Apnea-hypopnea Index Thresholds Research Articles

Predicting Obstructive Sleep Apnea with Periodic Snoring Sound Recorded at Home.

The cost-effectiveness of diagnosing obstructive sleep apnea (OSA) could be improved by using a preliminary screening method among subjects with no suspicion of other sleep disorders. We aimed to evaluate the diagnostic value of periodic snoring sound recorded at home. We included 211 subjects, aged 18-83 (130 men), who were referred to our laboratory for suspicion of OSA, and had a technically successful overnight polygraphy, measured with the Nox T3 Sleep Monitor (Nox Medical, Iceland) with a built-in microphone. We analyzed the percentage of periodic snoring during the home sleep apnea study. Apnea-hypopnea index (AHI) ranged from 0.1 to 116 events/h and the percentage of periodic snoring from 1% to 97%. We found a strong positive correlation (r = 0.727, p < 0.001) between periodic snoring and AHI. The correlation was slightly stronger among female, younger, and obese subjects. The best threshold value of the periodic snoring for predicting an AHI > 15 events/h with as high sensitivity as possible was found to be 15%. There, sensitivity was 93.3%, specificity 35.1%, and negative predictive value 75.0%. According to our results, it is possible to set a periodic snoring threshold (15% or more) for the subject to advance to further sleep studies. Together with medical history and prior to more expensive studies, measuring periodic snoring at home is a simple and useful method for predicting the probability of OSA, in particular among women who are often unaware of their apnea-related snoring.

Value of routine polysomnography in bariatric surgery.

Obstructive sleep apnea (OSA), present in 60-70% of bariatric surgery patients, is a potentially life-threatening condition when not detected and managed appropriately. The best available method to identify the severity of OSA is polysomnography. However, routine polysomnography measurements have not been accepted as standard modality in bariatric surgery. We report our experience with routine polysomnography in a cohort of patients undergoing bariatric surgery to determine the true prevalence of OSA with respect to the different severity levels as determined by the apnea-hypopnea index (AHI). AHI data were retrospectively collected from all patients who underwent bariatric surgery from 2012 onward, when the performance of preoperative polysomnography became mandatory. Mild, moderate and severe OSA were defined as an AHI ≥5, ≥15 and ≥30/h, respectively. Prevalence and number needed to screen (NNS) were calculated for all OSA severity levels. A total of 1358 patients were included. OSA was detected in 813 (59.9%; NNS: 2) patients. Moreover, 405 (29.8%; NNS: 4) patients were diagnosed with an AHI ≥15/h and 213 (15.7%; NNS: 7) with severe OSA (AHI ≥30/h). Extreme AHI thresholds of ≥60 and ≥90/h were detected in 79 (5.8%; NNS: 18) and 17 (1.3%; NNS: 77) patients, respectively. One-third of the bariatric surgery patients have an AHI ≥15/h and would benefit from continuous positive airway pressure therapy. In order to increase perioperative safety and avoid the preventable risk of perioperative complications, we recommend mandatory P(S)G prior to bariatric surgery.

Diagnostic accuracy of a mathematical model to predict apnea-hypopnea index using nighttime pulse oximetry.

The intent of this study is to develop a predictive model to convert an oxygen desaturation index (ODI) to an apnea-hypopnea index (AHI). This model will then be compared to actual AHI to determine its precision. One thousand four hundred and sixty-seven subjects given polysomnograms with concurrent pulse oximetry between April 14, 2010, and February 7, 2012, were divided into model development (n = 733) and verification groups (n = 734) in order to develop a predictive model of AHI using ODI. Quadratic regression was used for model development. The coefficient of determination (r(2)) between the actual AHI and the predicted AHI (PredAHI) was 0.80 (r = 0.90), which was significant at a p < 0.001. The areas under the receiver operating characteristic curve ranged from 0.96 for AHI thresholds of ≥ 10 and ≥ 15/h to 0.97 for thresholds of ≥ 5 and ≥ 30/h. The algorithm described in this paper provides a convenient and accurate way to convert ODI to a predicted AHI. This tool makes it easier for clinicians to understand oximetry data in the context of traditional measures of sleep apnea.

