| 摘要: |
| ［摘要］ 目的 评价基于鼾声分析技术的智能手机软件“睡好了么”（Sleepok）在成人阻塞性睡眠呼吸暂停（OSA）筛查中的应用价值，并与常用OSA筛查量表进行比较。方法 纳入2022年7月至2023年4月因打鼾就诊于北京大学人民医院呼吸睡眠医学科进行多导睡眠监测（PSG）的成人受试者173例，在行PSG时同步使用Sleepok监测鼾声，受试者于监测当晚在研究者指导下填写纸质问卷（包括基本信息和常用OSA筛查量表）。结果 Sleepok与PSG测得呼吸暂停低通气指数（AHI）差异无统计学意义（P>0.05）。Pearson相关分析结果显示，Sleepok_AHI与PSG_AHI具有较好的相关性（r=0.80，P<0.001）。Bland-Altman一致性检验结果显示，Sleepok_AHI与PSG_AHI有较高的一致性（P=0.118），两者之间的平均差异仅为-1.82次/h。ROC曲线分析结果显示，Sleepok_AHI对OSA、中重度OSA和重度OSA均有较好的诊断价值，其最佳截断值分别为16.6次/h、18.9次/h和28.0次/h。单纯鼾症组与OSA组6种量表（No-Apnea、GOAL、NoSAS、STOP、STOP-Bang、CNCQ-OSA）的评分值以及Sleepok_AHI水平比较差异均有统计学意义（P<0.05）。ROC曲线分析结果显示，6种量表均有诊断OSA的应用价值（P<0.05）。结论 基于鼾声分析技术的智能手机软件Sleepok能有效识别睡眠中的呼吸事件，对于OSA高风险患者有较好的灵敏度和特异度，可用于OSA筛查。 |
| 关键词: 阻塞性睡眠呼吸暂停 鼾声分析 多导睡眠监测 筛查量表 |
| DOI：10.3969/j.issn.1674-3806.2024.01.04 |
| 分类号:R 563 |
| 基金项目:北京大学人民医院研究与发展基金资助项目（编号：RDL2022-02） |
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| A study on the application value of smartphone software based on snoring analysis technology in adult obstructive sleep apnea |
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CHEN Yuanyuan1,2, PENG Maohuan2, DONG Xiaosong1,2, ZHAO Rui2, SUN Mingze2, GU Jiahui2, ZHANG Xueli2, ZHAO Long2, ZHOU Bing2, WU Lanbo2, WANG Weihan2, HAN Fang2
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1.Institute of Medical Technology, Peking University, Beijing 100083, China; 2.Department of Respiratory and Sleep Medicine, Peking University People′s Hospital, Beijing 100044, China
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| Abstract: |
| ［Abstract］ Objective To evaluate the application value of smartphone software Sleepok based on snoring analysis technology in adult obstructive sleep apnea(OSA), and to compare its predictive value with the commonly used OSA screening scales for OSA. Methods One hundred and seventy-three adult subjects who came to the clinic in the Department of Respiratory and Sleep Medicine of Peking University People′s Hospital from July 2022 to April 2023 due to snoring and underwent polysomnography(PSG) were enrolled. Sleepok was used to monitor snoring during PSG monitoring. On the night of monitoring, the subjects completed a paper questionnaire(including basic information and commonly used OSA screening scales) under the guidance of the researchers. Results There was not significant difference between apnea hypopnea index(AHI) obtained by Sleepok and that obtained by PSG(P>0.05). The results of Pearson regression analysis showed that Sleepok_AHI had a good correlation with PSG_AHI(r=0.80, P<0.001). Bland-Altman consistency test results showed that Sleepok_AHI and PSG_AHI had higher consistency(P=0.118), and their average difference was only -1.82 events/hour. The results of receiver operating characteristic(ROC) curve analysis showed that Sleepok_AHI had better diagnostic value in OSA, moderate to severe OSA and severe OSA, and their cut-off values were 16.6 events/hour, 18.9 events/hour and 28.0 events/hour, respectively. There were significant differences in the scoring values of 6 scales(No-Apnea, GOAL, NoSAS, STOP, STOP-Bang, CNCQ-OSA) and Sleepok_AHI level between the simple snoring group and the OSA group(P<0.05). The results of ROC curve analysis showed that all the 6 scales had application value in diagnosing OSA(P<0.05). Conclusion The smartphone software Sleepok based on snoring analysis technology can effectively identify respiratory events during sleep, and has better sensitivity and specificity for the patients at high risk of OSA, and can be used for OSA screening. |
| Key words: Obstructive sleep apnea(OSA) Snoring analysis Polysomnography(PSG) Screening scale |
