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The study is to compare the performance of a CPAP (continuous positive airway pressure) device to a clinical polysomnography (PSG) in identifying breathing events in patients with obstructive sleep apnea.
Purpose: The purpose of this study was to compare the AED algorithm used in a PAP device with manually scored events on PSG. The PAP device was modified to produce a square wave voltage output identifying when apneas, hypopneas, and snoring events were detected. Recording this event signal on the PSG performed with the patient using the PAP device allowed an event-by-event comparison between manually scored PSG events and AED events. In addition, the AHI, AI, and HI derived from the manually scored PSG were compared with the respective measures reported by the PAP device used during the PSG.
Study Objectives: Compare automatic event detection (AED) of respiratory events using a positive airway pressure (PAP) device with manual scoring of polysomnography (PSG) during PAP treatment of obstructive sleep apnea (OSA).
Design: Prospective PSGs of patients using a PAP device.
Setting: Six academic and private sleep disorders centers.
Interventions: A signal generated by the PAP device identifying the AED of respiratory events based on airflow was recorded during PSG.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| CPAP Device | Experimental | Breathing event detection (AED) by the CPAP device will be compared to breathing event detection by a simultaneous PSG (manual PSG scoring). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Analysis with AED and manual PSG scoring | Device | The CPAP device will be set-up at a sub-therapeutic pressure and will remain at this pressure for the entire night, if tolerated. Then the events will be analyzed with Automatic Event Detection (AED) and manual PSG scoring. |
| Measure | Description | Time Frame |
|---|---|---|
| Apnea-hypopnea Indices (AHI) as Determined by Polysomnography (PSG) vs Automatic Event Detection (AED ) Algorithm | Apnea-hypopnea index (AHI) is the combined average number of apneas and hypopneas that occur per hour of sleep. The Apnea index (AI) is the average number of apneas that occur per hour of sleep. The Hypopnea index (HI) is the average number of hypopneas that occur per hour of sleep. The PSGs were manually scored to determine the apnea-hypopnea index. This value was then compared to the PAP device which utilized the AED algorithm to determine the apnea-hypopnea index. | one night |
| Measure | Description | Time Frame |
|---|---|---|
| Methodological Comparisons of AHI, Apnea Index (AI) and Hypopnea Index (HI) as Determined by Intra-class Correlation (ICC) | Methodological comparisons utilizing ICC for detection of AHI, apnea index (AI) and hypopnea index (HI) were caculated between the values obtained by PSG and the REMstar Auto with A-Flex device. | one night |
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Inclusion Criteria:
Exclusion Criteria:
Participation in another interventional research study within the last 30 days
Major medical or psychiatric condition that would interfere with the demands of the study and adherence to PAP. Examples include unstable cardiovascular disease (Class III / IV CHF), neuromuscular disease, cancer, and renal failure.
Chronic respiratory failure or insufficiency with suspected or known neuromuscular disease, moderate or severe continuous positive airway pressure (COPD) or other pulmonary disorders, or any condition with an elevation of arterial carbon dioxide levels (> 45 mmHg) while awake, or subjects requiring continuous oxygen therapy.
Surgery of the upper airway, nose, sinus, or middle ear within the previous 90 days
Surgery at any time for the treatment of OSAHS such as uvulopalatopharyngoplasty (UPPP)
Presence of untreated or poorly managed,non-OSAHS related sleep disorders:
Consumption of ethanol immediately prior to the research PSG
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| Name | Affiliation | Role |
|---|---|---|
| Richard Berry, MD | University of Florida | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Shands and UF Sleep Disorder Center | Gainesville | Florida | 32606 | United States |
119 studies had a technically adequate recording and were collected from 90 patients (29 patients participated in two studies).
These 119 studies were pooled with another 29 studies from another trial for a total of 148 studies. 4 patients participated in both trials and 3 of these patients contributed two recordings.