Risk factors for residual obstructive sleep apnea after adenotonsillectomy in children.

To determine the prevalence of residual obstructive sleep apnea (OSA) in children who had adenotonsillectomy (AT) and to identify the risk factors for residual OSA after AT. Retrospective chart review. Children with OSA who had AT at a tertiary care children's hospital were reviewed. Data pertaining to demographics, past medical history, body mass index, tonsil and adenoid size, and polysomnography were obtained. Residual OSA was defined as apnea hypopnea index (AHI) greater than 2. The rate of residual OSA and risk factors for residual OSA were assessed. One hundred sixty-nine children with OSA underwent polysomnography before and after AT. The prevalence of residual OSA was 38%. The prevalence of residual OSA in obese patients (49%) was higher than that of nonobese patients (27%) (P = .02). Patients with neurological/developmental/craniofacial abnormalities had higher prevalence of residual OSA (44%) than patients without comorbidities (33%) (P < .05). The prevalence of residual OSA in patients with severe OSA (42%) was higher than patients with moderate (29%) or mild OSA (0%) (P = .03). Teenage patients (67%) had a higher prevalence of residual OSA than toddlers (27%), preschooler (33%), and middle childhood groups (29%) (P = .03). The majority of children had improvement in OSA after AT. The choice of AHI threshold used to define residual OSA influenced the prevalence of residual OSA. Teenagers and children with obesity, comorbidities including neurological/developmental/craniofacial abnormalities alone or in combination with asthma, or severe OSA have a high risk of residual OSA. 4 Laryngoscope, 126:2624-2629, 2016.

Hidden Markov modelling of intra-snore episode behavior of acoustic characteristics of obstructive sleep apnea patients

Obstructive sleep apnea (OSA) is a breathing disorder that can cause serious medical consequences. It is caused by full (apnea) or partial (hypopnea) obstructions of the upper airway during sleep. The gold standard for diagnosis of OSA is the polysomnography (PSG). The main measure for OSA diagnosis is the apnea-hypopnea index (AHI). However, the AHI is a time averaged summary measure of vast amounts of information gathered in an overnight PSG study. It cannot capture the dynamic characteristics associated with apnea/hypopnea events and their overnight distribution. The dynamic characteristics of apnea/hypopnea events are affected by the structural and functional characteristics of the upper airway. The upper airway characteristics also affect the upper airway collapsibility. These effects are manifested in snoring sounds generated from the vibrations of upper airway structures which are then modified by the upper airway geometric and physical characteristics. Hence, it is highly likely that the acoustical behavior of snoring is affected by the upper airway structural and functional characteristics. In the current work, we propose a novel method to model the intra-snore episode behavior of the acoustic characteristics of snoring sounds which can indirectly describe the instantaneous and temporal dynamics of the upper airway. We model the intra-snore episode acoustical behavior by using hidden Markov models (HMMs) with Mel frequency cepstral coefficients. Assuming significant differences in the anatomical and physiological upper airway configurations between low-AHI and high-AHI subjects, we defined different snorer groups with respect to AHI thresholds 15 and 30 and also developed HMM-based classifiers to classify snore episodes into those groups. We also define a measure called instantaneous apneaness score (IAS) in terms of the log-likelihoods produced by respective HMMs. IAS indicates the degree of class membership of each episode to one of the predefined groups as well as the instantaneous OSA severity. We then assigned each patient to an overall AHI band based on the majority vote of each episode of snoring. The proposed method has a diagnostic sensitivity and specificity between 87–91%.

Scoring criteria for portable monitor recordings: a comparison of four hypopnoea definitions in a population-based cohort

RationaleLimited-channel portable monitors (PMs) are increasingly used as an alternative to polysomnography (PSG) for the diagnosis of obstructive sleep apnoea (OSA). However, recommendations for the scoring of PM recordings are...

Acoustic Analysis of Snoring in the Diagnosis of Obstructive Sleep Apnea Syndrome: A Call for More Rigorous Studies.

Snoring is a common symptom of obstructive sleep apnea syndrome (OSA) and has recently been considered for diagnosis of OSA. The goal of the current study was to systematically determine the accuracy of acoustic analysis of snoring in the diagnosis of OSA using a meta-analysis. PubMed, Cochrane Library database, and EMBASE were searched up to July 15, 2014. A systematic review and meta-analysis of sensitivity, specificity, and other measures of accuracy of acoustic analysis of snoring in the diagnosis of OSA were conducted. The median of apneahypopnea index threshold was 10 events/h, range: 5-15 or 10-15 if aforementioned suggestion is adopted. A total of seven studies with 273 patients were included in the meta-analysis. The pooled estimates were as follows: sensitivity, 88% (95% confidence interval [CI]: 82-93%); specificity, 81% (95% CI: 72-88%); positive likelihood ratio (PLR), 4.44 (95% CI: 2.39-8.27); negative likelihood ratio (NLR), 0.15 (95% CI: 0.10-0.24); and diagnostic odds ratio (DOR), 32.18 (95% CI: 13.96-74.81). χ(2) values of sensitivity, specificity, PLR, NLR, and DOR were 2.37, 10.39, 12.57, 3.79, and 6.91 respectively (All p > 0.05). The area under the summary receiver operating characteristic curve was 0.93. Sensitivity analysis demonstrated that the pooled estimates were stable and reliable. The results of publication bias were not significant (p = 0.30). Acoustic analysis of snoring is a relatively accurate but not a strong method for diagnosing OSA. There is an urgent need for rigorous studies involving large samples and single snore event tests with an efficacy criterion that reflects the particular features of snoring acoustics for OSA diagnosis.

Sleep apnea prevalence in acute myocardial infarction — The Sleep Apnea in Post-acute Myocardial Infarction Patients (SAPAMI) Study

BackgroundWhile sleep apnea (SA) might be a modifiable cardiovascular risk factor, recent data suggest that SA is severely underdiagnosed in patients after acute myocardial infarction (MI). There is limited evidence about day–night variation of onset of MI on dependence of having SA. We therefore investigated the prevalence of SA and examined the day–night variation of onset of MI in acute MI patients. MethodsWe prospectively studied 782 consecutive patients admitted to the hospital with the diagnosis of acute MI. All subjects underwent sleep evaluations using a portable device after at least 48h post-admission. Using the apnea–hypopnea index (AHI), groups were defined as patients without SA (<5events/h), mild SA (5–15events/h), moderate SA (15–30events/h), and severe SA (≥30events/h). ResultsAlmost all patients (98%) underwent urgent coronary angiography and 91% of patients underwent primary PCI. Using a threshold of AHI≥5events/h, SA was present in 65.7% of patients after acute MI. Mild SA was present in 32.6%, moderate in 20.4% and severe in 12.7%. The day–night variation in the onset of MI in all groups of SA patients was similar to that observed in non-SA patients. From 6AM to 12PM, the frequency of MI was higher in both SA and non-SA patients, as compared to the interval from 12AM to 6AM (all p<0.05). ConclusionThere is a high prevalence of SA in patients presenting with acute MI. Peak time of MI onset in SA patients was between 6AM and noon, similar to that in the general population. Whether diagnosis and treatment of SA after MI will significantly improve outcomes in these patients remains to be determined.

