Identification Of Obstructive Sleep Apnea Research Articles

Identification of obstructive sleep apnea in children with obesity: A cluster analysis approach.

Obstructive sleep apnea (OSA) is a heterogeneous disorder with a prevalence of 25%-60% in children with obesity. There is a lack of diagnostic tools to identify those at high risk for OSA. Children with obesity, aged 8-19 years old, were enrolled into an ongoing multicenter, prospective cohort study related to OSA. We performed k-means cluster analysis to identify clinical variables which could help identify obesity related OSA. In this study, 118 participants were included in the analysis; 40.7% were diagnosed with OSA, 46.6% were female and the mean (SD) body mass index (BMI) and age were 39.7 (9.6) Kg/m², and 14.4 (2.6) years, respectively. The mean (SD) obstructive apnea-hypopnea index (OAHI) was 11.0 (21.1) events/h. We identified two distinct clusters based on three clustering variables (age, BMI z-score, and neck-height ratio [NHR]). The prevalence of OSA in clusters 1 and 2, were 22.4% and 58.3% (p = 0.001), respectively. Children in cluster 2, in comparison to cluster 1, had higher BMI z-score (4.7 (1.1) versus 3.2 (0.7), p < 0.001), higher NHR (0.3 (0.02) versus 0.2 (0.01), p < 0.001) and were older (15.0 (2.2) versus 13.7 (2.9) years, p = 0.09), respectively. However, there were no significant differences in sex and OSA symptoms between the clusters. The results from hierarchical clustering were similar to k-means analysis suggesting that the resulting OSA clusters were stable to different analysis approaches. BMI, NHR, and age are easily obtained in a clinical setting and can be utilized to identify children at high risk for OSA.

Validation of STOP, STOP-BANG, STOP-BAG, STOP-B28, and GOAL screening tools for identification of obstructive sleep apnea in patients with Parkinson disease.

Obstructive sleep apnea (OSA) is common in Parkinson disease (PD). Questionnaires can be used as screening tools and have been used as a surrogate definition of OSA in large-scale research. This study aimed to validate the performance of STOP, STOP-BANG, STOP-BAG, STOP-B28, and GOAL and OSA predictors as tools to identify OSA in PD. Data were analyzed from a PD cohort study in which OSA was diagnosed using laboratory polysomnography. We calculated sensitivity and specificity of each questionnaire for OSA using different definitions and performed receiver operating characteristics curve analysis. Linear regression was used to assess adjusted associations between questionnaires and outcomes: Montreal Cognitive Assessment, Epworth Sleepiness Scale, and Movement Disorder Society revision of the Unified Parkinson Disease Rating Scale. Questionnaire data were available for 68 PD patients (61.8% male, mean age 64.5 [standard deviation 9.9] years, and Hoehn and Yahr score 2.1 [0.8]). OSA (apnea-hypopnea index ≥ 15 events/h) occurred in 69.4% of participants. STOP-B28 ≥ 2 presented a higher sensitivity for OSA than STOP ≥ 2 (0.76 vs 0.65, respectively) and slightly lower specificity (0.65 vs 0.70, respectively). GOAL ≥ 2 had the highest sensitivity but poor specificity. Loud snoring had sensitivity 0.63 and specificity 0.65. STOP and snoring were significantly associated with Montreal Cognitive Assessment, Epworth Sleepiness Scale, and Movement Disorder Society revision of the Unified Parkinson Disease Rating Scale (total, motor, and nonmotor); STOP-BANG, STOP-BAG, and STOP-B28 showed associations with most outcomes, but the GOAL showed none. The STOP-B28 followed by STOP and presence of loud snoring alone seem to have the best overall properties to identify PD patients with OSA, whose clinical characteristics differ from the general population with OSA. Gomes T, Benedetti A, Lafontaine A-L, Kimoff RJ Robinson A, Kaminska M. Validation of STOP, STOP-BANG, STOP-BAG, STOP-B28, and GOAL screening tools for identification of obstructive sleep apnea in patients with Parkinson disease. J Clin Sleep Med. 2023;19(1):45-54.

