Ignite Creation Date:
2025-12-25 @ 2:07 AM
Ignite Modification Date:
2025-12-28 @ 12:10 AM
Study NCT ID:
NCT05742360
Status:
RECRUITING
Last Update Posted:
2025-08-13
First Post:
2023-01-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Effect of CPAP on Blood Pressure in Excessively Sleepy Obstructive Sleep Apnea Subtype
Sponsor:
Ohio State University
Organization:
Study Overview
Official Title:
Effect of CPAP on 24-Hour Blood Pressure in the Excessively Sleepy Obstructive Sleep Apnea Subtype
Status:
RECRUITING
Status Verified Date:
2025-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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.
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.
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.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?: