SLEEP 2011 Abstract Supplement

B. Clinical Sleep Science I. Sleep Disorders – Breathing
0329
DETECTION OF HYPOVENTILATION BY CONTINUOUS
MONITORING OF MAIN STREAM END-TIDAL CO2 IN
OBESE PATIENTS WITH SUSPECTED SLEEP APNEA
Capozzolo B 1 , Baltzan M 1,2,3 , Verschelden P 1,4,5,6
1
OSR Medical Sleep Disorders Centre, Town of Mount-Royal, QC,
Canada, 2 Mount Sinai Hospital, Montreal, QC, Canada, 3 McGill
University, Montreal, QC, Canada, 4 Institut de Médecine Spécialisé
de Laval, Laval, QC, Canada, 5 Cité de la Santé de Laval, Laval, QC,
Canada, 6 Université de Montréal, Montreal, QC, Canada
Introduction: Hypoventilation is considered both a serious co-morbidity
and consequence of sleep apnea, which increases with prevalence in
obese patients. It is substantially under-diagnosed prior to the advent of
clinical respiratory failure. We evaluated the incidence of a new diagnosis
of hypoventilation using the systematic monitoring of main stream
end-tidal CO2 (ETCO2) the extent of under-diagnosis.
Methods: Three hundred eighty-four community-based obese adults
with a body mass index (BMI) of 30 kg/m2 or more were referred for
clinically suspected sleep apnea. All patients were monitored with nocturnal
polysomnography over a 17-month period. Participants were
evaluated by a sleep medicine specialist, questionnaire and one night of
polysomnography. Main stream ETCO2 was monitored on all patients,
including during the diagnostic portion of split-night polysomnography.
Results: Patients were a mean (SD) age of 50.5 (SD 11.9) years and
71.4% were men. The mean apnea-hypopnea index was 53.0(SD 37.0)
and 85.4% of patients met diagnostic criteria for at least moderate obstructive
sleep apnea. Of the patients with obesity, 233 (60.7% of 384)
had a BMI of 30-35.9 kg/m2, 83 (21.6% of 384) had a BMI of 36-40.9
kg/m2 and 68 (17.7% of 384) had a BMI of 41 kg/m2 or more. Overall,
23 (6.0% of 384) had at least 10% of total sleep time with an ETCO2 of
50mmHg or higher. With increasing obesity, the number with this degree
of hypoventilation was 6 (2.6% of BMI 30-35.9), 5 (6.0% of BMI 36-
40.9), and 12 (17.6 % of BMI 41 or more). None of these patients were
known for hypoventilation prior to polysomnography.
Conclusion: We conclude that hypoventilation is frequent in patients
with obesity, and that continuous monitoring of main stream ETCO2
facilitates diagnoses that were previously undiagnosed.
0330
A PILOT EVALUATION OF A NOVEL SCREENING TOOL
FOR SLEEP RELATED BREATHING DISORDERS
suraiya S 1 , Pillar G 1 , Colman J 2 , Shalev I 2 , Ronen M 2 , Weissbrod R 2 ,
Lain D 2
1
Sleep laboratory, Technion Instiute of technology, haifa, Israel, 2 R&D,
Oredion Medical 1987 LTD, Jerusalem, Israel
Introduction: Polysomnography (PSG) is the standard procedure for
the diagnosis of sleep related breathing disorders (SRDB) and patients
are typically referred for overnight studies when they are identified as
being at risk by a clinician. Various tools are in use today to identify
patients at risk for SRDB and refer them subsequently for studies. The
ASA (American Society of Anesthesiologists) and other professional
bodies have published guidelines calling for the recognition of patients
suffering from SRDB during perioperative care. The Capnostream20p
capnograph/pulse oximeter with SSDx algorithm is used in many hospital
type environments whenever patient monitoring is required. The
monitor provides an Apnea Index (AI), based on summation of the nobreath
events per hour recognized by the capnograph, and an Oxygen
Desaturation Index (ODI), using pulse oximetry. The information is presented
in a simple summary report. The purpose of our evaluation was
to assess the level of agreement between the indices generated by the
device and overnight polysomnograph studies.
Methods: During routine overnight sleep studies 39 adult patients were
monitored with the device. The sleep study was interpreted by a trained
clinician who was blinded to the device. The AI and ODI values generated
by the device were compared to the sleep study outcomes.
Results: A statistically significant model using the maximal AI and
ODI values to predict OSA was defined. At a cut-off point of 19 - (ODI
max+AI max) >19., sensitivity equals 0.87 and specificity 0.82. The
PPV with actual prevalence of 0.68 (per the clinical data gathered)
equals 0.91 and NPV = 0.75.
Conclusion: The results indicated that the device showed high sensitivity
and specificity, and hence can be used as a tool for screening and
assisting in the diagnosis of adult patients with medium and severe Obstructive
Sleep Apnea in the hospital environment.
Support (If Any): Oredion Medical 1978 LTD
0331
OBSTRUCTIVE SLEEP APNEA PREDICTION BY BREATH
SOUND ANALYSIS DURING WAKEFULNESS
Moussavi Z, Montazeri A
Electrical & computer Engineering, University of Manitoba, Winnipeg,
MB, Canada
Introduction: Obstructive sleep apnea (OSA) is highly prevalent in the
general population. While prompt initiation of treatment is particularly
important for patients with severe OSA, only about 30% of the patients
referred to a sleep lab are found to be in that category. Although there
are clinical algorithms that can predict the likelihood of a patient having
OSA, there are no easy clinical or laboratory predictors for the severity
of OSA other than a sleep study. The goal of this study was to investigate
the feasibility of developing a fast, simple and accurate screening tool
for stratification of severity of OSA during wakefulness.
Methods: We studied 35 OSA patients with various degrees of severity
and 17 age-matched control subjects, who were also tested by full-night
polysomnography (PSG) for validation. The subjects were instructed to
breathe, once through their nose and once through their mouth with a
nose clip in place, at their normal breathing level for at least 5 breaths,
and then breathe at the their maximum flow level for another 5 breaths.
The breath sound signals were picked up by a Sony (ECM-77B) microphone
placed over the neck and digitized at 10240 Hz rate. The recordings
were repeated in two body positions: sitting upright, and supine.
Data were analyzed using spectral and waveform fractal dimension
techniques followed by statistical analysis to extract the most significant
features for stratification of OSA severity.
Results: Several sound features were found to be statistically significant
between the OSA and non-OSA groups. Using Maximum Relevancy
Minimum Redundancy method, we reduced the number of features
to two. Unsupervised clustering of the two most significant features
showed an overall accuracy of over 84% (sensitivity=88%, specificity=80%)
for separating healthy from OSA patients as well as stratification
of the OSA severity among the patients group.
Conclusion: The results hold promise in sound analysis of different
breathing maneuvers for OSA screening during wakefulness. It is known
that OSA patients on average have smaller and more collapsible pharynx.
This is compensated by the increased dilator muscle activity during
wakefulness. They tend to have more negative pharyngeal pressure,
which is detectable by breathing sound through the nose due to higher
resistance. Given that breath sounds are directly related to the pharyngeal
pressure, our proposed method is sensitive to the severity of OSA
even during wakefulness.
Support (If Any): This project was supported financially by the TR-
Labs Winnipeg.
SLEEP, Volume 34, Abstract Supplement, 2011
A116