Score that Snore: Proactive Nursing Identification of Obstructive Sleep Apnea in Surgical Patients

<h3>Introduction</h3> Surgical patients with undiagnosed Obstructive Sleep Apnea (OSA) are at an increased risk for post-operative complications of respiratory distress and over sedation. Early identification of undiagnosed OSA can mitigate future risks of post-operative adverse events. <h3>Identification of the problem</h3> In2020, 22% of RRT events occurring within 24 hours of transfer from PACU to inpatient units were related to over sedation and respiratory issues. Retrospective assessment of these patients indicate 50% would have a STOP BANG score of ≥ 3 (moderate to severe OSA risk) while 88% of these patients had no prior OSA diagnosis. Current practice excludes preoperative screening for OSA risks among surgical patients. <h3>EBP Question/Purpose</h3> <b>PICOquestion -</b> In adult surgical patients does a preoperative STOP BANG assessment to identify undiagnosed OSA compared to no assessment reduce post-operative rapid response (RRT) events of respiratory distress and over sedation? <b>Databases utilized</b> - PubMed, CINHAL <h3>Methods/Evidence</h3> Preoperative nurses assess patient risk for OSA using the STOP BANG questionnaire. Moderate to high-risk patients (STOP BANG score ≥ 3) are identified. Nurses notify providers prior to surgery. Post-operatively, PACU nurses implement interventions such as oxygenation, patient positioning, multimodal pain relief, and, if needed, extended monitoring in these moderate to high risk OSA patients. <h3>Significance of Findings/Outcomes</h3> Resultsare pending. Anticipate reduction in RRT events of respiratory distress and over sedation in postoperative patients transferred within 24 hours from PACU to inpatient units. Introduces new perioperative practice of proactive identification of pre-surgical patients with moderate to severe OSA risk which allows for intra-and post-operative interventions that prevent adverse events. Standardizes best practices in perioperative services across multi-hospital healthcare system. <h3>Implications for perianesthesia nurses and future research</h3> Proactive early identification of high risk undiagnosed OSA patients by preoperative nurses can ensure coordinated medical and nursing interventions at all stages of the perioperative process.

Neutrophil-to-Lymphocyte Ratio Facilitates Identification of Obstructive Sleep Apnea in Patients with Type B Aortic Dissection.

Purpose To determine whether the neutrophil-to-lymphocyte ratio (NLR) aids in the detection of obstructive sleep apnea (OSA) in patients with type B aortic dissection (TBAD). Methods 324 patients with TBAD or type B aortic intramural hematoma (TB-AIMH) underwent an overnight sleep study. We divided the eligible 256 studied subjects into three groups: group A (n = 109, TBAD patients with OSA), group B (n = 68, TB-AIMH patients with OSA), and group C (n = 79, TBAD patients without OSA). Baseline characteristics, biochemical and sleep parameters, and STOP-Bang questionnaire scores were collected. To assess the predictive efficacy of potential variables, multivariate logistic regression analysis and receiver operating characteristic (ROC) curves were used. Results The study found that about 58% of TBAD patients and 54% of TB-AIMH patients had OSA, a majority of whom had moderate to severe OSA (95.41% and 89.71%, respectively). In the comparison of sleep parameters between patients with TBAD and TB-AIMH, no other than apnea and hypopnea index (AHI) made a significant difference. The multivariate logistic regression analysis showed that neutrophil-to-lymphocyte ratio (NLR) (odds ratio (OR): 3.614, 95% confidence interval (CI): 2.273–5.748, and P < 0.05) and STOP-Bang scores (OR: 1.97, 95% CI: 1.34–2.90, and P < 0.05) were both independent predictors for OSA in patients with TBAD. ROC curves showed NLR had higher sensitivity (65% versus 59%) and specificity (86% versus 57%) for OSA than the STOP-Bang questionnaire. Furthermore, NLR was positively correlated with AHI through the Spearman test (r = 0.398 and P < 0.05). Conclusion NLR was an independent predictor of OSA in TBAD patients with higher sensitivity and specificity than the STOP-Bang questionnaire, and it was positively associated with AHI. NLR may aid in the diagnosis and risk stratification of OSA in TBAD patients.

In-Home Diagnosis of Obstructive Sleep Apnea Using Automatic Video Analysis

Study objectivesTo evaluate the diagnostic accuracy of a non-invasive technology based on image processing for the identification of obstructive sleep apnea (OSA) and its severity at patients’ home. MethodsObservational, prospective, diagnostic accuracy study to evaluate the degree of measure agreement between Sleepwise (SW), in-laboratory attended polysomnography (PSG) and a home sleep apnea test (HSAT). 38 consecutive subjects with suspected OSA referred as outpatients to the sleep unit were recruited from September 2016 to September 2017. All patients underwent in-laboratory attended PSG and image processing with SW simultaneously overnight. Subsequently, a HSAT and image processing with SW were performed simultaneously overnight at patients’ home, and the 2 nights after, patients underwent only image processing with SW consecutively. ResultsIn-laboratory polysomnography and SW had a Lin's concordance correlation coefficient of 0.933 and a κ of 0.930. Between HSAT and SW the Lin's concordance correlation coefficient was 0.842 and a κ of 0.571. Agreement between two consecutive nights with SW recording showed a Lin's concordance correlation coefficient of 0.923 and a κ of 0. 837. ConclusionsSW was highly accurate for non-invasive and automatic diagnosis of OSA in outpatients compared to standard methods for OSA diagnosis either in-laboratory attended PSG or HSAT. SW proved to be a technique with repeatable and concordant results on different nights for the same patient. We conclude SW is a non-invasive, easy-to-use, portable, effective and highly accurate system for the in-home diagnosis of OSA.

In-Home Diagnosis of Obstructive Sleep Apnea Using Automatic Video Analysis

Study objectivesTo evaluate the diagnostic accuracy of a non-invasive technology based on image processing for the identification of obstructive sleep apnea (OSA) and its severity at patients’ home. MethodsObservational, prospective, diagnostic accuracy study to evaluate the degree of measure agreement between Sleepwise (SW), in-laboratory attended polysomnography (PSG) and a home sleep apnea test (HSAT). 38 consecutive subjects with suspected OSA referred as outpatients to the sleep unit were recruited from September 2016 to September 2017. All patients underwent in-laboratory attended PSG and image processing with SW simultaneously overnight. Subsequently, a HSAT and image processing with SW were performed simultaneously overnight at patients’ home, and the 2 nights after, patients underwent only image processing with SW consecutively. ResultsIn-laboratory polysomnography and SW had a Lin's concordance correlation coefficient of 0.933 and a κ of 0.930. Between HSAT and SW the Lin's concordance correlation coefficient was 0.842 and a κ of 0.571. Agreement between two consecutive nights with SW recording showed a Lin's concordance correlation coefficient of 0.923 and a κ of 0. 837. ConclusionsSW was highly accurate for non-invasive and automatic diagnosis of OSA in outpatients compared to standard methods for OSA diagnosis either in-laboratory attended PSG or HSAT. SW proved to be a technique with repeatable and concordant results on different nights for the same patient. We conclude SW is a non-invasive, easy-to-use, portable, effective and highly accurate system for the in-home diagnosis of OSA.

Various Methods for Identification of Obstructive Sleep Apnea Using Electrocardiogram Features

Various Methods for Identification of Obstructive Sleep Apnea Using Electrocardiogram Features

Utilizing administrative data for the identification of obstructive sleep apnea in alberta, canada

Utilizing administrative data for the identification of obstructive sleep apnea in alberta, canada

Prevalence and association analysis of obstructive sleep apnea with gender and age differences - Results of SHIP-Trend.

Identification of obstructive sleep apnea and risk factors is important for reduction in symptoms and cardiovascular risk, and for improvement of quality of life. The population-based Study of Health in Pomerania investigated risk factors and clinical diseases in a general population of northeast Germany. Additional polysomnography was applied to measure sleep and respiration with the objective of assessing prevalence and risk factors of obstructive sleep apnea in a German cohort. One-thousand, two-hundred and eight people between 20 and 81 years old (54% men, median age 54 years) underwent overnight polysomnography. The estimated obstructive sleep apnea prevalence was 46% (59% men, 33% women) for an apnea-hypopnea index ≥5%, and 21% (30% men, 13% women) for an apnea-hypopnea index≥ 15. The estimated obstructive sleep apnea syndrome prevalence (apnea-hypopnea index≥5; Epworth Sleepiness Scale >10) was 6%. The prevalence of obstructive sleep apnea continuously increased with age for men and women with, however, later onset for women. Gender, age, body mass index, waist-to-hip ratio, snoring, alcohol consumption (for women only) and self-reported cardiovascular diseases were significantly positively associated with obstructive sleep apnea, whereas daytime sleepiness was not. Diabetes, hypertension and metabolic syndrome were positively associated with severe obstructive sleep apnea. The associations became non-significant after adjustment for body mass. Women exhibited stronger associations than men. The prevalence of obstructive sleep apnea was high, with almost half the population presenting some kind of obstructive sleep apnea. The continuous increase of obstructive sleep apnea with age challenges the current theory that mortality due to obstructive sleep apnea and cardiovascular co-morbidities affect obstructive sleep apnea prevalence at an advanced age. Also, gender differences regarding obstructive sleep apnea and associations are significant for recognizing obstructive sleep apnea mechanisms and therapy responsiveness.

The accuracy of the stop bang questionnaire in the identification of obstructive sleep apnoea (OSA) with polysomnography as the gold standard in adult patients with symptoms of sleep disordered breathing in a tertiary care centre in South India

The accuracy of the stop bang questionnaire in the identification of obstructive sleep apnoea (OSA) with polysomnography as the gold standard in adult patients with symptoms of sleep disordered breathing in a tertiary care centre in South India

An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting

Computerized obstructive sleep apnea detection is necessary to speed-up sleep apnea diagnosis and research and for assisting medical professionals. Moreover, the development of a device to monitor sleep apnea that is low-power and portable, requires a reliable and successful sleep apnea detection scheme. In this article, the problem of automated sleep apnea detection using singe-lead electrocardiogram (ECG) signals has been addressed. At first, segments of ECG signals are decomposed using a data-adaptive signal decomposition scheme, namely- tunable-Q factor wavelet transform (TQWT). Three statistical features are extracted from the TQWT sub-bands and train and test matrices are formed afterwards. These matrices are fed into the classifier to identify non-apneic and apneic ECG signal segments. In this work, a new machine learning algorithm, namely- random under sampling boosting (RUSBoost) is implemented to perform classification. This is for the first time TQWT along with RUSBoost is employed for automatic sleep apnea detection to our knowledge. The overall algorithmic performance of our method is inspected for various values of TQWT parameters. Optimal values of these parameters are investigated and determined. The efficacy and appropriateness of RUSBoost are demonstrated as opposed to the commonly used classification models. The algorithmic performance of our sleep apnea identification scheme is also evaluated against existing detection algorithms in the literature. Experimental outcomes manifest that our sleep apnea identification scheme performs better than the existing works in sensitivity, specificity, and accuracy. It can be anticipated that owing to its use of only one channel of ECG signal, the proposed method will be ideal for device implementation, eliminate the onus of clinicians of analyzing a large bulk of data manually, and expedite sleep apnea diagnosis.

Validity of administrative data for identification of obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a common condition associated with significant morbidity and health-care utilization. We determined the validity of an algorithm derived from administrative data for identifying OSA using the respiratory disturbance index (RDI) as the reference standard. We conducted a retrospective cohort study of adults in Alberta, Canada referred for facility and community-based sleep diagnostic testing between July 2005 and August 2007. Validity indices were estimated for several case definitions of OSA derived from outpatient physician billing claims and hospital discharge codes. For each algorithm, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated against several reference standards for OSA (RDI≥5h-1 , RDI≥15h-1 or RDI≥30h-1 ). For the 2149 patients included in the study, an algorithm requiring one hospital discharge code or two outpatient billing claims identifying OSA in a 2-year period had a sensitivity of 24.1%, specificity of 67.8%, PPV of 74.8% and NPV of 18.3% (reference standard RDI≥5h-1 ). When comorbidities were included in the case definition, the specificity was 90.5% and PPV was 83.3% (reference standard RDI≥5h-1 ). Similar findings were observed using RDI≥15 h-1 and ≥30h-1 as the reference standard. We identify a claims-based algorithm that identifies OSA with a high degree of specificityin patients referred for sleep diagnostic testing. This validatedalgorithm has a good PPV and may be useful when identifying patients with OSA for population studies within a single-payer health-care system.

Automatic Video Analysis for Obstructive Sleep Apnea Diagnosis.

We investigated the diagnostic accuracy for the identification of obstructive sleep apnea (OSA) and its severity of a noninvasive technology based on image processing (SleepWise). This is an observational, prospective study to evaluate the degree of agreement between polysomnography (PSG) and SleepWise. We recruited 56 consecutive subjects with suspected OSA who were referred as outpatients to the Sleep Unit of the Hospital Universitari Germans Trias i Pujol (HUGTiP) from January 2013 to January 2014. All patients underwent laboratory PSG and image processing with SleepWise simultaneously the same night. Both PSG and SleepWise analyses were carried independently and blindly. We analyzed 50 of the 56 patients recruited. OSA was diagnosed through PSG in a total of 44 patients (88%) with a median apnea-hypopnea index (AHI) of 25.35 (24.9). According to SleepWise, 45 patients (90%) met the criteria for a diagnosis of OSA, with a median AHI of 22.8 (22.03). An analysis of the ability of PSG and SleepWise to classify patients by severity on the basis of their AHI shows that the two diagnostic systems distribute the different groups similarly. According to PSG, 23 patients (46%) had a diagnosis of severe OSA, 11 patients (22%) moderate OSA, and 10 patients (20%) mild OSA. According to SleepWise, 20, 13, and 12 patients (40%, 26%, and 24%, respectively) had a diagnosis of severe, moderate, and mild OSA respectively. For OSA diagnosis, SleepWise was found to have sensitivity of 100% and specificity of 83% in relation to PSG. The positive predictive value was 97% and the negative predictive value was 100%. The Bland-Altman plot comparing the mean AHI values obtained through PSG and SleepWise shows very good agreement between the two diagnostic techniques, with a bias of -3.85, a standard error of 12.18, and a confidence interval of -0.39 to -7.31. SleepWise was reasonably accurate for noninvasive and automatic diagnosis of OSA in outpatients. SleepWise determined the severity of OSA with high reliability. The current study including simultaneous laboratory PSG and SleepWise processing image is proposed as a reasonable validation standard.

Quantitative sleep EEG and polysomnographic predictors of driving simulator performance in obstructive sleep apnea

ObjectivesTo improve identification of obstructive sleep apnea (OSA) patients at risk of driving impairment, this study explored predictors of driving performance impairment in untreated OSA patients using clinical PSG metrics, sleepiness questionnaires and quantitative EEG markers from routine sleep studies. MethodsSeventy-six OSA patients completed sleepiness questionnaires and driving simulator tests in the evening of their diagnostic sleep study. All sleep EEGs were subjected to quantitative power spectral analysis. Correlation and multivariate linear regression were used to identify the strongest predictors of driving simulator performance. ResultsAbsolute EEG spectral power across all frequencies (0.5–32Hz) throughout the entire sleep period and separately in REM and NREM sleep, (r range 0.239–0.473, all p<0.05), as well as sleep onset latency (r=0.273, p<0.017) positively correlated with driving simulator steering deviation. Regression models revealed that amongst clinical and qEEG variables, the significant predictors of worse steering deviation were greater total EEG power during NREM and REM sleep, greater beta EEG power in NREM and greater delta EEG power in REM (range of variance explained 5–17%, t range 2.29–4.0, all p<0.05) and sleep onset latency (range of variance explained 4–9%, t range 2.15–2.5, all p<0.05). ConclusionsIn OSA patients, increased EEG power, especially in the faster frequency (beta) range during NREM sleep and slower frequency (delta) range in REM sleep were associated with worse driving performance, while no relationships were observed with clinical metrics e.g. apnea, arousal or oxygen indices. SignificanceQuantitative EEG analysis in OSA may provide useful markers of driving impairment risk. Future studies are necessary to confirm these findings and assess the clinical significance of quantitative EEG as predictors of driving impairment in OSA.

Obstructive Sleep Apnea Identification by Heart Rate Features Derived from Intrinsic Mode Function

Obstructive sleep apnea (OSA) is one of the most important sleep disorders. The gold standard diagnosis of OSA is overnight PSG examination that is time-consuming and labor intensive. Overnight ECG signal was developed to examine OSA, with easy implementation and portable equipment. There were various ECG derived features used for OSA identification, in this study, intrinsic mode function (IMF) was developed. IMF is a byproduct of Hilbert-Huang transform. IMF decompose original signal into various sub components, due to its complexity. In this study, some novel IMF derived features were used to examine the OSA duration measured from ECG signal, compared with traditional HRV features.

