Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea
The investigators have developed a simple, accurate, and a point-of-care, computer-based clinical decision support system (CDSS) not only to detect the presence of sleep apnea but also to predict its severity. The CDSS is based on deploying an artificial neural network (ANN) derived from anthropomorphic and clinical characteristics.
The investigators hypothesize that patients with severe OSA defined as AHI≥30 can be diagnosed with the use of ANN without undergoing a sleep study, and that empiric management with auto-CPAP has similar outcomes to those who undergo a formal sleep study.
|Study Design:||Allocation: Randomized
Endpoint Classification: Efficacy Study
Intervention Model: Parallel Assignment
Masking: Open Label
Primary Purpose: Diagnostic
|Official Title:||Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea|
- To demonstrate that using an ANN directed management of OSA is not inferior to PSG directed management of OSA in terms of sleepiness related functional outcome [ Time Frame: 6 weeks ] [ Designated as safety issue: No ]
|Study Start Date:||January 2011|
|Estimated Study Completion Date:||December 2014|
|Estimated Primary Completion Date:||December 2011 (Final data collection date for primary outcome measure)|
|Experimental: artificial neural network||
Other: computer model
Diagnosis of Sleep apnea and treatment guidance will rely on a computer model prediction.
|Active Comparator: Polysomnogram||
Diagnosis of sleep apnea will rely on polysomnogram
|Contact: Ali El-Solh, MD, MPHfirstname.lastname@example.org|
|United States, New York|
|Veterans Affairs Medical Center in Buffalo||Recruiting|
|Buffalo, New York, United States, 14215|
|Contact: Ali El-Solh, MD, MPH 716-862-8622 email@example.com|
|Principal Investigator:||Ali El-Solh, MD, MPH||State University of New York at Buffalo|