Unconstrained Sleep Apnea Monitoring Using Polyvinylidene Fluoride Film-Based Sensor

We established and tested an unconstrained sleep apnea monitoring method using a polyvinylidene (PVDF) film-based sensor for continuous and accurate monitoring of apneic events occurred during sleep. Twenty-six sleep apnea patients and six normal subjects participated in this study. Subjects' respiratory signals were measured using the PVDF-based sensor during polysomnography. The PVDF sensor comprised a 4 × 1 array, and a thin silicon pad was placed over the sensor to prevent damage. Total thickness of the merged system was approximately 1.1mm which was thin enough to prevent the subject from being consciously aware of its presence. It was designed to be placed under subjects' backs and installed between a bed cover and mattress. The proposed method was based on the standard deviation of the PVDF signals, and it was applied to a test set for detecting apneic events. The method's performance was assessed by comparing the results with a sleep physician's manual scoring. The correlation coefficient for the apnea-hypopnea index (AHI) values between the methods was 0.94 (p < 0.001). The areas under the receiver operating curves at three AHI threshold levels (>5, >15, and >20) for sleep apnea diagnosis were 0.98, 0.99, and 0.98, respectively. For min-by-min apnea detection, the method classified sleep apnea with an average sensitivity of 72.9%, specificity of 90.6%, accuracy of 85.5%, and kappa statistic of 0.60. The developed system and method can be applied to sleep apnea detection in home or ambulatory monitoring.

Recognition of sleep-related breathing disorders from Holter-ECGS¨C a new robust method suitable for clinical screening applications

Detection of sleep-related breathing disorders (SRBD) in an early stage is highly desirable in order to route patients to appropriate diagnostics and therapy before severe consequences become overt. We present a traceable, robust and accurate method for detection and quantification of SRDB from Holter ECGs. It provides an epoch-based (1 min) statement as well as an estimate of the apnea-hypopnea-index (AHI) and appears suitable for screening for SBAS in routine Holter-ECGs. Time-series of QRS-amplitude (QRSA) and respiratory myogram interference (RMI) were extracted from 140 overnight Holter-ECGs recorded in parallel to polysomnograms (PSGs) in 121 patients. The only exclusion criterion was persistent atrial fibrillation; other co- morbidities such as diabetes, myocardial infarction or periodic limb movements as well as concomitant medication were admitted. The PSG annotations served as reference for ECG-based detection of the presence of respiratory events in epochs of 1 min duration. Detection was based on ROC analysis of a single classification feature that quantifies the joint time-locked occurrence of characteristic modulations in QRSA and RMI using normalized cross-correlation. Two different ROC-thresholds were considered, the first balancing sensitivity and specificity (Tbal), the second maximizing accuracy (Tacc). For AHI assessment, the local period of modulations in the QRSA-series was determined from its zero crossings. Epochs classified as apnea-positive were weighted with a factor 60s/period providing an estimate of the number of respiratory events in that epoch. The sum of these weights, normalized for the duration of the record, served as an AHI estimate. A threshold of AHI ¡Ý 15 was used for screening. We implemented a ternary strategy where a borderline-class collected all records with conflicting decisions for the two thresholds Tbal and Tacc. For the epoch-based detection, sensitivity was 85.5% and specificity was 86%. In the screening application (AHI ¡Ý 15), 16% of the recordings were classified as ¡®borderline¡;= . The sensitivity for the remaining 84% (117 recordings) was 100% with a specificity of 91% and a Kappa coefficient of 0.91. Robust, traceable and accurate detection of SRDB is possible from the Holter-ECG for a very heterogeneous real-world sample including different types of SRDB and typical co- morbidities. Since our approach does not make use of heart rate information, it may be even applicable in patients with severe arrhythmias.

