Prevalence of Sleep Apnea and Atrial Fibrillation
Purpose
The investigators will screen consecutive patients presenting to the atrial fibrillation clinic for sleep apnea using a FDA-approved home sleep testing device, WatchPAT to determine prevalence of sleep apnea in a clinic-based sample.
Conditions
- Sleep Apnea
- Atrial Fibrillation
Eligibility
- Eligible Ages
- Over 18 Years
- Eligible Genders
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- Diagnosis of AF confirmed on any electrocardiographic recording - Age greater than or equal to 18 - Ability to read and understand consent form, complete questionnaires, and provide informed consent
Exclusion Criteria
- Prior diagnosis of sleep disordered breathing or negative testing within the past 2 years without significant change in weight or clinical symptoms of sleep apnea - History of Raynaud
Study Design
- Phase
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Recruiting Locations
Boston, Massachusetts 02215
More Details
- Status
- Recruiting
- Sponsor
- Brigham and Women's Hospital
Detailed Description
The investigators will determine the prevalence of sleep apnea among consecutive patients with atrial fibrillation (AF) regardless of symptoms and determine the predictive value of screening tools for sleep apnea in this population. The primary outcome will be the prevalence of sleep apnea among all-comers with AF. Three secondary outcomes will be evaluated in the study. Investigators will determine the efficiency and time-to-diagnosis of sleep apnea using a FDA approved home sleep testing device (i.e. WatchPAT). These analyses will be compared to published studies performed using a traditional home sleep study test (which historically takes approximately 8-10 weeks). There will be no collection of data from a non-intervention group in our study. Second, investigtaors will calculate the predictive value of traditional clinical screening tools in an AF population using the STOP-BANG questionnaire and Epworth Sleepiness Scale. Third, investigators will identify potential risk factors for AF in patients with sleep apnea using a logistic regression model.