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WithdrawnNCT00497640Updated Nov 25, 2020

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

An interventional study of Artificial Neural Network in Obstructive Sleep Apnea, sponsored by State University of New York at Buffalo. Withdrawn at 1 site in United States. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2020-11-25.

Sponsored by State University of New York at Buffalo · Not applicable, Interventional, and Diagnostic

Why this study was withdrawn
Study was terminated due to lack of interest from subjects and no funding, only 1 subject signed consent but did not participate.
Phase
Not applicable
Study type
Interventional
Enrollment
0
Allocation
Randomized
Ages
18 Years to 80 Years
Sex
All
01

Study 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

Read the 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

02

Conditions studied

  • Obstructive Sleep Apnea

Keywords

  • sleep apnea, titration, CPAP, neural network
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In context

Sleep Apnea Syndromes

2,162 studies on the registry are indexed under Sleep Apnea Syndromes; 290 are open to participants now.

Browse Sleep Apnea Syndromes studies →

Lead sponsor

State University of New York at Buffalo is the lead sponsor of 287 studies on the registry; 65 are open to participants now.

Of its 19 completed or terminated interventional studies of FDA-regulated products, 15 (79%) have results posted.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 80 Years
Sexes eligible
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

Exclusion Criteria:

  1. previously treated OSA,
  2. unwilling to undergo a titration study,
  3. unable or unwilling to sign an informed consent.
05

Study design

Phase
Not applicable
Primary purpose
Diagnostic
Allocation
Randomized
Intervention model
Parallel assignment
Masking
None (open label)
Enrollment
0 participants (actual)

Interventions

  • ProcedureArtificial Neural Network

    Use of a predicted optimal CPAP

06

What researchers measure

Primary outcomes

  1. Time to achieve optimal CPAP

    Time frame: minutes

Secondary outcomes

  1. Failure Rate of CPAP titration

    Time frame: percentage

07

Study locations

1 site
  • State University of New York at Buffalo
    Buffalo, New York 14215, United States
08

References and documents

Publications

  • 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):471-7. doi: 10.1016/j.sleep.2006.09.005. Epub 2007 May 18. PubMed 17512788 ↗
09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Nov 25, 2020, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT00497640
Lead sponsor
State University of New York at Buffalo
Responsible party
Ali El Solh (Principal Investigator, State University of New York at Buffalo) — Principal investigator
First posted
Jul 6, 2007
Start date
May 2007
Primary completion
Jul 2008 (estimated)
Completion
Jun 2009 (estimated)
Last update
Nov 25, 2020

Study contacts

Ali A El Solh, MD, MPH
principal investigator · Sate University of New York at Buffalo

Oversight

Data monitoring committee
Yes
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is withdrawn, as verified in Sep 2009. You cannot join it, but the record below documents what was studied.

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