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

Sponsor
State University of New York at Buffalo (Other)
Overall Status
Withdrawn
CT.gov ID
NCT00497640
Collaborator
(none)
0
Enrollment
1
Location
25
Anticipated Duration (Months)
0
Patients Per Site Per Month

Study Details

Study Description

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 Phase
  • Procedure: Artificial Neural Network
 N/A

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 Design

Study Type:
Interventional
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
Actual Study Start Date :
May 1, 2007
Anticipated Primary Completion Date :
Jul 1, 2008
Anticipated Study Completion Date :
Jun 1, 2009

Outcome Measures

Primary Outcome Measures

  1. Time to achieve optimal CPAP [minutes]

Secondary Outcome Measures

  1. Failure Rate of CPAP titration [percentage]

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years to 80 Years
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
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

Locations

Site City State Country Postal Code
1 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

Study Documents (Full-Text)

None provided.

More Information

Publications

Responsible Party:
Ali El Solh, Principal Investigator, State University of New York at Buffalo
ClinicalTrials.gov Identifier:
NCT00497640
Other Study ID Numbers:
  • MED4890507E
First Posted:
Jul 6, 2007
Last Update Posted:
Nov 25, 2020
Last Verified:
Sep 1, 2009
Keywords provided by Ali El Solh, Principal Investigator, State University of New York at Buffalo
sleep apnea, titration, CPAP, neural network
Additional relevant MeSH terms:
Sleep Apnea Syndromes
Sleep Apnea, Obstructive

Study Results

No Results Posted as of Nov 25, 2020