Department of Respiratory Medicine, Nara Medical University, 840 Shijocho, Kashihara, Nara, 634-8521, Japan.
Department of Respiratory Medicine, Nara Medical University, 840 Shijocho, Kashihara, Nara, 634-8521, Japan; Department of Clinical Pathophysiology of Nursing, Nara Medical University, 840 Shijocho, Kashihara, Nara, 634-8521, Japan.
Sleep Med. 2024 Oct;122:177-184. doi: 10.1016/j.sleep.2024.08.018. Epub 2024 Aug 20.
Continuous positive airway pressure (CPAP) is the standard treatment for obstructive sleep apnea (OSA). Unsatisfactory adherence to CPAP is an important clinical issue to resolve. Cluster analysis is a powerful tool to distinguish subgroups in a multidimensional fashion. This study aimed to investigate the use of cluster analysis for predicting CPAP adherence using clinical polysomnographic (PSG) parameters and patient characteristics.
PATIENTS/METHODS: Participants of this multicenter observational study were 1133 patients with OSA who were newly diagnosed and implemented CPAP. Ward's method of cluster analysis was applied to in-laboratory diagnostic PSG parameters and patient characteristics. CPAP adherence was assessed during 90- and 365-day periods after CPAP initiation in each cluster. We adopted the Centers for Medicare and Medicaid Services criterion for CPAP adherence, i.e., CPAP use ≥4 h per night for 70 % or more of the observation period. Logistic regression analysis was performed to stratify clusters according to CPAP adherence.
Five clusters were identified through cluster analysis. Clustering was significantly associated with CPAP adherence at 90- and 365-day periods after CPAP initiation. Logistic regression revealed that the cluster with features including apnea predominant sleep-disordered breathing, high apnea-hypopnea index, and relatively older age demonstrated the highest CPAP adherence.
Cluster analysis revealed hidden connections using patient characteristics and PSG parameters to successfully identify patients more likely to adhere to CPAP for 90 days and up to 365 days. When prescribing CPAP, it is possible to identify patients with OSA who are more likely to be non-adherent.
持续气道正压通气（CPAP）是治疗阻塞性睡眠呼吸暂停（OSA）的标准方法。CPAP 治疗的依从性差是一个重要的临床问题，需要解决。聚类分析是一种以多维方式区分亚组的强大工具。本研究旨在通过临床多导睡眠图（PSG）参数和患者特征，利用聚类分析预测 CPAP 依从性。
患者/方法：这项多中心观察性研究的参与者是 1133 名新诊断为 OSA 并开始使用 CPAP 的患者。采用 Ward 聚类分析方法对实验室诊断 PSG 参数和患者特征进行聚类分析。在每个聚类中，评估 CPAP 启动后 90 天和 365 天的 CPAP 依从性。我们采用医疗保险和医疗补助服务中心的 CPAP 依从性标准，即 CPAP 使用率≥每晚 4 小时，且观察期内 70%以上的时间使用。采用逻辑回归分析根据 CPAP 依从性对聚类进行分层。
通过聚类分析确定了 5 个聚类。聚类分析与 CPAP 启动后 90 天和 365 天的 CPAP 依从性显著相关。逻辑回归显示，具有以睡眠呼吸暂停为主的睡眠呼吸障碍、高呼吸暂停低通气指数和相对较老年龄特征的聚类患者 CPAP 依从性最高。
聚类分析利用患者特征和 PSG 参数揭示了隐藏的联系，成功地识别了更有可能在 90 天和 365 天内坚持使用 CPAP 的患者。在开具 CPAP 时，可以识别出更有可能不依从的 OSA 患者。
