Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.
Asleep Research Institute, Seoul, South Korea.
JAMA Otolaryngol Head Neck Surg. 2024 Jan 1;150(1):22-29. doi: 10.1001/jamaoto.2023.3490.
Consumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important.
To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.
DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants' own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023.
Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.
Of the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively.
This diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.
消费者级别的睡眠分析技术有可能彻底改变阻塞性睡眠呼吸暂停（OSA）的筛查。然而，基于家庭记录数据的 OSA 预测模型的评估通常是与一级实验室多导睡眠图（PSG）同时进行的。基于家庭二级 PSG 利用智能手机记录的声音数据来建立 OSA 可预测性非常重要。
使用从智能手机记录的呼吸声验证基于家庭二级 PSG 的 OSA 预测模型的性能。
设计、设置和参与者：这是一项前瞻性研究，涉及接受无人值守家庭二级 PSG 的参与者。在参与者自己的家庭环境中，使用两部智能手机（一部运行 iOS 操作系统，另一部运行 Android 操作系统）同时进行睡眠时的呼吸声记录，同时进行家庭 PSG。参与者年龄在 19 岁及以上，独自睡眠，或者已经被诊断为 OSA，或者没有之前的诊断。该研究于 2022 年 2 月至 2023 年 2 月进行。
基于记录呼吸声的预测模型的灵敏度、特异性、阳性预测值、阴性预测值和准确性。
在研究期间，共有 101 名参与者纳入研究，平均（SD）年龄为 48.3（14.9）岁，51 名（50.5%）为女性。对于 iOS 智能手机，呼吸暂停低通气指数（AHI）为 5、15 和 30 时的灵敏度值分别为 92.6%、90.9%和 93.3%，特异性分别为 84.3%、94.4%和 94.4%。同样，对于 Android 智能手机，AHI 为 5、15 和 30 时的灵敏度值分别为 92.2%、90.0%和 92.9%，特异性分别为 84.0%、94.4%和 94.3%。iOS 智能手机的准确率分别为 88.6%、93.3%和 94.3%，Android 智能手机的准确率分别为 88.1%、93.1%和 94.1%，AHI 分别为 5、15 和 30。
这项诊断研究表明，使用智能手机在家庭睡眠期间获得的呼吸声可以合理准确地预测 OSA，具有可行性。
