[新型雷达设备对阻塞性睡眠呼吸暂停的诊断:一项平行对照研究,评估与多导睡眠监测的一致性]。

Q4 Medicine
C Y Li, W Wang, W J Huang, H H Xu, H L Yi, J Guan, G Li, S K Yin
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引用次数: 0

摘要

研究目的本研究评估了基于毫米波雷达技术的新型低负荷睡眠监测系统 QSA600 与多导睡眠图(PSG)在诊断阻塞性睡眠呼吸暂停(OSA)方面的一致性。研究方法2023 年 7 月至 9 月,上海市第六人民医院耳鼻咽喉头颈外科睡眠实验室共招募 155 名受试者进行平行协议研究。受试者同时接受了 PSG 和 QSA600 系统的监测。145名受试者中包括75名男性和70名女性,平均年龄(35.30±12.41)岁,平均身高(168.23±8.08)厘米,平均体重(68.28±13.74)公斤。根据呼吸暂停-低通气指数(AHI)将受试者分为四组:r),并采用布兰-阿尔特曼分析评估两种监测技术在 AHI 和其他参数方面的一致性。在不同的 AHI 临界值下,对 QSA600 诊断 OSA 的灵敏度和特异性进行了评估。统计分析使用 MATLAB R2022a 进行。结果如下以 AHI 5 事件/小时、15 事件/小时和 30 事件/小时为阈值,诊断轻度、中度和重度 OSA 的灵敏度分别为 88.68%、89.83% 和 97.06%。特异性分别为 94.87%、98.84% 和 99.10%。接收者操作特征曲线(ROC)下面积分别为 0.973 4、0.990 9 和 0.999 5。QSA600 和 PSG 诊断结果的主要指标比较显示:皮尔逊相关系数为 0.987 2(PCI:-8.74-5.88)events/h,两者之间的 ICC 为 0.985 0(95%CI:0.975 4-0.990 4)。结论作为一种新型低负荷睡眠监测系统,QSA600 在诊断 OSA 及其严重程度分层方面与传统 PSG 具有很高的一致性,具有很好的临床应用前景。(临床试验注册号:NCT06038006)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Diagnosis of obstructive sleep apnea by a new radar device: a parallel controlled study evaluating agreement with polysomnographic monitoring].

Objective: This study evaluates the agreement between a new low-load sleep monitoring system, QSA600, based on millimeter-wave radar technology, and polysomnography (PSG) in diagnosing obstructive sleep apnea (OSA). Methods: A total of 155 subjects were recruited for a parallel agreement study in the sleep laboratory of the Department of Otorhinolaryngology Head and Neck Surgery at Shanghai Sixth People's Hospital from July to September 2023. The subjects underwent simultaneous monitoring with both PSG and the QSA600 system. One hundred and forty-five subjects consisting of 75 males and 70 females included in the final analysis, with an average age of (35.30±12.41) years, an average height of (168.23±8.08) cm, and an average weight of (68.28±13.74) kg. The subjects were divided into four groups based on the apnea-hypopnea index (AHI): <5.0 events/h (non-OSA group, 39 cases), ≥5.0-<15.0 events/h (mild OSA group, 47 cases), ≥15.0-<30.0 events/h (moderate OSA group, 25 cases), and≥30.0 events/h (severe OSA group, 34 cases). Intraclass correlation coefficients (ICC), Pearson correlation coefficients (r), and Bland-Altman analysis were employed to assess the agreement between the two monitoring techniques regarding AHI and other parameters. Sensitivity and specificity of the QSA600 in diagnosing OSA were evaluated at different AHI thresholds. Statistical analyses were conducted using MATLAB R2022a. Results: Using AHI 5 events/h, 15 events/h and 30 events/h as thresholds, the sensitivity for diagnosing mild, moderate, and severe OSA was 88.68%, 89.83% and 97.06%, respectively. The specificity was 94.87%, 98.84% and 99.10%, respectively. The areas under the receiver operating characteristic (ROC) curve was 0.973 4, 0.990 9 and 0.999 5, respectively. The comparison of key indicators between QSA600 and PSG diagnostic results revealed:a Pearson correlation coefficient of 0.987 2(P<0.001) between the AHI measurement values. The mean difference between the Bland-Altman measurement values of the two was -1.43(95%CI:-8.74-5.88) events/h and the ICC between the two was 0.985 0(95%CI: 0.975 4-0.990 4). Conclusions: As a new low-load sleep monitoring system, QSA600 demonstrates high concordance with traditional PSG in diagnosing OSA and stratifying its severity, which has promising potential for clinical application. (Clinical trial registration number: NCT06038006).

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