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消费者睡眠技术在阻塞性睡眠呼吸暂停和打鼾筛查中的应用:现状及系统评价和诊断准确性荟萃分析方案。

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy.

机构信息

Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil.

European Sleep Research Society (ESRS), Regensburg, Germany.

出版信息

J Sleep Res. 2023 Aug;32(4):e13819. doi: 10.1111/jsr.13819. Epub 2023 Feb 17.

DOI:10.1111/jsr.13819
PMID:36807680
Abstract

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

摘要

目前对于消费者睡眠技术在睡眠呼吸障碍中的验证和准确性存在一些担忧。本报告提供了对现有消费者睡眠技术的背景审查,并披露了对这些设备和应用程序进行诊断测试准确性的系统评价和荟萃分析的方法和程序,以比较多导睡眠图检测阻塞性睡眠呼吸暂停和打鼾。搜索将在四个数据库(PubMed、Scopus、Web of Science 和 Cochrane Library)中进行。研究将分两步进行选择,首先是对摘要进行分析,然后是对全文进行分析,两位独立的审稿人将进行这两个阶段。主要结果包括阻塞性睡眠呼吸暂停低通气指数、呼吸干扰指数、呼吸事件指数、氧减指数和鼾声持续时间(用于指数和参考测试),以及每个阈值的真阳性、假阳性、真阴性和假阴性的数量,以及逐epoch 和逐事件的结果,这些结果将用于计算替代指标(包括敏感性、特异性和准确性)。使用 Chu 和 Cole 双变量二项式模型对诊断测试准确性进行荟萃分析。使用 DerSimonian 和 Laird 随机效应模型对连续结果进行均值差异荟萃分析。将对每个结果进行独立分析。亚组和敏感性分析将评估类型(可穿戴设备、近场设备、床传感器、智能手机应用程序)、技术(例如,血氧计、麦克风、动脉张力计、加速度计)、制造商的作用以及样本的代表性的影响。

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