Screening for sleep-disordered breathing in the general population: predictive performance of four questionnaires

Background : Several questionnaires have been used to screen for sleep-disordered breathing (SDB). Most of them have been validated in selected clinical populations, and little is known about their predictive performance in the general population. To investigate the accuracy of four questionnaires used for SDB screening in the general population: the Berlin Questionnaire (BQ), the Epworth Sleepiness Scale (ESS), the STOP-Bang (SB) and the adjusted-neck circumference (neck+). 2115 subjects (50.4% women, 58.4 ± 11.0 years old, BMI 26.2 ± 4.4 kg/m 2 ) participating in an ongoing population-based sleep cohort study (HypnoLaus, Lausanne, Switzerland) underwent complete polysomnographic recordings at home. Respiratory events were scored according to the AASM 2013 criteria. Prevalence of SDB was determined for apnea–hypopnea index (AHI) thresholds of ⩾15 and ⩾30. Interviewers administered the BQ and the ESS and calculated SB and neck+. SB includes the following items: snoring, daytime Tiredness, Observed apnea, high blood Pressure, Body-mass index ⩾35 kg/m 2 , Age ⩾ 50 yr, Neck girth ⩾ 40 cm, and male Gender. Neck + is obtained by measuring the neck girth in cm and adding 3 cm for the presence of snoring, 3 cm for observed apnea and 4 cm for high blood pressure. Subjects were considered at increased risk for SDB if BQ is positive in ⩾2 categories, ESS score ⩾11, SB score ⩾3 and neck+ ⩾43 cm. Assessment of questionnaire included their sensitivity, specificity, positive-predicted value (PPV) and negative-predicted value (NPV). Prevalence of SDB with AHI thresholds of ⩾15 and ⩾30 were respectively 36.4% and 15.0%. Percentages of positive BQ was 25.3%, ESS:13.4%, SB: 41.3% and neck+:29.0%. For an AHI ⩾ 15, sensitivity, specificity, PPV and NPV were respectively: BQ (39.4%, 82.7%, 56.6% and 70.5%), ESS (13.5%, 86.7%, 35.8% and 64.5%), SB (67.3%, 72.8%, 57.3% and 80.4%) and neck+ (52.3%, 83.8%, 64.0% and 76.1%). For an AHI ⩾ 30, sensitivity, specificity, PPV and NPV were respectively: BQ (48.7%, 78.8%, 28.8% and 89.7%), ESS (13.1, 86.6%, 14.2% and 85.4%), SB (81.7%, 65.6%, 28.7% and 95.5%) and neck+ (69.8%, 77.9%, 34.6% and 93.9%). Our population-based study suggests that questionnaires such as BQ, ESS, SB and neck+ could be used to rule out severe SDB considering their strong NPV. However they do not seem suitable to screen for moderate to severe SDB in the general population. Funding : Ligue pulmonaire vaudoise, Fondation Leenaards, Fond national Suisse and GSK.

Does This Patient Have Obstructive Sleep Apnea?

Obstructive sleep apnea is a common disease, responsible for daytime sleepiness. Prior to referring patients for definitive testing, the likelihood of obstructive sleep apnea should be established in the clinical examination. To systematically review the clinical examination accuracy in diagnosing obstructive sleep apnea. MEDLINE and reference lists from articles were searched from 1966 to June 2013. Titles and abstracts (n = 4449) were reviewed for eligibility and appraised for evidence levels. For inclusion, studies must have used full, attended nocturnal polysomnography for the reference standard (n = 42). Community and referral-based prevalence of obstructive sleep apnea; accuracy of symptoms and signs for the diagnosis of obstructive sleep apnea. The prevalence of sleep apnea in community-screened patients is 2% to 14% (sample sizes 360-1741) and 21% to 90% (sample sizes 42-2677) for patients referred for sleep evaluation. The prevalence varies based on the apnea-hypopnea index (AHI) threshold used for the evaluation (≥5 events/h, prevalence 14%; ≥15/h, prevalence 6%) and whether the disease definition requires symptoms in addition to an abnormal AHI (≥5/h with symptoms, prevalence 2%-4%). Among patients referred for sleep evaluation, those with sleep apnea weighed more (summary body mass index, 31.4; 95% CI, 30.5-32.2) than those without sleep apnea (summary BMI, 28.3; 95% CI, 27.6-29.0; P < .001 for the comparison). The most useful observation for identifying patients with obstructive sleep apnea was nocturnal choking or gasping (summary likelihood ratio [LR], 3.3; 95% CI, 2.1-4.6) when the diagnosis was established by AHI ≥10/h). Snoring is common in sleep apnea patients but is not useful for establishing the diagnosis (summary LR, 1.1; 95% CI, 1.0-1.1). Patients with mild snoring and body mass index lower than 26 are unlikely to have moderate or severe obstructive sleep apnea (LR, 0.07; 95% CI, 0.03-0.19 at threshold of AHI ≥15/h). Nocturnal gasping or choking is the most reliable indicator of obstructive sleep apnea, whereas snoring is not very specific. The clinical examination of patients with suspected obstructive sleep apnea is useful for selecting patients for more definitive testing.

Comparison of Polysomnographic and Portable Home Monitoring Assessments of Obstructive Sleep Apnea in Saskatchewan Women

To compare a commercially available, level III in-home diagnostic sleep test (Embletta, Embletta USA) and in-laboratory polysomnography (PSG) in women with suspected obstructive sleep apnea (OSA). Consecutive women scheduled for routine PSG testing for evaluation of clinically suspected OSA and who met inclusion⁄exclusion criteria, were invited to participate. An in-home Embletta portable monitor test was performed one week before or after diagnostic PSG. Forty-seven of 96 women who met the inclusion⁄exclusion criteria agreed to participate. The mean (± SD) age of the patients was 52.0 ± 11.0 years, with a mean body mass index of 34.86 ± 9.04 kg⁄m2, and 66% (31 of 47) of patients were at high risk for OSA according to the Berlin score. Paired analysis of the overall population revealed no significant difference in mean apnea⁄hypopnea index (AHI) between the two diagnostic methods (P = 0.475). At an AHI of ≥ 5, the Embletta test was highly sensitive (90.6%) in determining abnormal versus normal OSA, with a positive predictive value of 82.7%. However, a higher Embletta AHI threshold of ≥ 10 may be more useful, with a higher level of agreement (kappa coefficient) with PSG testing and a positive predictive value of 92.3%. The in-home study was less useful at distinguishing severe from nonsevere OSA, yielding a sensitivity of 50%. In women believed to be at high-risk for OSA, Embletta in-home sleep testing is useful for the detection of sleep disordered breathing.

Diagnostic Accuracy of Split-Night Polysomnograms

American Academy of Sleep Medicine (AASM) practice parameters indicate that split-night polysomnograms (SN-PSG) may be performed when the apnea hypopnea index (AHI) is > or = 20 to 40, depending on clinical factors. The aim of this study was to determine the diagnostic accuracy of SN-PSG, including at the lower range of AHIs. We reviewed 114 consecutive full-night PSGs (FN-PSG) performed at our center between August 2006 and November 2008 on subjects enrolled in studies in which obstructive sleep apnea (OSA) was the sleep disorder of interest. We compared the AHI from the first 2 hours (2 hr-AHI) and 3 hours (3 hr-AHI) of sleep with the "gold standard" AHI from FN-PSG (FN-AHI), considering OSA present if FN-AHI > or = 5. The 2 hr-AHI and 3 hr-AHI correlated strongly with the FN-AHI (concordance correlation coefficient [CCC] = 0.93 and 0.97, respectively). After adjusting for percentage of sleep in stage REM sleep and in supine position, the correlation of 2 hr- and 3 hr-AHI with FN-AHI remained strong (0.92 and 0.96, respectively). The area under the receiver operating curves (AUC) for 2 hr-AHI and 3 hr-AHI using FN-AHI > or = 5 were 0.93 and 0.95, respectively. The AHI derived from the first 2 or 3 hours of sleep is of sufficient diagnostic accuracy to rule-in OSA at an AHI threshold of 5 in patients suspected of having OSA. This study suggests that the current recommended threshold for split-night studies (AHI > or = 20 to 40) may be revised to a lower number, allowing for more efficient use of resources.

