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

This study has been completed.
Sponsor:
Information provided by:
State University of New York at Buffalo
ClinicalTrials.gov Identifier:
NCT00497640
First received: July 5, 2007
Last updated: September 18, 2009
Last verified: September 2009
  Purpose

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 Intervention
Obstructive Sleep Apnea
Procedure: Artificial Neural Network

Study Type: Interventional
Study Design: Allocation: Randomized
Endpoint Classification: Bio-equivalence Study
Intervention Model: Parallel Assignment
Masking: Open Label
Primary Purpose: Diagnostic
Official Title: CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study

Resource links provided by NLM:


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

Primary Outcome Measures:
  • Time to achieve optimal CPAP [ Time Frame: minutes ] [ Designated as safety issue: No ]

Secondary Outcome Measures:
  • Failure Rate of CPAP titration [ Time Frame: percentage ] [ Designated as safety issue: No ]

Estimated Enrollment: 120
Study Start Date: May 2007
Study Completion Date: June 2009
Primary Completion Date: July 2008 (Final data collection date for primary outcome measure)
Intervention Details:
    Procedure: Artificial Neural Network
    Use of a predicted optimal CPAP
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

  Eligibility

Ages Eligible for Study:   18 Years to 80 Years
Genders Eligible for Study:   Both
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.
  Contacts and Locations
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Please refer to this study by its ClinicalTrials.gov identifier: 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
  More Information

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
Study First Received: July 5, 2007
Last Updated: September 18, 2009
Health Authority: United States: Institutional Review Board

Keywords provided by State University of New York at Buffalo:
sleep apnea, titration, CPAP, 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 July 20, 2014