Effect of CPAP on Blood Pressure in Excessively Sleepy Obstructive Sleep Apnea Subtype
Purpose
The primary objective of this study is to determine the longer-term (6 months) effect of CPAP therapy on change in 24-hour mean blood pressure (24hMBP) in OSA subjects with the excessively sleepy symptom subtype.
Condition
- Obstructive Sleep Apnea
Eligibility
- Eligible Ages
- Between 18 Years and 75 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- Age 18-75 years - Moderate-severe OSA (defined as ODI ≥10 events/hour) via Polysomnography (PSG) or Home Sleep Apnea Study (HSAT) done based on clinical grounds - Excessively sleepy subtype determined by patient-reported symptoms - Willing to accept CPAP therapy - An elevated baseline office BP defined as ≥120 or ≥80 mmHg - Planned PAP (CPAP or bi-level PAP) treatment by treating provider
Exclusion Criteria
- Recent changes (within 3 months) to BP medications among those who are on these medications - Unable to apply ABPM cuff - Current use of CPAP or other OSA treatments - Resting, awake SaO2 <90% or use of home oxygen therapy - New York Heart Association (NYHA) categories III-IV of heart failure - Presence of Cheyne-Stokes Respiration (CSR) in sleep study identified by typical crescendo-decrescendo pattern of respiration with associated apneas and/or hypopneas in the absence of inspiratory flow limitation - Predominantly central sleep apnea (AHI≥15 events/hour, with >50% central events [apnea or hypopnea]) - Life expectancy <2 years - Pregnancy - Clinical history of chronic kidney disease (Stage 5) requiring dialysis, or renal transplant - Systolic BP > 180 mmHg
Study Design
- Phase
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
| OSA subjects with the excessively sleepy symptom subtype treated with CPAP | Patients with the excessively sleepy symptom subtype who accept CPAP therapy |
|
Recruiting Locations
Columbus, Ohio 43221
Philadelphia, Pennsylvania 19104
More Details
- NCT ID
- NCT05742360
- Status
- Recruiting
- Sponsor
- Ohio State University
Detailed Description
This is a prospective, non-randomized, observational, two-center study involving newly diagnosed subjects with moderate-severe OSA with the excessively sleepy symptom subtype. Variables of Interest: Change in 24-hour ambulatory BP, change in sitting BP, change in reaction time by psychomotor vigilance test (PVT) Participants will complete questionnaires that pertain to demographics, lifestyle factors, and co-morbidities. The blood samples will be used to determine levels of BP medications and serum creatinine. Measurements will be collected at baseline and at 6-month follow-up visits. Data Analysis Approach: To correct for potential bias in the non-randomized comparison, the investigators will apply a Propensity Score (PS) Design via subclassification. Models to derive the PS values used in this design will include a number of covariates relevant to CPAP adherence, including age, sex, obesity (BMI, neck circumference, waist-to-hip ratio), current smoking, history of hypertension, diabetes mellitus (history, medications), lipid profile, hyperlipidemia (history, medications), family history of premature coronary disease, Charlson comorbidity index, physical activity (IPAQ), diet, OSA severity (AHI, ODI4, T90), sleepiness (Epworth Sleepiness Scale), educational attainment, socioeconomic status (postcode), insomnia symptoms (Insomnia Symptom Questionnaire), anxiety and depression-related symptoms (Patient Health Questionnaire-2), self-efficacy (General self-efficacy scale), and medication adherence (Medication Adherence Report Scale [MARS-5]). Baseline values of outcome measures will also be included in the PS model. After creating the PS design, all analyses are performed accounting for PS subclass as a categorical stratification factor. Evaluations of the CPAP effect on binary outcomes are performed utilizing conditional logistic regression. Similarly, CPAP effects in the context of survival analyses (e.g., Cox Proportional Hazards models) or on continuous outcomes (e.g., linear regression) are assessed by including PS subclass as a categorical covariate in all models.