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

July 5, 2007
September 18, 2009
May 2007
July 2008   (final data collection date for primary outcome measure)
Time to achieve optimal CPAP [ Time Frame: minutes ] [ Designated as safety issue: No ]
Time to achieve optimal CPAP
Complete list of historical versions of study NCT00497640 on ClinicalTrials.gov Archive Site
Failure Rate of CPAP titration [ Time Frame: percentage ] [ Designated as safety issue: No ]
Failure Rate of CPAP titration
Not Provided
Not Provided
 
CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study
CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study

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

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

Interventional
Not Provided
Allocation: Randomized
Endpoint Classification: Bio-equivalence Study
Intervention Model: Parallel Assignment
Masking: Open Label
Primary Purpose: Diagnostic
Obstructive Sleep Apnea
Procedure: Artificial Neural Network
Use of a predicted optimal CPAP
Not Provided
El Solh AA, Aldik Z, Alnabhan M, Grant B. Predicting effective continuous positive airway pressure in sleep apnea using an artificial neural network. Sleep Med. 2007 Aug;8(5):471-477. Epub 2007 May 18.

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Completed
120
June 2009
July 2008   (final data collection date for primary outcome measure)

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.
Both
18 Years to 80 Years
No
Contact information is only displayed when the study is recruiting subjects
United States
 
NCT00497640
MED4890507E
Yes
Ali El Solh, State University of New York at Buffalo
State University of New York at Buffalo
Not Provided
Principal Investigator: Ali A El Solh, MD, MPH Sate University of New York at Buffalo
State University of New York at Buffalo
September 2009

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