Continuous analysis and monitoring of snores and their relationship to the apnea‐hypopnea index

We used a new automatic snoring detection and analysis system to monitor snoring during full-night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea-hypopnea index (AHI) and to classify subjects according to their AHI. Individual Case-Control Study. Thirty-seven snorers (12 females and 25 males; ages 40-65 years; body mass index (BMI), 29.65 +/- 4.7 kg/m(2)) participated. Subjects were divided into three groups: G1 (AHI <5), G2 (AHI >or=5, <15) and G3 (AHI >or=15). Snore and breathing sounds were recorded with a tracheal microphone throughout 6 hours of nighttime polysomnography. The snoring episodes identified were automatically and continuously analyzed with a previously trained 2-layer feed-forward neural network. Snore number, average intensity, and power spectral density parameters were computed for every subject and compared among AHI groups. Subjects were classified using different AHI thresholds by means of a logistic regression model. There were significant differences in supine position between G1 and G3 in sound intensity; number of snores; standard deviation of the spectrum; power ratio in bands 0-500, 100-500, and 0-800 Hz; and the symmetry coefficient (P < .03). Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively. A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology.

Blood pressure associated with sleep-disordered breathing in a population sample of children.

The current criteria for sleep-disordered breathing (SDB) in children are not based on a clinically relevant outcome. The purpose of this study was to assess the association of blood pressure with SDB in a random sample of the local elementary school children (kindergarten through grade 5) using a 2-phased strategy. During phase 1, a brief questionnaire was completed for all of the children (N=5740) with a response rate of 78.5%. During phase 2, 700 randomly selected children from phase 1 with a response rate of 70.0% were assessed with a full polysomnograph and a history/physical, including an ECG; ear, nose, and throat; and pulmonary evaluation. We observed a significantly elevated systolic blood pressure associated with the apnea hypopnea index (AHI): AHI >or=1 (2.9 mm Hg); AHI >or=3 (7.1 mm Hg); and AHI >or=5 (12.9 mm Hg). The SDB and blood pressure association remained significant after adjusting for age, sex, race, body mass index percentile or waist circumference, sleep efficiency, percentage of rapid eye movement sleep, and snoring. In addition, older age, body mass index percentile, waist circumference, and snoring were significantly associated with blood pressure, independent of SDB. Based on these findings, our study suggests that SDB is significantly associated with higher levels of systolic blood pressure in children aged 5 to 12 years even after adjusting for the various confounding factors. Clinically, the data support the threshold of AHI >or=5 for the initiation of treatment for SDB. Additional research is indicated to assess the efficacy of SDB treatment on reducing blood pressure.

Electrocardiogram Recording as a Screening Tool for Sleep Disordered Breathing

Transient changes in heart rate associated with obstructive apneas have been suggested for screening of sleep disordered breathing (SDB). This study prospectively compares the outcomes of an automated ECG-based SDB screening tool with simultaneous polysomnography. The previously-developed automated algorithm was applied to a single channel ECG obtained during standard overnight polysomnography (92 subjects) to obtain an apnea-hypopnea index (AHI) estimate. Using AHI thresholds of < 5 and > or =15 to define absence and presence of SDB, respectively, we determined the likelihood ratios of the proposed technique. The automated algorithm achieved positive and negative likelihood ratios of 2.16 and 0.08. Estimated and reference AHI were highly correlated (r = 0.88). Pathologically insignificant arrhythmia in some subjects had no discernible impact on the algorithm. ECG-based assessment provides a simple but limited means of recognizing subjects with obstructive sleep apnea.