Validation of Watch-PAT-200 Against Polysomnography During Pregnancy

To determine the relationships between key variables obtained from ambulatory polysomnography (PSG) and the wrist-worn Watch-PAT 200 device in pregnant women. In this prospective cohort study, women in their third trimester of pregnancy underwent full overnight home PSG using the 22-channel MediPalm system and the Watch-PAT 200 device. PSGs were scored by a blinded, experienced technologist using AASM 2007 criteria; the Watch-PAT was scored automatically by the manufacturer's proprietary software. A total of 31 pregnant women were studied. Mean age was 30.2 ± 7.1 years; mean gestational age was 33.4 ± 3.0 weeks; mean BMI was 31.9 ± 8.1 kg/m(2); 39% of women were nulliparous. Key variables generated by PSG and Watch-PAT correlated well over a wide range, including the apnea-hypopnea index (AHI, r = 0.76, p < 0.001); respiratory disturbance index (RDI, r = 0.68, p < 0.001), mean oxygen saturation (r = 0.94, p < 0.001), and minimum oxygen saturation (r = 0.88, p < 0.001). The area under the curve for AHI ≥ 5 and RDI ≥ 10 were 0.96 and 0.94, respectively. Association between stage 3 sleep on PSG and deep sleep on Watch-PAT was poor. Watch-PAT tended to overscore RDI, particularly as severity increased. Among pregnant women, Watch-PAT demonstrates excellent sensitivity and specificity for identification of obstructive sleep apnea, defined as AHI ≥ 5 on full PSG. Watch-PAT may overestimate RDI somewhat, especially at high RDI values.

The Epworth Sleepiness Scale in the Identification of Obstructive Sleep Apnea

The Epworth sleepiness scale (ESS) is often used clinically to screen for the manifestations of the behavioral morbidity associated to obstructive sleep apnea (OSA). The purpose of this study was to assess the sensitivity of the ESS in the identification of OSA, as defined by an elevated apnea-hypopnea index. A retrospective chart review of 268 consecutive patients diagnosed with OSA at a multidisciplinary sleep medicine clinic was conducted. The ESS obtained a relatively low sensitivity (66%) in the identification of an apnea-hypopnea index of 5 and above at the suggested cutoff of 10 and increased to 76% at 8. The results of the study showed only fair discriminatory ability of the ESS as a screener for OSA. A score of 8 (instead of 10) is suggested as the cutoff among clinic populations being screened for a sleep disorder.

Do sleep studies effectively predict postoperative ventilatory requirements in patients undergoing bariatric surgery?

Preoperative identification of obstructive sleep apnea (OSA) may help in the selection of patients who require ventilatory support. This study investigates the value of sleep studies in predicting the requirement for postoperative continuous positive airway pressure ventilation (CPAP) and high-dependency unit (HDU) admission.

Identification of obstructive sleep apnea in patients who snore: Tami TA, Duncan HJ, Pfleger M, et al. Laryngoscope 108:508, 1998

Identification of obstructive sleep apnea in patients who snore: Tami TA, Duncan HJ, Pfleger M, et al. Laryngoscope 108:508, 1998

Identification of obstructive sleep apnea in patients who snore.

As the field of sleep medicine has evolved, the clinical implications of obstructive sleep apnea (OSA) in snoring patients have become well accepted. Recent advances in surgical therapy for snoring allow otolaryngologists to offer simple outpatient treatment to patients with this problem. However, because the incidence of OSA in snorers seeking medical attention is unknown, the appropriate pretreatment evaluation of these patients is a subject of continued debate. Ninety-four snoring patients were recruited for a study to determine the incidence of OSA in this highly selected population. Subjects answered an extensive sleep questionnaire to determine factors that might suggest a diagnosis of OSA. Level III ambulatory sleep studies were performed on each participant. The incidence of OSA in this group was 72% (42% severe and 30% mild to moderate). Twenty of the subjects with OSA also underwent formal level I sleep studies, and the diagnosis of OSA was confirmed in each instance. Although there was a relationship between body mass index and OSA and certain questions correlated with OSA, the sensitivity and specificity of these data alone or in combination were too low to recommend their use in lieu of a formal sleep study. Given the remarkably high incidence of OSA in this group, which may reflect that seen by otolaryngologists who treat snoring, a sleep study should be performed to diagnose OSA and institute therapy for this condition. Level III ambulatory monitoring devices may be the most cost-effective alternative for evaluating this high-risk population.