Exploratory research to Establish the Strategy for Personalized Therapy with CPAP: Association between Patient Profile and Pressure Change Algorithm of Auto-CPAP

Exploratory research to Establish the Strategy for Personalized Therapy with CPAP: Association between Patient Profile and Pressure Change Algorithm of Auto-CPAP - Exploratory research for Personalized Therapy with CPAP

Status

Recruiting

Phases

Unknown

Study type

Interventional

Source

JPRN

Registry ID

JPRN-UMIN000055193

Enrollment

120

Registered

2024-08-07

Start date

2024-08-26

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Obstructive Sleep Apnea

Interventions

The patient uses each mode among three automatic pressure change algorithms for every two weeks, which are soft, normal, and hard modes. We adopted two orders as follows: Pattern A. Soft -&gt

Normal -&gt

Hard Pattern B. Hard -&gt

Soft The order will be randomly assigned and blinded to the patient. The patient uses each mode among three automatic pressure change algorithms for every two weeks, which are soft, normal, and hard

Soft The order will be randomly assigned and blinded to the patient.

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 1. Patients who are newly diagnosed with OSA and satisfy the eligibility for CPAP therapy. 2. Among patients who are already treated with CPAP, patients who present poor CPAP adherence or are not happy with the CPAP device.

Exclusion criteria

Exclusion criteria: 1. Patients with cardiovascular diseases such as stroke, heart failure, ischemic heart disease, and arrhythmia including atrial fibrillation which may cause central sleep apnea with Cheyne-Stokes breathing. 2. Patients with severe COPD and neuromuscular diseases which might cause sleep-related hypoventilation. 3. Patients with uncontrollable other sleep-related disorders including insomnia, hypersomnia, parasomnia, and sleep-related movement disorder.

Design outcomes

Primary

Measure	Time frame
1. Patients preference for three automatic pressure change algorithms 2. Identified Cluster reflecting the patient profile. Clustering is performed using the patient characteristics, symptoms, and polysomnographic parameters. The association between the above two factors will be evaluated.	—

Secondary

Measure	Time frame
1. CPAP adherence and treatment efficacy with each pressure change algorithm. Data is obtained from a CPAP memory card. 2. As for the patients who changed new CPAP from the one they used before they enrolled in this study, the association between patient preference for the pressure change algorithm and previously used CPAP device will be investigated.	—

Countries

Japan

Contacts

Public ContactMotoo Yamauchi

Nara Medical University Department of Clinical Pathophysiology of Nursing / Department of Respiratory Medicine

motoo@naramed-u.ac.jp0744-22-3051

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026