Apnea-hypopnea Index Cutoff Research Articles

Validation of the European Obstructive Sleep Apnea Screening (EUROSAS) in Professional Male Drivers.

(1) Background: The European Union Driver License Committee recently developed a questionnaire as a screening tool for obstructive sleep apnea (OSA), named the European Obstructive Sleep Apnea Screening (EUROSAS) questionnaire for drivers. The aim of the current study was to investigate the diagnostic performance of the EUROSAS to predict risk of OSA in professional male drivers. (2) Methods: Fifty-eight drivers were included in the current study. All participants were asked to fill out the EUROSAS before an overnight polysomnography (PSG) in the hospital. OSA was defined as an apnea-hypopnea index (AHI) 5 events/hour on the PSG. (3) Results: Out of 58 participants, the EUROSAS correctly identified 39 (67.2%) cases as having high-risk OSA and one patient as having low-risk OSA, using AHI ≥ 5 events/h. The results indicated that the EUROSAS has a sensitivity of 67.2%, a specificity of 33.3%, a positive predictive value of 94.8%, and a negative predictive value of 5.2%. Similar results were obtained using AHI cut-offs of 15 and 30 events/h. (4) Conclusions: The EUROSAS provides a moderate level of accuracy for the screening of OSA in the professional male drivers. It seems that the diagnostic performance of the EUROSAS is not promising as an alternative questionnaire to identify professional drivers with OSA, probably due to participant response bias. Despite its limited evidence, the EUROSAS might have potential as a clinical screening tool in the general population.

ECG-based convolutional neural network in pediatric obstructive sleep apnea diagnosis

Obstructive sleep apnea (OSA) is a prevalent respiratory condition in children and is characterized by partial or complete obstruction of the upper airway during sleep. The respiratory events in OSA induce transient alterations of the cardiovascular system that ultimately can lead to increased cardiovascular risk in affected children. Therefore, a timely and accurate diagnosis is of utmost importance. However, polysomnography (PSG), the standard diagnostic test for pediatric OSA, is complex, uncomfortable, costly, and relatively inaccessible, particularly in low-resource environments, thereby resulting in substantial underdiagnosis. Here, we propose a novel deep-learning approach to simplify the diagnosis of pediatric OSA using raw electrocardiogram tracing (ECG). Specifically, a new convolutional neural network (CNN)-based regression model was implemented to automatically predict pediatric OSA by estimating its severity based on the apnea-hypopnea index (AHI) and deriving 4 OSA severity categories. For this purpose, overnight ECGs from 1,610 PSG recordings obtained from the Childhood Adenotonsillectomy Trial (CHAT) database were used. The database was randomly divided into approximately 60%, 20%, and 20% for training, validation, and testing, respectively. The diagnostic performance of the proposed CNN model largely outperformed the most accurate previous algorithms that relied on ECG-derived features (4-class Cohen's kappa coefficient of 0.373 versus 0.166). Specifically, for AHI cutoff values of 1, 5, and 10 events/hour, the binary classification achieved sensitivities of 84.19%, 76.67%, and 53.66%; specificities of 46.15%, 91.39%, and 98.06%; and accuracies of 75.92%, 86.96%, and 91.97%, respectively. Therefore, pediatric OSA can be readily identified by our proposed CNN model, which provides a simpler, faster, and more accessible diagnostic test that can be implemented in clinical practice.

