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Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea

This study has been withdrawn prior to enrollment.
Sponsor:
ClinicalTrials.gov Identifier:
NCT01286636
First Posted: January 31, 2011
Last Update Posted: January 13, 2016
The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
Collaborator:
VA Office of Research and Development
Information provided by (Responsible Party):
Ali El Solh, State University of New York at Buffalo
January 27, 2011
January 31, 2011
January 13, 2016
January 2011
March 2015   (Final data collection date for primary outcome measure)
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 ]
Same as current
Complete list of historical versions of study NCT01286636 on ClinicalTrials.gov Archive Site
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Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea
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.

Not Provided
Interventional
Phase 3
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Sleep Apnea
  • Other: computer model
    Diagnosis of Sleep apnea and treatment guidance will rely on a computer model prediction.
  • Other: Polysomnogram
    Diagnosis of sleep apnea will rely on polysomnogram
  • Experimental: artificial neural network
    Intervention: Other: computer model
  • Active Comparator: Polysomnogram
    Intervention: Other: Polysomnogram
Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Withdrawn
0
June 2015
March 2015   (Final data collection date for primary outcome measure)

Inclusion Criteria:

  • Must be an adult (≥18 years old)
  • Must have symptoms suggestive of OSA, and be considered for sleep study by the sleep specialist provider.

Exclusion Criteria:

  • Pregnancy or breast feeding
  • Patients with severe congestive heart failure (eg, NYHA Class IV, ejection fraction < 35%).
  • Patients with end-stage renal disease on hemodialysis
  • Patients with CVA, Parkinson, neuromuscular degenerative disease.
  • Patient on narcotics.
  • Patients with severe lung disease requiring oxygen at night and/or during the day.
  • Patient with predominant insomnia or sleep hygiene problems, and who are not considered for PSG by the sleep specialist.
Sexes Eligible for Study: All
18 Years to 75 Years   (Adult, Senior)
No
Contact information is only displayed when the study is recruiting subjects
United States
 
 
NCT01286636
ANN02
No
Not Provided
Not Provided
Ali El Solh, State University of New York at Buffalo
State University of New York at Buffalo
VA Office of Research and Development
Principal Investigator: Ali El-Solh, MD, MPH State University of New York at Buffalo
State University of New York at Buffalo
January 2016

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP