Evaluation of an Algorithm to Detect Sleep and Wake in Continuous Positive Airway Pressure (CPAP)

Sleep Apnea, Obstructive Clinical Trial

— PBSW  

Official title:

Evaluation of an Algorithm to Detect Sleep and Wake in Continuous Positive Airway Pressure Users Using Paced Breathing

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT01031914
• Other study ID #: AR-0931-PBSW-SS
• Status: Completed
• Phase: N/A
• First received: December 11, 2009
• Last updated: April 1, 2013
• Start date: October 2009
• Est. completion date: September 2010

Study information

• Verified date: April 2013
• Source: Philips Respironics
• Contact: n/a
• Is FDA regulated: No
• Health authority: United States: Institutional Review Board
• Study type: Interventional

Clinical Trial Summary

The purpose of this engineering trial is to develop and validate an algorithm that will deliver Paced Breathing as a ramp feature to obstructive sleep apnea (OSA) subjects using Continuous Positive Airway Pressure (CPAP) therapy. In this trial the investigators will be evaluating the algorithm's ability to correctly distinguish between sleep and wake.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 36
• Est. completion date: September 2010
• Est. primary completion date: September 2010
• Accepts healthy volunteers: No
• Gender: Both
• Age group: 21 Years to 70 Years

Eligibility

Inclusion Criteria:

• Age 21-70

• Diagnosis of obstructive sleep apnea (OSA)

• Current adherent CPAP user (has been using CPAP nightly for at least 2 weeks).

• On CPAP pressures of 5-10cm.

• Subjects wishing to complete a day or evening appointment need to have significant daytime sleepiness (Epworth Sleepiness Scale score of 8 or above)

• Able and willing to provide written informed consent

• English speaking

Exclusion Criteria:

• Participation in another interventional research study within the last 30 days

• Major controlled or uncontrolled medical condition such as congestive heart failure, neuromuscular disease, renal failure etc.

• Inability to tolerate nasal CPAP mask due to problems breathing solely through their nose.

• Chronic respiratory failure or insufficiency with suspected or known neuromuscular disease, moderate or severe chronic obstructive pulmonary disease (COPD), or any condition with an elevation of arterial carbon dioxide levels while awake (PaCo2=55mmHg)

• Severe oxygen desaturation on the polysomnography (PSG), i.e. Sa02 < 70% for 10% of the study.

• Surgery of the upper airway, nose, sinus or middle ear within the past 90 days

• Currently using supplemental oxygen

• Regular use of sleeping pills or stimulants (> 3 nights a week)

• Currently working night shift or rotating day/night shift

• Drowsy Driving or near miss accident in the past 6 months

• Inability to tolerate or track to Paced Breathing device during initial habituation session in lab

• Chronic insomnia, Restless legs syndrome, or severe periodic limb movement disorder (PLMD - PLMAI>20/hr).

Study Design

Intervention Model: Single Group Assignment, Masking: Open Label

Related Conditions & MeSH terms

• Sleep Apnea, Obstructive

Intervention

Device:
• Paced Breathing
The Paced Breathing (PB) feature(when activated) will work to relax the user and help them fall asleep by encouraging them to take deep slow breaths until they reach 10 breaths (or less) per minute. The feature will also detect when the subject has fallen asleep so the Continuous Positive Airway Pressur (CPAP) device will automatically switch from PB mode to regular CPAP mode.

Locations

Country	Name	City	State
United States	Philips Respironics	Boston	Massachusetts

Sponsors (1)

Lead Sponsor	Collaborator
Philips Respironics

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Sleep/Wake Algorithm	We tested the ability of the Sleep/Wake algorithm to identify sleep an wake periods with precision, as compared to standard polysonography (PSG) measures, which was used as the gold standard, i.e. we tested the accuracy of the algorithm. Accuracy was defined as the proportion of true results (both true positives and true negatives)in the population and it was assesed using as 2 X 2 table, i.e. accuracy = number of true positives + number of true negatives/ number of true positives + false positives + false negatives +true negatives. where True positive = the algorithm tested correctly identified sleep, False positive = the algorithm tested incorrectly identified sleep, True negative = the algorithm tested correctly rejected awake periods, and False negative = the algorithm tested incorrectly rejected awake periods.	The performance of the algorithm will be evaluated in real time while the subject is wearing the device during the sleep study, an average of 08 hours.	No