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CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study

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ClinicalTrials.gov Identifier: NCT00497640
Recruitment Status : Withdrawn
First Posted : July 6, 2007
Last Update Posted : January 15, 2016
Sponsor:
Information provided by:
State University of New York at Buffalo

Brief Summary:
The purpose of the study is to determine the validity of the prediction model in reducing the rate of CPAP titration failure and in achieving a shorter time to optimal pressure

Condition or disease Intervention/treatment
Obstructive Sleep Apnea Procedure: Artificial Neural Network

Detailed Description:

In order to derive the most effective pressure, CPAP titration is performed in the sleep laboratory during which the pressure is gradually increased until apneas and hypopneas are abolished in all sleep stages and in all body positions. The technique is however time consuming and labor intensive. Furthermore, the duration of the study may not be sufficient to attain this goal because of patient's poor ability to sleep in this environment or due to difficulty in attaining an appropriate pressure. A predictive algorithm based on demographic, anthropometric, and polysomnographic data was developed to facilitate the selection of a starting pressure during the overnight titration study. Yet, the performance of this model was inconsistent when validated by other centers. One of the potential reasons for the lack of reproducibility is the complex relation of behavioral processes with nonlinear attributes. In areas of complex interactions, the artificial neural network (ANN) has been found to be a more appropriate alternative to linear, parametric statistical tools due to its inherent property of seeking information embedded in relations among variables thought to be independent.

Comparison: time to achieve optimal pressure in the conventional technique versus the intervention model


Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 0 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study
Study Start Date : May 2007
Primary Completion Date : July 2008
Study Completion Date : June 2009

Intervention Details:
    Procedure: Artificial Neural Network
    Use of a predicted optimal CPAP


Primary Outcome Measures :
  1. Time to achieve optimal CPAP [ Time Frame: minutes ]

Secondary Outcome Measures :
  1. Failure Rate of CPAP titration [ Time Frame: percentage ]


Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 80 Years   (Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  1. patients 18 years of age and older,
  2. documented OSA by sleep study defined as AHI > 5/hr

Exclusion Criteria:

  1. previously treated OSA,
  2. unwilling to undergo a titration study,
  3. unable or unwilling to sign an informed consent.

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): NCT00497640


Locations
United States, New York
State University of New York at Buffalo
Buffalo, New York, United States, 14215
Sponsors and Collaborators
State University of New York at Buffalo
Investigators
Principal Investigator: Ali A El Solh, MD, MPH Sate University of New York at Buffalo

Publications:
Responsible Party: Ali El Solh, State University of New York at Buffalo
ClinicalTrials.gov Identifier: NCT00497640     History of Changes
Other Study ID Numbers: MED4890507E
First Posted: July 6, 2007    Key Record Dates
Last Update Posted: January 15, 2016
Last Verified: September 2009

Keywords provided by State University of New York at Buffalo:
sleep apnea, titration, CPAP, neural network

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