Obstructive Sleep ApneaNCT00497640
CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study
A study testing Artificial Neural Network in people with Obstructive Sleep Apnea, run by State University of New York at Buffalo at 1 site in United States.
Trial 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 more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Obstructive Sleep Apnea
- What is being tested?
- Artificial Neural Network
- Type of treatment
- Surgery or procedure
- How many people
- 0 participants
- Where
- 1 siteUnited States
- Study dates
- May 2007to Jun 2009
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- patients 18 years of age and older,
- documented OSA by sleep study defined as AHI > 5/hr
Who cannot join
- previously treated OSA,
- unwilling to undergo a titration study,
- unable or unwilling to sign an informed consent.
Trial locations and contacts
1 site in 1 country.
United States1 site
State University of New York at Buffalo
Buffalo, New York, 14215
About this 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
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
- Conditions
- Obstructive Sleep Apnea
- Being tested
- Artificial Neural Network
- Keywords
- sleep apnea, titration, CPAP, neural network
- Medical subject headings
- Sleep Apnea, Obstructive, Sleep Apnea Syndromes, Microcephaly, Primary Autosomal Recessive, 6
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Neural NetworkProcedure or surgery
Use of a predicted optimal CPAP
What is being measured
The primary outcome is the main question the study is designed to answer.
Time to achieve optimal CPAP
Measured: minutes
Secondary outcomes (1)
Failure Rate of CPAP titration
Measured: percentage
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jul 2007 (estimated)
- Study startMay 2007
- Primary completionJul 2008 (estimated)
- Study completionJun 2009 (estimated)
Why it stopped: Study was terminated due to lack of interest from subjects and no funding, only 1 subject signed consent but did not participate.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Obstructive Sleep ApneaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- State University of New York at Buffalo
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ali El Solh, State University of New York at Buffalo
- Investigators
- Ali A El Solh, MD, MPH, Principal investigator, Sate University of New York at Buffalo
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT00497640
- Sponsor's protocol ID
- MED4890507E
- Status last verified
- Sep 2009
- First submitted
- 5 Jul 2007
- Last updated
- 25 Nov 2020
- Results posted
- No
- Data monitoring committee
- Yes
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
Study at a glance
From the registry record, last updated by the sponsor on 25 Nov 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study
- ClinicalTrials.gov ID
- NCT00497640
- Acronym / sponsor ID
- MED4890507E
- Conditions
- Obstructive Sleep Apnea
- Keywords
- sleep apnea, titration, CPAP, neural network
- Study type
- Interventional
- Recruitment status
- WithdrawnReason stopped: Study was terminated due to lack of interest from subjects and no funding, only 1 subject signed consent but did not participate.
- Enrolment
- 0 participants (actual)
- Lead sponsor
- State University of New York at BuffaloUniversity, hospital or other
- Study start
- May 2007 (actual)
- Primary completion
- Jul 2008 (estimated)
- Study completion
- Jun 2009 (estimated)
- First posted
- 6 Jul 2007 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- patients 18 years of age and older,
- documented OSA by sleep study defined as AHI > 5/hr
Key exclusion criteria (3 listed)
- previously treated OSA,
- unwilling to undergo a titration study,
- unable or unwilling to sign an informed consent.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Neural NetworkUse of a predicted optimal CPAP | Procedure or surgery | – |
Primary outcome
Time to achieve optimal CPAP
Time frame: minutes
1 secondary outcome
Failure Rate of CPAP titration
Time frame: percentage
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Nov 2020.
- Background: PubMed 17512788El 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ali A El Solh, MD, MPH principal investigator
Sate University of New York at Buffalo
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Neural Network in people with Obstructive Sleep Apnea, run by State University of New York at Buffalo at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 25 Nov 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Time to achieve optimal CPAP (time frame: minutes). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT00497640, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Nov 2020; AzTrials last refreshed its copy on 28 Oct 2026.