持续气道正压通气依从性数据的新评估揭示了不同的昼夜模式。

Novel assessment of CPAP adherence data reveals distinct diurnal patterns.

作者信息

Scharf Matthew T, Androulakis Ioannis P

机构信息

Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey.

Department of Neurology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey.

出版信息

J Clin Sleep Med. 2025 Mar 1;21(3):493-502. doi: 10.5664/jcsm.11446.

DOI:10.5664/jcsm.11446

PMID:39484806

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11874086/

Abstract

STUDY OBJECTIVES

Obstructive sleep apnea is a prevalent condition effectively treated by continuous positive airway pressure (CPAP) therapy. CPAP adherence data, routinely gathered in clinical practice, include detailed information regarding both duration and timing of use. The purpose of the present study was to develop a systematic way to measure the diurnal pattern of CPAP adherence data and to see if distinct patterns exist in a clinical cohort.

METHODS

Machine learning techniques were employed to analyze CPAP adherence data. A cohort of 200 unselected patients was assessed and a cluster analysis was subsequently performed. Application of this methodology to 17 patients with different visually noted patterns was carried out to further assess performance.

RESULTS

Each 30-day period of CPAP use for each patient was characterized by 4 variables describing the time of day of initiation and discontinuation of CPAP use, as well as the consistency of use during those times. Further analysis identified 6 distinct clusters, reflecting different timing and adherence patterns. Specifically, clusters with relatively normal timing vs delayed timing were identified. Finally, application of this methodology showed generally good performance with limitations in the ability to characterize shift worker and non-24 rhythms.

CONCLUSIONS

This study demonstrates a methodology for analysis of diurnal patterns from CPAP adherence data. Furthermore, distinct timing and adherence patterns are demonstrated. The potential impact of these patterns on the beneficial effects of CPAP requires elucidation.

CITATION

Scharf MT, Androulakis IP. Novel assessment of CPAP adherence data reveals distinct diurnal patterns. . 2025;21(3):493-502.

摘要

研究目的

阻塞性睡眠呼吸暂停是一种常见病症，可通过持续气道正压通气（CPAP）疗法有效治疗。在临床实践中常规收集的CPAP依从性数据包括有关使用持续时间和时间安排的详细信息。本研究的目的是开发一种系统方法来测量CPAP依从性数据的昼夜模式，并观察在临床队列中是否存在不同模式。

方法

采用机器学习技术分析CPAP依从性数据。对200名未经选择的患者进行队列评估，随后进行聚类分析。将该方法应用于17名具有不同视觉观察模式的患者，以进一步评估其性能。

结果

每位患者使用CPAP的每30天周期由4个变量表征，这些变量描述了开始和停止使用CPAP的一天中的时间，以及这些时间段内使用的一致性。进一步分析确定了6个不同的聚类，反映了不同的时间安排和依从模式。具体而言，确定了时间安排相对正常与延迟的聚类。最后，该方法的应用显示出总体良好的性能，但在表征轮班工作者和非24小时节律方面存在局限性。