Sleep Apnea Screening Research Articles

Society of Anesthesia and Sleep Medicine and the Society for Obstetric Anesthesia and Perinatology Consensus Guideline on the Screening, Diagnosis, and Treatment of Obstructive Sleep Apnea in Pregnancy.

The Society of Anesthesia and Sleep Medicine and the Society for Obstetric Anesthesia and Perinatology tasked an expert group to review existing evidence and to generate recommendations on the screening, diagnosis, and treatment of patients with obstructive sleep apnea during pregnancy. These recommendations are based on a systematic review of the available scientific evidence and expert opinion when scientific evidence is lacking. This guideline may not be appropriate for all clinical situations and patients, and physicians must decide whether these recommendations are appropriate for their patients on an individual basis. We recognize that not all pregnant people may identify as women. However, data on non-cisgendered pregnant patients are lacking, and many published studies use gender-binary terms; therefore, depending on the study referenced, we may refer to pregnant individuals as women. This guideline may inform the creation of clinical protocols by individual institutions that consider the unique considerations of their patient populations and the available resources.

Obstructive sleep apnea screening in different age groups: performance of the Berlin, STOP-Bang questionnaires and Epworth Sleepiness Scale

ObjectiveTo evaluate the performance of the Berlin Questionnaire, the STOP-Bang Questionnaire, and the Epworth Sleepiness Scale in screening for the disease in adults of different age groups by comparing them with polysomnography. MethodsCross-sectional study with prospective patient allocation, in which individuals underwent a medical interview, completion of the three screening instruments, and polysomnography. Individuals were categorized into three age groups: 18–39, 40–59, and ≥60 years. The results of the screening instruments were compared to the diagnostic criteria of the International Classification of Sleep Disorders—third edition. Performance was assessed using 2×2 contingency tables, estimating sensitivity, specificity, predictive value, likelihood ratio, and accuracy. Receiver Operating Characteristic curves were also constructed and the area under the curve was estimated for each instrument by age group. ResultsWe obtained a sample with 321 individuals suitable for analysis. The mean age was 50 years, with a predominance of females (56%). The prevalence of the disease in the overall sample was 79%, more prevalent in males in any age group and more frequent in the middle age group. The analyzes revealed that STOP-Bang performed better, both for the overall sample and for all age groups, followed by Berlin Questionnaire and Epworth Sleepiness Scale. ConclusionIn an outpatient setting with individuals with characteristics similar to those in this study, it seems sensible to choose the STOP-Bang as a screening tool for the disease, regardless of age group. Level of evidence according the guide for authorslevel 2.

Gender differences in obstructive sleep apnea with comorbid treatment-resistant depression

BackgroundA bidirectional relationship between major depression and obstructive sleep apnea (OSA) has been established, suggesting the possibility of overlapping and compounding disease processes. Depression, however, while more prevalent in women, is a highly heterogeneous disorder and can be difficult to treat regardless of gender. A common overlapping symptom of depression and OSA is fatigue. Gender differences in OSA symptomatology (and fatigue in particular) are also consistently observed. Here, we investigate OSA in specific relation to treatment-resistant depression.MethodsA cross-sectional exploratory design was used to analyse data from 94 patients with treatment-resistant depression from a subspecialist mood disorders outpatient service who had no previous sleep assessment. Participants completed overnight polysomnography and a battery of rating scales assessing mood, sleep, and daytime functioning. Linear regression models determined whether presence of fatigue in treatment-resistant depression predicted OSA severity.ResultsThere was a high prevalence (79%) of previously undiagnosed OSA in our sample of patients with treatment-resistant depression. Treatment-resistant depression was one factor to close the gap in obstructive sleep apnea prevalence between men and women in this group. Presence of OSA measured objectively by the Apnea Hypopnea Index was not associated with episode state (depressed vs. euthymic). Daytime sleepiness scores as measured by the Epworth Sleepiness Scale indicated higher than normal daytime sleepiness with no difference between genders. Men and women in our study reported similar amounts of fatigue as measured by the Profile of Mood States-Fatigue Subscale, however, daytime fatigue (but not sleepiness) predicted OSA severity in women only.ConclusionsWe argue that typical symptoms of treatment-resistant depression may overshadow key symptoms of undetected OSA. Specifically, we found that daytime fatigue may be one factor masking a potentially significant underlying sleep disorder in women only. Comprehensive assessment and screening for sleep apnea in patients with treatment-resistant depression is encouraged, and the importance of investigating severity of fatigue in this population is emphasized.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening.

Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease. The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance. The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures. The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