An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals

Deep-learning algorithms have been proposed to analyze overnight airflow (AF) and oximetry (SpO2) signals to simplify the diagnosis of pediatric obstructive sleep apnea (OSA), but current algorithms are hardly interpretable. Explainable artificial intelligence (XAI) algorithms can clarify the models-derived predictions on these signals, enhancing their diagnostic trustworthiness. Here, we assess an explainable architecture that combines convolutional and recurrent neural networks (CNN + RNN) to detect pediatric OSA and its severity. AF and SpO2 were obtained from the Childhood Adenotonsillectomy Trial (CHAT) public database (n = 1,638) and a proprietary database (n = 974). These signals were arranged in 30-min segments and processed by the CNN + RNN architecture to derive the number of apneic events per segment. The apnea-hypopnea index (AHI) was computed from the CNN + RNN-derived estimates and grouped into four OSA severity levels. The Gradient-weighted Class Activation Mapping (Grad-CAM) XAI algorithm was used to identify and interpret novel OSA-related patterns of interest. The AHI regression reached very high agreement (intraclass correlation coefficient > 0.9), while OSA severity classification achieved 4-class accuracies 74.51% and 62.31%, and 4-class Cohen’s Kappa 0.6231 and 0.4495, in CHAT and the private datasets, respectively. All diagnostic accuracies on increasing AHI cutoffs (1, 5 and 10 events/h) surpassed 84%. The Grad-CAM heatmaps revealed that the model focuses on sudden AF cessations and SpO2 drops to detect apneas and hypopneas with desaturations, and often discards patterns of hypopneas linked to arousals. Therefore, an interpretable CNN + RNN model to analyze AF and SpO2 can be helpful as a diagnostic alternative in symptomatic children at risk of OSA.

Role of automated detection of respiratory related heart rate changes in the diagnosis of sleep disordered breathing

Study objectivesThe objective of this study was to determine whether electrocardiogram (ECG) and heart rate accelerations that occur in the vicinity of respiratory events could predict the severity of sleep-disordered breathing (SDB).MethodsDe-identified polysomnogram (NPSG) recordings from 2091 eligible participants in the Sleep Heart Health Study (SHHS) were evaluated after developing and validating an automated algorithm using an initial set of recordings from 1,438 participants to detect RR interval (RRI) dips in ECG and heart rate accelerations from pulse rate signal. Within-subject comparisons were made between the apnea-hypopnea index (AHI) and both the total RRI dip index (total RRDI) and total heart rate acceleration index (total HRAI).ResultsThe estimated AHIs using respiratory-related HRAI correlated with NPSG AHI both in the unadjusted and adjusted model (B: 0.83 and 0.81, respectively P < 0.05). Respiratory-related HRAI had a strong agreement with NPSG AHI (intraclass correlation coefficient-ICC: 0.64, whereas respiratory-related RRDI displayed weaker agreement and ICC: 0.38). Further assessment of respiratory-related HRAI (≥5 events/h) showed a strong diagnostic ability (78, 87, 81, and 56% agreement for traditional AHI cutoffs 5, 10, 15, and 30 events/h, respectively). At the AHI cutoff of 5 events/h the receiver operating curves (ROC) revealed an area under the curve (AUCs) of 0.90 and 0.96 for RE RRDI and RE HRAI respectively.ConclusionThe automated respiratory-related heart rate measurements derived from pulse rate provide an accurate method to detect the presence of SDB. Therefore, the ability of mathematical models to accurately detect respiratory-related heart rate changes from pulse rate may enable an additional method to diagnose SDB.

0797 Correlation between Obstructive Sleep Apnea and Echocardiography Parameters in Infants and Preschool Children with Down Syndrome

Abstract Introduction Down syndrome (DS) is one of the most common genetic disorders caused by the Trisomy of chromosome 21. Children with DS secondary to the unique airway anatomy and hypotonia, are at risk for obstructive sleep apnea (OSA). Untreated OSA in children can lead to serious complications because of chronic hypoxia leading to Pulmonary hypertension (PHTN) and Cor pulmonale. An overnight polysomnogram (PSG), the gold standard for diagnosing OSA in children, should be performed within 4 years per American Academy of Pediatrics recommendations. However, OSA in DS has been observed at a much younger age. Methods In this single-center retrospective study, data was collected by reviewing the medical records and PSG REDCap database from 2019-2021. Patients with ICD code 10 diagnosis of DS between 0 to 4 years of age who underwent PSG and echocardiography were included. Our primary aim was to assess the severity of OSA in preschool children and describe echocardiogram parameters in DS patients with OSA. We hypothesized that there will be differences in polysomnogram parameters of DS children with and without PHTN. Results 61 patients met the criteria for the study with the mean age being 11.3 months. 3 patients (5.4%) did not have OSA (apnea hypopnea index (AHI)< 1/hr), 11 patients (19.6%) had mild OSA (AHI 1-4.9/hr), 12 patients (21.4%) had moderate OSA (AHI 5-10/hr), 30 patients had severe OSA (AHI >10/hr). Five patients with missing AHI values were excluded for subsequent analyses. Echocardiogram parameters were compared using the described AHI cut offs to define severity of OSA. OSA was observed to be significantly higher in the group with elevated RV systolic pressure. Conclusion Although our study did not find any difference in PSG findings in young children with DS with and without PHTN, obstructive apnea index was significantly elevated in young children with elevated RV systolic pressure. OSA is a significant contributor to the development of PHTN in children with DS. Children with DS are at higher risk for PHTN due to their unique pulmonary vasculature. As PHTN is a significant comorbidity of DS, it is important to perform PSGs early during childhood to detect and treat OSA. Support (if any)

Measuring Sleep Stages and Screening for Obstructive Sleep Apnea with a Wearable Multi-Sensor System in Comparison to Polysomnography.

To assess the performance of a wearable multi-sensor system (SensEcho) in comparison to polysomnography (PSG) in measuring sleep stages and searching for obstructive sleep apnea (OSA). Participants underwent overnight simultaneous monitoring using SensEcho and PSG in a sleep laboratory. SensEcho analyzed the recordings spontaneously, and PSG was assessed as per standard guidelines. The degree of snoring was evaluated according to the guidelines for the diagnosis and treatment of OSA hypopnea syndrome (2011 revision). The Epworth Sleepiness Scale (ESS) was used to assess general daytime sleepiness. This study included 103 Han Chinese, 91 of whom (age 39.02 ± 13.84 years, body mass index 27.28 ± 5.12 kg/m2, 61.54% male) completed the assessments. The measures of total sleep time (P = 0.198); total wake time (P = 0.182); shallow sleep (P = 0.297), deep sleep (P = 0.422), rapid eye movement sleep (P = 0.570), and awake (P = 0.336) proportions were similar between SensEcho and PSG. Using an apnea-hypopnea index (AHI) cutoff of ≥ 5 events/h, the SensEcho had 82.69% sensitivity and 89.74% specificity. Almost the same results were obtained at an AHI threshold of ≥ 15 events/h. Although the specificity increased to 94.67%, it decreased to 43.75% at an AHI cutoff of ≥ 30 events/h. This study demonstrated that SensEcho can be used to evaluate sleep status and screen for OSA. Nevertheless, improving the accuracy of its assessment of severe OSA and further testing its effectiveness in community and home environments is necessary.

Screening for obstructive sleep apnea: comparing the American Academy of Sleep Medicine proposed criteria with the STOP-Bang, NoSAS, and GOAL instruments.

