "characteristic identification difficulty" (8.81%), "arousal-wake confusion" (7.57%), "derivation inconsistence" (2.15%), "on-borderline characteristic" (0.92%), and "misrecognition" (33.27%).
CONCLUSIONS: This study demonstrated the sleep stage scoring agreement of the 2014 AASM manual and explored potential sources of labeling ambiguity. Improvement measures were suggested accordingly to help remove ambiguity for scorers and improve scoring reliability at the international level.

Keywords:  AASM manual; Discrepancy; Interrater reliability (IRR); Polysomnography (PSG); Sleep stage scoring

Mesh:

Year:  2019        PMID: 30783913     DOI: 10.1007/s11325-019-01801-x

Source DB:  PubMed          Journal:  Sleep Breath        ISSN: 1520-9512            Impact factor:   2.816


  14 in total

1.  Reliability of scoring respiratory disturbance indices and sleep staging.

Authors:  C W Whitney; D J Gottlieb; S Redline; R G Norman; R R Dodge; E Shahar; S Surovec; F J Nieto
Journal:  Sleep       Date:  1998-11-01       Impact factor: 5.849

2.  Limitations of Rechtschaffen and Kales.

Authors:  Sari Leena Himanen; Joel Hasan
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Review 3.  The visual scoring of sleep in adults.

Authors:  Michael H Silber; Sonia Ancoli-Israel; Michael H Bonnet; Sudhansu Chokroverty; Madeleine M Grigg-Damberger; Max Hirshkowitz; Sheldon Kapen; Sharon A Keenan; Meir H Kryger; Thomas Penzel; Mark R Pressman; Conrad Iber
Journal:  J Clin Sleep Med       Date:  2007-03-15       Impact factor: 4.062

4.  Commentary from the Italian Association of Sleep Medicine on the AASM manual for the scoring of sleep and associated events: for debate and discussion.

Authors:  Liborio Parrino; Raffaele Ferri; Marco Zucconi; Francesco Fanfulla
Journal:  Sleep Med       Date:  2009-06-28       Impact factor: 3.492

5.  The American Academy of Sleep Medicine inter-scorer reliability program: sleep stage scoring.

Authors:  Richard S Rosenberg; Steven Van Hout
Journal:  J Clin Sleep Med       Date:  2013-01-15       Impact factor: 4.062

6.  Inter-scorer reliability between sleep centers can teach us what to improve in the scoring rules.

Authors:  Thomas Penzel; Xiaozhe Zhang; Ingo Fietze
Journal:  J Clin Sleep Med       Date:  2013-01-15       Impact factor: 4.062

7.  Discrepancy in polysomnography scoring for a patient with obstructive sleep apnea hypopnea syndrome.

Authors:  Masaaki Suzuki; Hanako Saigusa; Shintaro Chiba; Tomoko Yagi; Kana Shibasaki; Mineko Hayashi; Michiko Suzuki; Kiyoshi Moriyama; Kazuoki Kodera
Journal:  Tohoku J Exp Med       Date:  2005-08       Impact factor: 1.848

8.  The 2007 AASM recommendations for EEG electrode placement in polysomnography: impact on sleep and cortical arousal scoring.

Authors:  Warren R Ruehland; Fergal J O'Donoghue; Robert J Pierce; Andrew T Thornton; Parmjit Singh; Janet M Copland; Bronwyn Stevens; Peter D Rochford
Journal:  Sleep       Date:  2011-01-01       Impact factor: 5.849

9.  Interrater reliability between scorers from eight European sleep laboratories in subjects with different sleep disorders.

Authors:  Heidi Danker-Hopfe; D Kunz; G Gruber; G Klösch; J L Lorenzo; S L Himanen; B Kemp; T Penzel; J Röschke; H Dorn; A Schlögl; E Trenker; G Dorffner
Journal:  J Sleep Res       Date:  2004-03       Impact factor: 3.981

10.  Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard.

Authors:  Heidi Danker-Hopfe; Peter Anderer; Josef Zeitlhofer; Marion Boeck; Hans Dorn; Georg Gruber; Esther Heller; Erna Loretz; Doris Moser; Silvia Parapatics; Bernd Saletu; Andrea Schmidt; Georg Dorffner
Journal:  J Sleep Res       Date:  2009-03       Impact factor: 3.981

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Authors:  Maurice Abou Jaoude; Haoqi Sun; Kyle R Pellerin; Milena Pavlova; Rani A Sarkis; Sydney S Cash; M Brandon Westover; Alice D Lam
Journal:  Sleep       Date:  2020-11-12       Impact factor: 5.849

2.  Interrater reliability of sleep stage scoring: a meta-analysis.

Authors:  Yun Ji Lee; Jae Yong Lee; Jae Hoon Cho; Ji Ho Choi
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3.  Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm.

Authors:  Matteo Cesari; Ambra Stefani; Thomas Penzel; Abubaker Ibrahim; Heinz Hackner; Anna Heidbreder; András Szentkirályi; Beate Stubbe; Henry Völzke; Klaus Berger; Birgit Högl
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Authors:  Diego Alvarez-Estevez; Roselyne M Rijsman
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5.  Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm.

Authors:  Jae Hoon Cho; Ji Ho Choi; Ji Eun Moon; Young Jun Lee; Ho Dong Lee; Tae Kyoung Ha
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6.  Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times.

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