Ignite Creation Date:
2025-12-24 @ 11:34 PM
Ignite Modification Date:
2025-12-25 @ 9:22 PM
Study NCT ID:
NCT04712656
Status:
RECRUITING
Last Update Posted:
2025-07-24
First Post:
2021-01-13
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Effects of CPAP Therapy on Blood Pressure and Heart Rate Variability in Obstructive Sleep Apnea
Sponsor:
Ohio State University
Organization:
Study Overview
Official Title:
Effects of CPAP Therapy on Blood Pressure and Heart Rate Variability in Obstructive Sleep Apnea: Role of Symptom Subtypes
Status:
RECRUITING
Status Verified Date:
2025-07
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 whether the effect of continuous positive airway pressure (CPAP) therapy on 24-hour mean blood pressure (BP) in patients with obstructive sleep apnea (OSA) varies between symptom phenotypes.
Detailed Description:
This is a prospective, non-randomized, multi-center, cohort study involving patients with moderate to severe OSA. This study will compare OSA patients who accept and comply with CPAP therapy versus those who do not.
Variables of Interest: 24-hour ambulatory BP, sitting BP, electrocardiogram (ECG), facial photographs, psychomotor vigilance test (PVT), questionnaires, and blood samples.
Patients will complete questionnaires that pertain to demographics, lifestyle factors, and co-morbidities associated with CVD. The blood samples will be used to look for evidence of diabetes; elevated lipids; markers of heart injury, inflammation, and coagulation; and genetic information. Measurements will be collected at baseline and at 6-month follow-up.
In addition, patients enrolled in this study will be contacted by telephone once a year over a ten year period. The purpose of the telephone interview is to determine if they are using any treatment for OSA, if they have developed any new health problems such as a heart attack or stroke, and if they have changed any of their usual medications.
Data Analysis Approach: To correct for potential bias in the non-randomized comparison, we 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 and cardiovascular outcomes, including age, sex, obesity (BMI, neck circumference, waist-to-hip ratio), current smoking, prevalent CVD at baseline, history of hypertension, HbA1c, 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: 24-hour ambulatory BP, sitting BP, electrocardiogram (ECG), facial photographs, psychomotor vigilance test (PVT), questionnaires, and blood samples.
Patients will complete questionnaires that pertain to demographics, lifestyle factors, and co-morbidities associated with CVD. The blood samples will be used to look for evidence of diabetes; elevated lipids; markers of heart injury, inflammation, and coagulation; and genetic information. Measurements will be collected at baseline and at 6-month follow-up.
In addition, patients enrolled in this study will be contacted by telephone once a year over a ten year period. The purpose of the telephone interview is to determine if they are using any treatment for OSA, if they have developed any new health problems such as a heart attack or stroke, and if they have changed any of their usual medications.
Data Analysis Approach: To correct for potential bias in the non-randomized comparison, we 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 and cardiovascular outcomes, including age, sex, obesity (BMI, neck circumference, waist-to-hip ratio), current smoking, prevalent CVD at baseline, history of hypertension, HbA1c, 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:
True
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?: