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Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea

This study is currently recruiting participants.
Verified January 2011 by State University of New York at Buffalo
Sponsor:
Collaborator:
Department of Veterans Affairs
Information provided by:
State University of New York at Buffalo
ClinicalTrials.gov Identifier:
NCT01286636
First received: January 27, 2011
Last updated: July 20, 2011
Last verified: January 2011
History of Changes
  Purpose

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.


Condition Intervention Phase
Sleep Apnea
 Other: computer model
Other: Polysomnogram
 Phase 3

Study Type: Interventional
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

Resource links provided by NLM:

MedlinePlus related topics: Sleep Apnea
U.S. FDA Resources

Further study details as provided by State University of New York at Buffalo:

Primary Outcome Measures:
  • 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 ]

Estimated Enrollment: 56
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)
Arms Assigned Interventions
Experimental: artificial neural network Other: computer model
Diagnosis of Sleep apnea and treatment guidance will rely on a computer model prediction.
Active Comparator: Polysomnogram Other: Polysomnogram
Diagnosis of sleep apnea will rely on polysomnogram

  Eligibility

Ages Eligible for Study:   18 Years and older
Genders Eligible for Study:   Both
Accepts Healthy Volunteers:   No
Criteria

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.
  Contacts and Locations
Please refer to this study by its ClinicalTrials.gov identifier: NCT01286636

Contacts
Contact: Ali El-Solh, MD, MPH 7168628622 solh@buffalo.edu

Locations
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     solh@buffalo.edu    
Sponsors and Collaborators
State University of New York at Buffalo
Department of Veterans Affairs
Investigators
Principal Investigator: Ali El-Solh, MD, MPH State University of New York at Buffalo
  More Information

No publications provided

Responsible Party: Ali El Solh, State University of New York at Buffalo
ClinicalTrials.gov Identifier: NCT01286636     History of Changes
Other Study ID Numbers: ANN02
Study First Received: January 27, 2011
Last Updated: July 20, 2011
Health Authority: United States: Institutional Review Board

Keywords provided by State University of New York at Buffalo:
artificial neural network

Additional relevant MeSH terms:
Apnea
Sleep Apnea Syndromes
Sleep Apnea, Obstructive
Respiration Disorders
Respiratory Tract Diseases
Signs and Symptoms, Respiratory
 Signs and Symptoms
Sleep Disorders, Intrinsic
Dyssomnias
Sleep Disorders
Nervous System Diseases

ClinicalTrials.gov processed this record on May 16, 2013