Nightly variability of sleep-disordered breathing measured over 3 nights

To examine the nightly variability of sleep-disordered breathing (SDB) as measured by the apnea-hypopnea index (AHI). Retrospective comparison of 3 sequential nights of testing performed in the home in 1091 patients who were referred for diagnostic testing of SDB. The Pearson and Intraclass correlation coefficients ranged between 0.88 and 0.90 for each pair of nights. Based on night 1, approximately 90% of patients were classified consistently with "AHI-high" (the highest AHI measured across the 3 nights) using an AHI threshold of 5. However, 10% were misclassified on night 1 relative to the highest AHI level. These findings suggest that (1) 1 night of diagnostic testing for SDB is not sufficient to diagnosis SDB in approximately 1 of every 10 cases, and (2) there is little, if any, significant nightly change in SDB in the home environment. D.

Assessment of a wrist-worn device in the detection of obstructive sleep apnea

Objective: To assess the accuracy of a wrist-worn device (Watch_PAT100) to diagnose obstructive sleep apnea (OSA). Methods: Thirty adult subjects with and without suspected OSA simultaneously had a standard in-laboratory polysomnogram (PSG) and wore the Watch_PAT100 during a full-night recording. PSG sleep and respiratory events were scored according to standard criteria. Watch_PAT data were analyzed with an automated computerized algorithm which calculated the frequency of respiratory events per hour of actigraphy measured sleep using a combination of peripheral arterial tonometry (PAT) signal attenuation, desaturation on pulse oximetry, and changes in heart rate. This yielded a PAT apnea hypopnea index (AHI). Results: Mean age was 47.0±14.8 years, mean body mass index 31.0±7.6 kg/m 2, mean PSG AHI 23±23.9 events per hour, and mean PAT AHI 23±15.9 events per hour. There was a significant correlation between PAT AHI and AHI by PSG ( r=0.87, P<0.001). To assess sensitivity and specificity of Watch_PAT, we constructed receiver operator characteristic curves using a variety of AHI threshold values (10, 15, 20, and 30 events per hour). Optimal combinations of sensitivity and specificity for the various thresholds were 82.6/71.4, 93.3/73.3, 90.9/84.2, and 83.3/91.7, respectively. Conclusions: The Watch_PAT is a device that can detect OSA with reasonable accuracy. Thus, the Watch_PAT may be a useful method to diagnose OSA.

Utility of Home Oximetry as a Screening Test for Patients with Moderate to Severe Symptoms of Obstructive Sleep Apnea

To determine the value of home oximetry as a screening test in patients with moderate to severe symptoms of obstructive sleep apnea (OSA). Retrospective, observational study. The Sleep Unit of a tertiary referral, university hospital. 116 patients referred for evaluation of moderate to severe symptoms of OSA in which both home oximetry and polysomnography (PSG) were performed. NA. Three numerical oximetry indices were evaluated: average of desaturations > or =4% and average of resaturations > or =3% per hour of analysis time (DI4% and RI3%, respectively); and cumulative percentages of time spent at saturations below 90% (CT90%). A qualitative assessment was also performed. Oximetry indices were compared with apnea/hypopnea index (AHI) by simple linear regression and Bland-Altman analyses. Optimal cut-off points, in terms of sensitivity and specificity, for the oximetry indices were searched using ROC analysis, at an AHI threshold of > or =10. The correlation between AHI and the desaturation indices was r = 0.50 for CT90%, r = 0.60 for DI4%, and r = 0.58 for RI3%. No bias was found between PSG and oximetry indices in Bland-Altman plots. Neither the numerical indices nor the qualitative analysis achieved an adequate (>0.8) area under the ROC curve. A CT90% <0.79 excluded OSA with 84% sensitivity. A DI4% > or =31.4 or a RI3% > or =40.5 diagnosed OSA with 97% specificity. Using these values, 38% of the patients would have been correctly classified by oximetry alone, 10% would have been incorrectly classified, and 50% could not have been classified with certainty. Eleven (15%) OSA patients would have been missed by oximetry. Correlation between home oximetry and PSG was not high. Oximetry was more useful to confirm than to exclude OSA in our study. Qualitative assessment was not better than numerical analysis. The greatest value of oximetry in this setting seems to be as a tool to rapidly recognize and treat more severe OSA patients in waiting list for PSG.