规模至关重要：一个基于智能手机的普通人群阻塞性睡眠呼吸暂停筛查的大规模真实世界数据集

文献信息

中文摘要

背景： 阻塞性睡眠呼吸暂停（OSA）是一种高度流行的睡眠相关呼吸障碍，仍存在大量诊断不足，并与心血管和代谢疾病存在因果关联。临床诊断依赖于多导睡眠监测（PSG）或家庭睡眠呼吸暂停检测得出的呼吸暂停低通气指数（AHI）。可在临床环境之外反复使用的低门槛、经临床验证的筛查工具有助于减少诊断不足并获取纵向信息。
方法： 我们描述了一个由医疗设备级、仅使用智能手机的OSA筛查应用程序（Snorefox M）生成的真实世界数据集，该应用程序根据夜间呼吸音计算AHI。纳入2023年3月至2025年12月期间所有知情同意将其匿名数据用于研究的用户。该应用程序的诊断性能此前已通过与同步PSG对照得到验证。本文还描述了在其他AHI阈值下分类性能的事后分析，以及应用程序得出的AHI与PSG得出的AHI之间的线性相关性。
结果： 该数据集包含来自31,647名知情同意成人的239,132次测量。共有14,351名用户贡献了至少两个有效夜晚，产生了221,820个夜晚的纵向筛查数据。在所有尝试的测量中，96.5%获得了有效分析结果。每次测量的平均AHI为14.3（中位数11.8，标准差11.0）。8092名用户的子组提供了自愿自我报告的人口学信息（平均年龄54.4岁，平均ESS评分7.2）。与PSG对照，该应用程序检测中重度OSA（AHI ≥ 15）的敏感性为0.91，特异性为0.83；检测重度OSA（AHI ≥ 30）的敏感性为0.93，特异性为0.97。应用程序得出的AHI与PSG得出的AHI强相关（Pearson r = 0.80，p < 0.001）。应用程序得出的AHI对PSG参考的判别能力为：重度OSA（AHI ≥ 30）的曲线下面积（AUC）为0.951，中重度OSA（AHI ≥ 15）为0.925，AHI ≥ 10阈值为0.877。
结论： 据我们所知，这是使用经临床验证的低门槛智能手机工具、来自普通成人人群的最大规模无监督真实世界OSA筛查纵向数据集。基于智能手机的筛查可以补充既有的诊断路径，支持临床前分诊，并生成单夜检测无法获得的纵向数据，而自我选择和数据采集的无监督性质限制了代表性。通过引入这一资源，本研究为深入分析人口学特征、症状与结果一致性以及夜间变异性提供了基础。

英文摘要

Background: Obstructive sleep apnea (OSA) is a highly prevalent sleep-related breathing disorder that remains substantially underdiagnosed and is causally linked to cardiovascular and metabolic morbidity. Clinical diagnosis relies on the apnea-hypopnea index (AHI) derived from polysomnography (PSG) or home sleep apnea testing. Low-threshold, clinically validated screening tools that can be used repeatedly outside a clinical setting may help to reduce underdiagnosis and to capture longitudinal information. Methods: We describe a real-world dataset generated by a medical device-grade, smartphone-only OSA screening application (Snorefox M) that calculates the AHI from nocturnal breathing sounds. All users who provided informed consent for the research use of their anonymized data between March 2023 and December 2025 were included. The diagnostic performance of the application had previously been validated against parallel PSG. Additional post hoc analysis of classification performance at additional AHI thresholds, together with the linear correlation between the app-derived and PSG-derived AHI are described. Results: The dataset comprises 239,132 measurements from 31,647 consenting adults. A total of 14,351 users contributed at least two valid nights, yielding 221,820 nights of longitudinal screening data. Across all attempted measurements, 96.5% yielded a valid analysis result. The mean per-measurement AHI was 14.3 (median 11.8, SD 11.0). Voluntary, self-reported demographic information was available for a subgroup of 8092 users (mean age 54.4 years, mean ESS score 7.2). Against PSG, the application detected moderate-to-severe OSA (AHI ≥ 15) with a sensitivity of 0.91 and a specificity of 0.83, and severe OSA (AHI ≥ 30) with a sensitivity of 0.93 and a specificity of 0.97. The app-derived AHI correlated strongly with the PSG-derived AHI (Pearson r = 0.80, p < 0.001). The app-derived AHI discriminated against the PSG reference with an area under the curve (AUC) of 0.951 for severe OSA (AHI ≥ 30), 0.925 for moderate-to-severe OSA (AHI ≥ 15) and 0.877 for the AHI ≥ 10 threshold. Conclusions: This is, to our knowledge, the largest longitudinal dataset of unsupervised, real-world OSA screening from a general adult population using a clinically validated, low-threshold smartphone tool. Smartphone-based screening can complement established diagnostic pathways, support pre-clinical triage and generate longitudinal data not accessible through single-night testing, while self-selection and the unsupervised nature of data capture constrain representativeness. By introducing this resource, this work provides the basis for the in-depth analyses of demographics, symptom-result concordance and night-to-night variability.