A total of 148 (PSGs and overnights with PAP therapy), collected from 115 unique participants, were included in this analysis.
| ID | Title | Description |
|---|---|---|
| FG000 | All Participants | Data was collected from participants that participated in a multi-center, randomized, double-blind trial comparing three modes of positive pressure delivery who had overnight PSGs. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
*In summarizing the age, BMI, and sex statistics, the 4 participants who completed both trials were only counted once.
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| ID | Title | Description |
|---|---|---|
| BG000 | CPAP Device | Breathing event detection (AED) by the continuous positive airway pressure (CPAP) device will be compared to breathing event detection by a simultaneous PSG (manual PSG scoring). Analysis with AED and manual PSG scoring: The CPAP device will be set-up at a sub-therapeutic pressure and will remain at this pressure for the entire night, if tolerated. Then the events will be analyzed with Automatic Event Detection (AED) and manual PSG scoring. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Apnea-hypopnea Indices (AHI) as Determined by Polysomnography (PSG) vs Automatic Event Detection (AED ) Algorithm | Apnea-hypopnea index (AHI) is the combined average number of apneas and hypopneas that occur per hour of sleep. The Apnea index (AI) is the average number of apneas that occur per hour of sleep. The Hypopnea index (HI) is the average number of hypopneas that occur per hour of sleep. The PSGs were manually scored to determine the apnea-hypopnea index. This value was then compared to the PAP device which utilized the AED algorithm to determine the apnea-hypopnea index. | Posted | Mean | Standard Deviation | events per hour | one night | Overnight PSG and AED algorithms | Overnight PSG and AED algorithms |
|
one overnight visit
As this was a review of overnight data from another study, there were no adverse events or serious adverse events, as only data was reviewed.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | CPAP Device | Breathing event detection (AED) by the CPAP device will be compared to breathing event detection by a simultaneous PSG (manual PSG scoring). Analysis with AED and manual PSG scoring: The CPAP device will be set-up at a sub-therapeutic pressure and will remain at this pressure for the entire night, if tolerated. Then the events will be analyzed with Automatic Event Detection (AED) and manual PSG scoring. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Richard B. Berry, MD | University of Florida | 352-262-1575 | sleep_doc@msn.com |
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| ID | Term |
|---|---|
| D012891 | Sleep Apnea Syndromes |
| ID | Term |
|---|---|
| D001049 | Apnea |
| D012120 | Respiration Disorders |
| D012140 | Respiratory Tract Diseases |
| D020919 | Sleep Disorders, Intrinsic |
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Participants wore a PAP device and had a PSG at the same time. These participants had their PSG data compared to the PAP data.
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| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
| BMI | Mean | Standard Deviation | kg/m^2 |
|
| OG001 | Automatic Event Detection | The AED algorithm used the following criteria to identify respiratory events. An apnea was detected when there was an 80% or greater reduction in the airflow for 10 sec or longer in comparison with the average airflow over the previous 2 minutes. A hypopnea was detected when there was a device estimated 40% reduction in airflow for ≥ 10 sec but < 60 sec compared with the average airflow over the previous 2 minutes. The hypopnea detection algorithm required the presence of two recovery breaths that nominally were at least 75% to 80% of the baseline airflow. The algorithm also looked for evidence of flow limitation to detect hypopneas. The algorithm monitored the flow signal for changes in peak flow, and the shape of the inspiratory airflow signal that would be associated with flow limited breathing. |
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| Secondary | Methodological Comparisons of AHI, Apnea Index (AI) and Hypopnea Index (HI) as Determined by Intra-class Correlation (ICC) | Methodological comparisons utilizing ICC for detection of AHI, apnea index (AI) and hypopnea index (HI) were caculated between the values obtained by PSG and the REMstar Auto with A-Flex device. | Posted | Number | coefficient | one night | Overnight PSG and AED algorithms | Overnight PSG and AED algorithms |
|
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| 0 |
| 115 |
| 0 |
| 115 |
| 0 |
| 115 |
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| D020920 |
| Dyssomnias |
| D012893 | Sleep Wake Disorders |
| D009422 | Nervous System Diseases |
| Title | Measurements |
|---|---|
|