Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, 28 Dian Xin Nan Jie, Chengdu, 610041, Sichuan, China.
Department of Respiratory and Critical Care Medicine, Dazhou Central Hospital, Dazhou, China.
Sleep Breath. 2023 Oct;27(5):1829-1837. doi: 10.1007/s11325-023-02786-4. Epub 2023 Feb 28.
To determine obstructive sleep apnea (OSA) phenotypes using cluster analysis including variables of sleep perception and sleep quality and to further explore factors correlated with poor sleep quality in different clusters.
This retrospective study included patients with OSA undergoing polysomnography (PSG) between December 2020 and April 2022. Two-step cluster analysis was performed to detect distinct clusters using sleep perception variables including discrepancy in total sleep time (TST), sleep onset latency (SOL), and wakefulness after sleep onset (WASO); objective TST, SOL, and WASO; and sleep quality. One-way analysis of variance or chi-squared tests were used to compare clinical and PSG characteristics between clusters. Binary logistic regression analyses were used to explore factors correlated with poor sleep quality.
A total of 1118 patients were included (81.6% men) with mean age ± SD 43.3 ± 13.1 years, Epworth sleepiness score, 5.7 ± 4.4, and insomnia severity index 3.0 ± 2.4. Five distinct OSA clusters were identified: cluster 1 (n = 254), underestimated TST; cluster 2 (n = 158), overestimated TST; cluster 3 (n = 169), overestimated SOL; cluster 4 (n = 155), normal sleep discrepancy and poor sleep quality; and cluster 5 (n = 382), normal sleep discrepancy and good sleep quality. Patients in cluster 2 were older, more commonly had hypertension, and had the lowest apnea-hypopnea index and oxygen desaturation index. Age and sleep efficiency were correlated with poor sleep quality in clusters 1, 2, and 5, and also AHI in cluster 2.
Subgroups of patients with OSA have different patterns of sleep perception and quality that may help us to further understand the characteristics of sleep perception in OSA and provide clues for personalized treatment.
通过包括睡眠感知和睡眠质量变量的聚类分析来确定阻塞性睡眠呼吸暂停（OSA）表型，并进一步探讨不同聚类中与睡眠质量差相关的因素。
本回顾性研究纳入了 2020 年 12 月至 2022 年 4 月期间接受多导睡眠图（PSG）检查的 OSA 患者。采用两步聚类分析，使用包括总睡眠时间（TST）、睡眠潜伏期（SOL）和睡眠后觉醒（WASO）差异、客观 TST、SOL 和 WASO 以及睡眠质量等睡眠感知变量来检测不同的聚类。采用单因素方差分析或卡方检验比较聚类间的临床和 PSG 特征。采用二项逻辑回归分析探讨与睡眠质量差相关的因素。
共纳入 1118 例患者（81.6%为男性），平均年龄±标准差为 43.3±13.1 岁，Epworth 嗜睡评分 5.7±4.4，失眠严重程度指数 3.0±2.4。确定了 5 个不同的 OSA 聚类：聚类 1（n=254），低估 TST；聚类 2（n=158），高估 TST；聚类 3（n=169），高估 SOL；聚类 4（n=155），睡眠差异正常且睡眠质量差；聚类 5（n=382），睡眠差异正常且睡眠质量好。聚类 2 患者年龄较大，更常见高血压，呼吸暂停低通气指数和氧减指数最低。年龄和睡眠效率与聚类 1、2 和 5 中的睡眠质量差相关，在聚类 2 中也与 AHI 相关。
OSA 患者的亚组具有不同的睡眠感知和质量模式，这可能有助于我们进一步了解 OSA 中的睡眠感知特征，并为个性化治疗提供线索。
