Faculty of Medicine, University of Porto, Porto, Portugal.
Department of Community Medicine, Information and Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal.
J Med Internet Res. 2023 Jul 26;25:e47735. doi: 10.2196/47735.
Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard.
This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population.
We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures.
We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events.
These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings.
PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748.
数字临床工具是一种新技术,可用于阻塞性睡眠呼吸暂停(OSA)的筛查或诊断,尽管多导睡眠图是金标准。
本研究旨在确定、收集和分析用于成人 OSA 筛查或诊断的最准确的数字工具和基于智能手机的健康平台。
我们对 PubMed、Scopus 和 Web of Science 数据库进行了全面的文献检索,以评估数字工具在 OSA 筛查或诊断中的有效性,检索时间截至 2022 年 11 月。使用 Joanna Briggs 研究所诊断测试准确性研究的批判性评价工具评估偏倚风险。灵敏度、特异性和曲线下面积(AUC)用于区分。
我们检索到 1714 篇文章,其中 41 篇(2.39%)纳入研究。在这 41 篇文章中,我们发现 7 篇(17%)基于智能手机的工具、10 篇(24%)可穿戴设备、11 篇(27%)床或床垫传感器、5 篇(12%)鼻气流装置和 8 篇(20%)其他传感器,这些传感器不属于前几类。仅 8 项(20%)研究对开发工具进行了外部验证。其中,AUC、灵敏度和特异性最高的报告值分别为 0.99、96%和 92%,用于临床截断值 AHI≥30。这些值对应于一个非接触式音频记录器,它记录睡眠声音,然后通过深度学习技术进行分析,自动检测睡眠呼吸暂停事件,计算 AHI,并识别 OSA。对于仅内部验证其模型的研究,报告最高准确性测量值的工作显示 AUC、灵敏度和特异性的截断值为 AHI≥30,分别为 1.00、100%和 96%。它使用的是 Sonomat-a 泡沫床垫,除了记录呼吸声外,还有压力传感器,当变形时会产生电压,从而检测呼吸运动,并用于分类 OSA 事件。
这些临床工具具有较高的区分度指标(最佳结果为 AUC>0.99),结果有前景。然而,在将这些开发工具与金标准进行比较,并在外部人群和其他环境中验证之前,仍需要进行高质量的研究,然后才能将其用于临床环境。
PROSPERO CRD42023387748;https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748。