We updated the design of this site on December 18, 2017. Learn more.
ClinicalTrials.gov Menu

Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea

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.
ClinicalTrials.gov Identifier: NCT01286636
Recruitment Status : Withdrawn
First Posted : January 31, 2011
Last Update Posted : January 13, 2016
Information provided by (Responsible Party):

Study Description
Brief Summary:

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 or disease Intervention/treatment Phase
Sleep Apnea Other: computer model Other: Polysomnogram Phase 3

Study Design

Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 0 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea
Study Start Date : January 2011
Primary Completion Date : March 2015
Study Completion Date : June 2015

Resource links provided by the National Library of Medicine

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

Arms and Interventions

Arm Intervention/treatment
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

Outcome Measures

Primary Outcome Measures :
  1. 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 ]

Eligibility Criteria

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

Ages Eligible for Study:   18 Years to 75 Years   (Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01286636

United States, New York
Veterans Affairs Medical Center in Buffalo
Buffalo, New York, United States, 14215
Sponsors and Collaborators
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
More Information

Responsible Party: Ali El Solh, Professor, State University of New York at Buffalo
ClinicalTrials.gov Identifier: NCT01286636     History of Changes
Other Study ID Numbers: ANN02
First Posted: January 31, 2011    Key Record Dates
Last Update Posted: January 13, 2016
Last Verified: January 2016

Keywords provided by Ali El Solh, State University of New York at Buffalo:
artificial neural network

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