Suppr超能文献

呼吸事件人工评分中时间和睡眠阶段依赖性的一致性

Temporal and sleep stage-dependent agreement in manual scoring of respiratory events.

作者信息

Pitkänen Minna, Pitkänen Henna, Nath Rajdeep Kumar, Nikkonen Sami, Kainulainen Samu, Korkalainen Henri, Ólafsdóttir Kristín Anna, Arnardottir Erna Sif, Sigurdardottir Sigridur, Penzel Thomas, Fanfulla Francesco, Anttalainen Ulla, Saaresranta Tarja, Grote Ludger, Hedner Jan, Staats Richard, Töyräs Juha, Leppänen Timo

机构信息

Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.

Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland.

出版信息

J Sleep Res. 2025 Jun;34(3):e14391. doi: 10.1111/jsr.14391. Epub 2024 Nov 4.

Abstract

Obstructive sleep apnea diagnosis is based on the manual scoring of respiratory events. The agreement in the manual scoring of the respiratory events lacks an in-depth investigation as most of the previous studies reported only the apnea-hypopnea index or overall agreement, and not temporal, second-by-second or event subtype agreement. We hypothesized the temporal and subtype agreement to be low because the event duration or subtypes are not generally considered in current clinical practice. The data comprised 50 polysomnography recordings scored by 10 experts. The respiratory event agreement between the scorers was calculated using kappa statistics in a second-by-second manner. Obstructive sleep apnea severity categories (no obstructive sleep apnea/mild/moderate/severe) were compared between scorers. The Fleiss' kappa value for binary (event/no event) respiratory event scorings was 0.32. When calculated separately within N1, N2, N3 and R, the Fleiss' kappa values were 0.12, 0.23, 0.22 and 0.23, respectively. Binary analysis conducted separately for the event subtypes showed the highest Fleiss' kappa for hypopneas to be 0.26. In 34% of the participants, the obstructive sleep apnea severity category was the same regardless of the scorer, whereas in the rest of the participants the category changed depending on the scorer. Our findings indicate that the agreement of manual scoring of respiratory events depends on the event type and sleep stage. The manual scoring has discrepancies, and these differences affect the obstructive sleep apnea diagnosis. This is an alarming finding, as ultimately these differences in the scorings affect treatment decisions.

摘要

阻塞性睡眠呼吸暂停的诊断基于呼吸事件的人工评分。对于呼吸事件人工评分的一致性缺乏深入研究,因为大多数先前的研究仅报告了呼吸暂停低通气指数或总体一致性,而没有报告时间上的、逐秒的或事件亚型的一致性。我们推测时间和亚型的一致性较低,因为目前的临床实践中通常不考虑事件持续时间或亚型。数据包括由10位专家评分的50份多导睡眠图记录。评分者之间的呼吸事件一致性采用kappa统计以逐秒的方式计算。比较了评分者之间阻塞性睡眠呼吸暂停的严重程度类别(无阻塞性睡眠呼吸暂停/轻度/中度/重度)。二元(事件/无事件)呼吸事件评分的Fleiss' kappa值为0.32。在N1、N2、N3和R期内分别计算时,Fleiss' kappa值分别为0.12、0.23、0.22和0.23。对事件亚型分别进行的二元分析显示,呼吸浅慢的最高Fleiss' kappa值为0.26。在34%的参与者中,无论评分者如何,阻塞性睡眠呼吸暂停的严重程度类别都是相同的,而在其余参与者中,该类别则因评分者而异。我们的研究结果表明,呼吸事件人工评分的一致性取决于事件类型和睡眠阶段。人工评分存在差异,这些差异会影响阻塞性睡眠呼吸暂停的诊断。这是一个令人担忧的发现,因为最终这些评分差异会影响治疗决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b158/12069727/57ae6b050f60/JSR-34-e14391-g004.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验