We evaluated the performance of the 2017 American Academy of Sleep Medicine criteria (AASM2017) in screening obstructive sleep apnea (OSA) and compared them with 3 other validated instruments: NoSAS score, STOP-Bang, and GOAL questionnaires. From July 2019 to December 2021, 4,499 adults undergoing overnight polysomnography were included. The AASM2017 instrument considers an increased high risk for moderate-to-severe OSA in the presence of excessive daytime sleepiness and at least 2 of the following 3 criteria: loud snoring; observed apnea, gasping, or choking; and hypertension. OSA severity was based on polysomnography-derived apnea-hypopnea index cutoffs: 5.0 events/h, 15.0 events/h, and 30.0 events/h. Predictive performance was evaluated by the area under the curve and contingency tables. When screening for any OSA severity, AASM2017 displayed a sensitivity of 31.0-40.6% and a specificity of 80.8-89.6%. For all apnea-hypopnea index thresholds, AASM2017, unlike the GOAL, STOP-Bang, and NoSAS, exhibited superior specificity but markedly lower sensitivity. GOAL, STOP-Bang, and NoSAS, but not AASM2017 criteria, emerged as an adequate screening tool for any OSA severity (all areas under the curve > 0.7) and performed significantly better than AASM2017 in predicting any OSA severity (all P < .001). For all severity OSA levels, GOAL, STOP-Bang, and NoSAS displayed similar performance when compared to each other (all P > .05). GOAL, STOP-Bang, and NoSAS instruments, but not AASM2017 criteria, emerge as useful OSA screening tools in a large referral single-center clinical cohort. Duarte RLM, Magalhães-da-Silveira FJ, Gozal D. Screening for obstructive sleep apnea: comparing the American Academy of Sleep Medicine proposed criteria with the STOP-Bang, NoSAS, and GOAL instruments. J Clin Sleep Med. 2023;19(7):1239-1246.

Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening

Objective Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening. Methods Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea–hypopnea index cutoff thresholds: [Formula: see text] 5 and [Formula: see text] 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database ( N = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis. Results For cutoff [Formula: see text] 5/hours, logistic regression model achieved the highest Matthew’s correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff [Formula: see text] 30/hours, support vector machine model achieved the highest Matthew’s correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%). Conclusions Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters.

Central Sleep Apnea Detection by Means of Finger Photoplethysmography.

For 266 patients with a suspicion of SA, concurrent in-lab PSG and PPG data were acquired. The respiratory information embedded in the PPG data was extracted and used to train an ensemble of trees classifiers that predicts the central or obstructive nature of each respiratory event. The classifier performance was evaluated using patient-wise leave-one-out cross-validation where an expert analysis of the PSG served as ground truth. A second, independent analysis of the PSG was also evaluated against the ground truth to allow benchmarking of the PPG-based method. The method achieved a sensitivity of 81%, a specificity of 99%, a positive predictive value of 90%, and a negative predictive value of 98% at the central apnea-hypopnea index cutoff of 10 events per hour of sleep. The present study aimed to evaluate a method to detect CSA in SA patients using only PPG data which could be used to flag CSA which in turn may aid in more optimal therapy decision making.

Application value of joint STOP-Bang questionnaire and Epworth Sleepiness Scale in screening for obstructive sleep apnea.

ObjectiveThis paper evaluates the application value of the STOP-Bang questionnaire combined with the Epworth Sleepiness Scale (ESS) in screening for obstructive sleep apnea (OSA) in the population.MethodThousand-six hundred seventy-one patients with suspected OSA who visited the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University from August 2017 to August 2020 were monitored by overnight polysomnography (PSG) after completing the ESS scale and STOP-Bang questionnaire. The sensitivity, specificity, positive predictive value, negative predictive value and receiver operating characteristic (ROC) curves of the two scales were calculated, and the accuracy in predicting OSA of the STOP-Bang questionnaire combined with ESS was analyzed.ResultsWith Apnea Hypopnea Index (AHI) cutoffs of ≥5, ≥15 and ≥30 events/h, the areas under the ROC curve scored by STOP-Bang were 0.724, 0.703 and 0.712, and those of ESS were 0.632, 0.634 and 0.695; the diagnostic odds ratio (DOR) values of STOP-Bang for OSA, moderate to severe OSA, and severe OSA were 3.349, 2.651 and 3.189, and those of ESS were 2.665, 2.279 and 3.289. The STOP-Bang score of three was used as the cut-off point for OSA diagnosis with higher sensitivity and lower specificity, while ESS had higher specificity. STOP-Bang (≥3) combined with ESS significantly improved its specificity for predicting OSA.ConclusionThe STOP-Bang questionnaire is a simple and effective new tool for screening patients for OSA, while a STOP-Bang score of ≥3 combined with ESS can further improve its specificity. Thus, we suggest further screening with ESS after a STOP-Bang score of ≥3 in suspected patients.

Quality measures for the care of adult patients with obstructive sleep apnea: 2022 update after measure maintenance.

Lloyd R, Morgenthaler TI, Donald R, etal. Quality measures for the care of adult patients with obstructive sleep apnea: 2022 update after measure maintenance. J Clin Sleep Med. 2022;18(11):2673-2680.

Diagnostic accuracy of home sleep apnea testing using peripheral arterial tonometry for sleep apnea: A systematic review and meta-analysis.

This study aimed to evaluate the diagnostic accuracy of home sleep apnea testing using peripheral arterial tonometry for sleep apnea as an alternative to polysomnography. We conducted a systematic review and meta-analysis of observational studies, randomized controlled trials, and diagnostic case-control studies examining the diagnostic accuracy of peripheral arterial tonometry by searching the CENTRAL, MEDLINE, EMBASE, ICTRP and ClinicalTrials.gov databases on 5 October 2021. We assessed the risk of bias of the included studies using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random-effects model was generated to derive the summary point estimates of sensitivity and specificity with 95% confidence intervals at different apnea-hypopnea index cutoffs. This meta-analysis included 13 studies (1227 participants, median prevalence of sleep apnea with apnea-hypopnea index ≥ 5 events per hr: 85%). The risk of bias in the included studies was low to moderate. The pooled sensitivity and specificity estimates were 96% (95% confidence interval: 93%-97%) and 44% (95% confidence interval: 32%-56%) at apnea-hypopnea index ≥ 5 events per hr, 88% (85%-91%) and 74% (63%-83%) at apnea-hypopnea index ≧ 15 events per hr, and 80% (66%-89%) and 90% (83%-95%) at apnea-hypopnea index ≧ 30 events per hr, respectively. Peripheral arterial tonometry resulted in a significant number of false negatives and false positives at any apnea-hypopnea index cutoff when applied to the median prevalence setting of the included studies. The inadequate sensitivity and specificity of peripheral arterial tonometry render it an unsuitable alternative to polysomnography for detecting sleep apnea for apnea-hypopnea index ≧ 5, 15 and 30 events per hr.

A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

The gold standard approach to diagnose obstructive sleep apnea (OSA) in children is overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous to healthcare systems and families. Simplification of PSG should enhance availability and comfort, and reduce complexity and waitlists. Airflow (AF) and oximetry (SpO2) signals summarize most of the information needed to detect apneas and hypopneas, but automatic analysis of these signals using deep-learning algorithms has not been extensively investigated in the pediatric context. The aim of this study was to evaluate a convolutional neural network (CNN) architecture based on these two signals to estimate the severity of pediatric OSA. PSG-derived AF and SpO2 signals from the Childhood Adenotonsillectomy Trial (CHAT) database (1638 recordings), as well as from a clinical database (974 recordings), were analyzed. A 2D CNN fed with AF and SpO2 signals was implemented to estimate the number of apneic events, and the total apnea-hypopnea index (AHI) was estimated. A training-validation-test strategy was used to train the CNN, adjust the hyperparameters, and assess the diagnostic ability of the algorithm, respectively. Classification into four OSA severity levels (no OSA, mild, moderate, or severe) reached 4-class accuracy and Cohen's Kappa of 72.55% and 0.6011 in the CHAT test set, and 61.79% and 0.4469 in the clinical dataset, respectively. Binary classification accuracy using AHI cutoffs 1, 5 and 10 events/h ranged between 84.64% and 94.44% in CHAT, and 84.10%–90.26% in the clinical database. The proposed CNN-based architecture achieved high diagnostic ability in two independent databases, outperforming previous approaches that employed SpO2 signals alone, or other classical feature-engineering approaches. Therefore, analysis of AF and SpO2 signals using deep learning can be useful to deploy reliable computer-aided diagnostic tools for childhood OSA.

Identifying obstructive sleep apnea in patients with epilepsy: A cross-sectional multicenter study

ObjectiveSeveral screening questionnaires for obstructive sleep apnea (OSA) have been introduced. No study has compared the performance of different questionnaire in the same patients with epilepsy. Herein, we compare the performance characteristics of four common questionnaires for assessing the probability of OSA in patients with epilepsy. MethodsThis cross-sectional multicenter study was conducted among adult epilepsy patients attending neurology and general medical clinics in Thailand. Before performing full polysomnography (PSG), all participants completed the STOP-BANG, STOP-BAG, SA-SDQ, and NoSAS questionnaires. OSA was defined by apnea/ hypopnea index (AHI) criteria of AHI: ≥ 5, ≥ 15, and ≥ 30. Discriminatory ability was assessed by area under the receiver operating characteristics (ROC) curve (AUC) and likelihood ratio. To improve discriminative ability, we created 3 ranges of the score to predict lower, middle, and higher probability of OSA as defined by each diagnostic criterion. ResultsA total 166 patients with epilepsy were included. OSA prevalence was 38%. Overall, NoSAS had higher AUC at all AHI criteria but not significantly higher than that of other scales. Using prediction cut-points for NoSAS of ≥ 4 and ≥ 8 to predict OSA defined by AHI ≥ 5, the likelihood ratios for the 3 ranges were 0.37, 2.22 and 9.81 respectively. ConclusionAmong the 4 scales, the 2-cut-point NoSAS score had the highest discriminatory ability at each AHI cutoff.

Application value of joint NoSAS score and Epworth Sleepiness Scale for assessment of obstructive sleep apnea hypopnea syndrome

ObjectiveBy comparing the predictive value of the NoSAS (Neck circumference, Obesity, Snoring, Age and Sex) score combined with the Epworth Sleepiness Scale (ESS), STOP-Bang Questionnaire (STOP-Bang), STOP Questionnaire (STOP) and Berlin Questionnaire (Berlin), the application value of the NoSAS score combined with ESS in screening Obstructive sleep apnea hypopnea syndrome (OSAHS) in the population is evaluated. Method2560 suspected OSAHS patients visited the Sleep Medical Center of the First Hospital of Guangzhou Medical University between September 1, 2016 and October 31, 2020, and were monitored with a polysomnogram (PSG) after completing the NoSAS score, ESS, STOP-Bang, STOP and Berlin. The sensitivity, specificity, positive predictive value, negative predictive value and receiver operating characteristic (ROC) curve of each scale were calculated, and the accuracy in predicting OSAHS of the NoSAS score combined with ESS and each scale was analyzed. ResultsThe areas under the ROC curve scored by Berlin were higher than those of the other four questionnaires with Apnea Hypopnea Index (AHI) cutoffs of ≥5 and ≥ 10 events/h, while the area under the ROC curve scored by the NoSAS score was the highest with AHI cutoffs of ≥15, ≥20, ≥25 and ≥ 30 events/h. Among the five scales, the diagnostic odds ratio (DOR) of the NoSAS score was the highest. When a NoSAS score of ≥7 was used as the cutoff point for diagnostic NoSAS, it had higher sensitivity and specificity with a NoSAS score of ≥8 as the cutoff point for diagnostic NoSAS. A NoSAS score of ≥7 combined with ESS significantly improved its specificity for predicted OSAHS patients. ConclusionThe NoSAS score is a simple and effective new tool for screening patients for OSAHS, while a NoSAS score of ≥7 combined with ESS can further improve its specificity. Thus, we suggest further screening with ESS after a NoSAS score of ≥7 in suspected populations.

0724 Age-related AHI cut-offs associated with cardiovascular and cerebrovascular disorders: clinical implications

Abstract Introduction Several studies have shown that the association of obstructive sleep apnea (OSA) with clinical outcomes, such as hypertension, weakens in older adults. An apnea hypopnea index (AHI) ≥5 is recommended as the cut-off for initiating treatment without any consideration of possible age differences. We aimed to examine at which cut-off point of AHI, OSA is associated with cardiovascular and/or cerebrovascular diseases (CBVD) in middle-aged adults and in older adults. Methods We studied 1,741 adults from the Penn State Adult Cohort (age 20-88 years, 52.3% female, 12.4% racial/ethnic minority), who underwent a 1-night sleep laboratory evaluation, clinical history and physical examination. Hypertension was defined as a diastolic blood pressure ≥90 mmHg or a systolic blood pressure ≥140 mmHg or the use of anti-hypertensive medication. CBVD was defined based on a self-report of a physician diagnosis of heart disease and/or stroke. Logistic regression models examined the odds of having hypertension or CBVD in a step-wise manner starting from an AHI ≥5 up to AHI ≥30 by increments of 5 events, while simultaneously adjusting for sex, race and BMI and stratifying by age. Results The odds of hypertension were significantly increased in adults aged &amp;lt;60y (43.2±9.1y) with an AHI≥5 (OR=1.56, 95% CI 1.01-2.41, p=0.045), while the odds of hypertension were not significantly increased in adults aged ≥60y (68.4±6.3y) for any given AHI cut-off. The odds of CBVD were significantly increased in adults aged ≥60y with an AHI≥15 (OR=2.50, 95% 1.24-5.04, p=0.011), while the odds of CBVD were not significantly increased in adults aged &amp;lt;60y for any given AHI cut-off. Conclusion These data suggest that AHI cut-offs warranting treatment of OSA should be adjusted based on age and prevalent clinical disorders, consistent with the concept of personalized medicine. These findings are also consistent with the notion that OSA in older adults is a distinctly different phenotype than in young and middle-aged adults. Support (If Any)

Recruiting "clean" chronic insomnia participants: the unicorn of sleep research.

Recruiting "clean" chronic insomnia participants: the unicorn of sleep research.

Diagnostic performance of screening tools for the detection of obstructive sleep apnea in people living with HIV.

Many people living with human immunodeficiency virus (PLWH) have undiagnosed obstructive sleep apnea (OSA), which may contribute to commonly reported fatigue and the high cardiovascular disease burden in this population. Our objective was to assess the utility of traditional OSA screening tools (STOP-BANG, Berlin Questionnaire, and Epworth Sleepiness Scale) for detecting OSA in PLWH. Adult PLWH were recruited from sleep/ human immunodeficiency virus clinics and the community into a larger clinical trial that included completion of these questionnaires before in-laboratory polysomnography. Discriminatory performance of these screening tools was assessed using area under receiver operating characteristic curves (AUC). The reference standard for the primary analysis was OSA based on an apnea-hypopnea index ≥ 5 events/h using recommended "1A"-criteria (hypopnea with 3% desaturation and/or arousal). Secondary analyses explored acceptable "1B"-criteria (hypopnea with 4% desaturation) and/or higher apnea-hypopnea index cut-offs (≥ 15 events/h). 120 PLWH were included (mean age: 50 ± 11 years; body mass index: 27 ± 4 kg/m2, 84% male) and OSA was diagnosed in 75% using 1A-criteria. In the primary analysis, the discriminatory performance of the 3 screening tools was low (AUCs 0.58 to 0.70) and similar across the tools (P ≥ .14). In secondary analyses, STOP-BANG showed moderate-high discriminatory ability (AUCs 0.77-0.80) and performed significantly better (P ≤ .008) than the Berlin Questionnaire or Epworth Sleepiness Scale (AUCs 0.53-0.62). OSA was highly prevalent in our cohort of PLWH. Although STOP-BANG could reasonably identify moderate-severe OSA, the tools were not reliable for mild disease. Specifically, the questionnaires perform poorly for PLWH with mild OSA manifesting with arousals, yet such people may be at risk of fatigue/sleepiness and impaired memory consolidation. Registry: ClinicalTrials.gov; Title: Obstructive Sleep Apnea Endotypes and Impact on Phenotypes of People Living with HIV (PLWH/OSA); Identifier: NCT03575143; URL: https://clinicaltrials.gov/ct2/show/NCT03575143. Schmickl CN, Bosompra N-O, DeYoung PN, etal. Diagnostic performance of screening tools for the detection of obstructive sleep apnea in people living with HIV. J Clin Sleep Med. 2022;18(7):1797-1804.

