Novel assessment of CPAP adherence data reveals distinct diurnal patterns.

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

E

SUBMITTER: Scharf MT 

PROVIDER: S-EPMC11874086 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature