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

NCT ID: NCT00497640

Last Updated: 2020-11-25

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

WITHDRAWN

Clinical Phase

NA

Study Classification

INTERVENTIONAL

Study Start Date

2007-05-31

Study Completion Date

2009-06-30

Brief Summary

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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

Detailed Description

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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

Conditions

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Obstructive Sleep Apnea

Keywords

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sleep apnea, titration, CPAP, neural network

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Interventions

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Artificial Neural Network

Use of a predicted optimal CPAP

Intervention Type PROCEDURE

Eligibility Criteria

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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.
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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State University of New York at Buffalo

OTHER

Sponsor Role lead

Responsible Party

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Ali El Solh

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Ali A El Solh, MD, MPH

Role: PRINCIPAL_INVESTIGATOR

Sate University of New York at Buffalo

Locations

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State University of New York at Buffalo

Buffalo, New York, United States

Site Status

Countries

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United States

References

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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.

Reference Type BACKGROUND
PMID: 17512788 (View on PubMed)

Other Identifiers

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MED4890507E

Identifier Type: -

Identifier Source: org_study_id