Effect of CPAP on Blood Pressure in Excessively Sleepy Obstructive Sleep Apnea Subtype

Overview

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.

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.

Conditions

Interventions

Eligibility

Locations (2)

Outcomes

Central Contacts