Respiratory Event Detection During Sleep Using Electrocardiogram and Respiratory Related Signals: Using Polysomnogram and Patch-Type Wearable Device Data.

This paper presents an automatic algorithm for the detection of respiratory events in patients using electrocardiogram (ECG) and respiratory signals. The proposed method was developed using data of polysomnogram (PSG) and those recorded from a patch-type device. In total, data of 1,285 subjects were used for algorithm development and evaluation. The proposed method involved respiratory event detection and apnea-hypopnea index (AHI) estimation. Handcrafted features from the ECG and respiratory signals were applied to machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, random forest, multi-layer perceptron, and the support vector machine (SVM). High performance was demonstrated when using SVM, where the overall accuracy achieved was 83% and the Cohen's kappa was 0.53 for the minute-by-minute respiratory event detection. The correlation coefficient between the reference AHI obtained using the PSG and estimated AHI as per the proposed method was 0.87. Furthermore, patient classification based on an AHI cutoff of 15 showed an accuracy of 87% and a Cohen's kappa of 0.72. The proposed method increases performance result, as it records the ECG and respiratory signals simultaneously. Overall, it can be used to lower the development cost of commercial software owing to the use of open datasets.

Performance comparison of peripheral arterial tonometry-based testing and polysomnography to diagnose obstructive sleep apnea in military personnel.

Obstructive sleep apnea (OSA) is increasingly common among military personnel, but diagnostic capabilities are challenged by limited capability for polysomnography (PSG). We sought to evaluate the diagnostic properties of peripheral arterial tonometry (PAT)-based home sleep apnea testing (HSAT) to accurately identify and classify OSA among active-duty military personnel. This study was a retrospective review of all patients suspected of having OSA who completed an initial PAT-based HSAT followed by confirmatory PSG within 120 days. The diagnostic properties of a PAT-based, HSAT-derived apnea-hypopnea index (AHI) vs a PSG-derived AHI were assessed. Two hundred eight matched pairs of asynchronous studies were analyzed. The prevalence of OSA was 63.5%. PAT-based HSAT overdiagnosed 27.4% of patients with OSA and underdiagnosed 46.6% of patients with OSA. The majority (n = 116, 55.8%) of patients changed OSA severity classification (absent, mild, moderate, severe) after PSG. OSA severity classification concordance between PAT-based HSAT and PSG was observed in 53.4%, 40.5%, 28.6%, and 40.0% of patients with absent, mild, moderate, and severe OSA, respectively. Receiver operating characteristic curve analysis showed an area under the curve of 0.715 and a proposed PAT-based, HSAT-derived AHI cutoff score for OSA diagnosis of 9.0 events/h. This PAT-based, HSAT-derived AHI provided a 52% sensitivity, 83% specificity, 84% positive predictive value, and 50% negative predictive value. Bland-Altman plots showed an unacceptable degree of agreement between PAT-based, HSAT-derived AHI and AHI. There is significant discordance between PAT-based HSAT and PSG among active-duty military personnel evaluated for OSA. PAT-based HSAT may have limited utility for diagnosing OSA and grading severity in this unique patient population. O'Reilly BM, Wang Q, Collen J, Matsangas P, Colombo CJ, Mysliwiec V. Performance comparison of peripheral arterial tonometry-based testing and polysomnography to diagnose obstructive sleep apnea in military personnel. J Clin Sleep Med. 2022;18(6):1523-1530.