SECONDARY HYPERTENSION IN THE INTERNAL MEDICINE OUTPATIENT DEPARTMENT OF A PORTUGUESE HOSPITAL

Objective: Secondary hypertension is an uncommon form of hypertension and universal screening is not beneficial or cost-effective. Consequently, investigation is reserved for selected cases, according to clinical suspicion, and is often referred to a specialist. The aim of this study was to characterize the evaluation of secondary hypertension in the Internal Medicine Outpatient Department of a portuguese hospital. Design and method: This longitudinal retrospective study analysed patients referred to our Internal Medicine Outpatient Department for hypertension evaluation in 2021. Patient records and results were analysed, focusing on indication for evaluation, testing performed and identified causes of secondary hypertension. Results: A total of 147 patients were referred for hypertension evaluation, 7 (4,76%) of which were explicitly referred for secondary hypertension screening. Testing was conducted in 105 patients (71,43%), with basic evaluation (CBC, renal function, thyroid function) in 71 patients (48,30%) and additional testing (such as endocrine, renovascular, obstructive sleep apnoea screening) performed in 34 patients (23,13%). Considering age criteria, resistant hypertension, and other clinical clues, 60 patients (40,82%) had a documented indication for additional testing for secondary hypertension. Additional testing was requested in 5 patients (3,40%) not meeting these criteria, most of whom needed testing to follow-up investigations ordered in primary care without clear indication. Conversely, a small proportion (6,12%) met criteria for additional testing that wasn’t requested: 22% already had a previous screening ordered in primary care negative for secondary hypertension; 44% were evaluated for hypertension-mediated organ damage without further screening. Patients in which specific evaluation was complete at the time of this study, a secondary cause was identified in 9 patients (6.12%), all with a previous diagnosis of Obstructive Sleep Apnea. Conclusions: The prevalence of secondary forms identified didn’t significantly differ from the described 5-10% overall prevalence of secondary hypertension and reiterated the identification of OSA as an exceedingly common recognized cause of hypertension. This study emphasised that extensive testing was mostly non-diagnostic and was associated with inconclusive results leading to further investigations, raising costs and risks. This suggests that careful selection of patients and method of screening by a specialist is beneficial.

1019 New onset OSA after long term Vagus Nerve Stimulator implantation in a patient with Lennox-Gastaut syndrome: A case report

Abstract Introduction Implantable vagus nerve stimulation (VNS) is an established treatment for drug resistant epilepsy. New onset obstructive sleep apnea (OSA) is a rare side effect of VNS. There are few studies describing VNS induced sleep apnea in adults. [1-2] A study of 16 patients with drug-refractory epilepsy found 5 patients with normal AHI develop mild OSA after VNS treatment for 3 months. [2] However, no literature exists regarding OSA after long term VNS implantation. Untreated OSA in patients with epilepsy can impact epilepsy outcomes. We report a case of new onset OSA after long term VNS implantation, refractory to PAP therapy. Report of case(s) A 36 year-old man with Lennox-Gastaut syndrome presented with gasping during sleep and daytime hypersomnolence. He underwent VNS implantation at the age of 12. A prior PSG in 2018 showed no sleep apnea. Repeat PSG showed VNS-induced OSA (AHI 10.1/hour). After failed CPAP titration, BPAP-S at 25/16 cm H2O improved OSA. Review of therapy data on follow up showed AHI of 13.7/hour and CAI of 10.9/hour, which is concerning for treatment induced central sleep apnea on BPAP. Diagnostic study with VNS turned off revealed mild OSA (AHI 5.8/hour). A multidisciplinary approach involving ENT for drug-induced sleep endoscopy to optimize VNS stimulation parameters along with neurology and sleep involvement is planned. The optimized VNS setting could be used during planned sleep times using a VNS device with dual-programming capability. Conclusion To our knowledge, there is no literature on development of sleep apnea after long term VNS implantation. Although the exact mechanism is unknown, VNS can cause sleep apnea or worsen sleep apnea in patients with a preexisting diagnosis. [1] This case demonstrates need for continuous monitoring and screening for sleep apnea in patients who have been implanted with VNS, even if baseline and follow up PGSs are normal. Current literature suggests discontinuation of VNS to resolve VNS-induced OSA, but we describe a case here where VNS is needed for ongoing management of epilepsy. Further studies are needed to identify treatment options for patients that cannot discontinue VNS therapy who also cannot be successfully treated with PAP if they develop VNS-induced OSA. Support (if any)

Clinical utility of smartphone applications for sleep physicians.

To review various smartphone applications (apps) for sleep architecture and screening of obstructive sleep apnea (OSA) and to outline their utility for sleep physicians. Mobile application stores (Google Play and Apple iOS App Store) were searched for sleep analysis applications (apps) that are targeted for consumer use. Apps were identified by two independent investigators for apps published through July 2022. App information including parameters obtained for sleep analysis were extracted from each app. The search identified 50 apps that reported sufficient outcome measures to be considered for assessment. Half of the apps tracked sleep with phone-only technology, while 19 utilized sleep and fitness trackers, three utilized sleep-only wearable devices, and three utilized nearable devices. Seven apps provided data useful for tracking users for signs and symptoms of obstructive sleep apnea. There are a variety of sleep analysis apps available on the market to consumers currently. Though the sleep analysis of these apps may not be validated, sleep physicians should be aware of these apps to improve understanding and education of their patients.

Validation of a transthoracic impendance sensor for screening of sleep apnea in CIED patients: a pooled analysis of AIRLESS and UPGRADE

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Scientific grant support from the Boston Scientific Investigator Sponsored Research (ISR) Committee, Boston Scientific, St. Paul, MN, USA. Background/Introduction Cardiac implantable electronic devices and their integrated thoracic impedance sensors, meant to detect sleep apnea, are available for over a decade now. Despite its usage in daily clinical practice, there is only limited data on its diagnostic accuracy. Purpose To further validate an apnea sensor by using polysomnography (PSG) and investigate subgroups with potential differences in diagnostic accuracy. Methods AIRLESS and UPGRADE were prospective investigator driven trials meant to validate an apnea sensor. Patients, who either fulfilled the criteria for implantation of an implantable cardioverter-defibrillator (ICD), cardiac resynchronization therapy (CRT) or upgrading to CRT according to current guidelines were eligible for enrolment. Sleep apnea and its severity, measured by apnea-hypopnea index (AHI), were exclusively assessed by PSG. PSG was conducted in a time interval of 1 to 3 months post implantation of the device. For direct comparison, the apnea sensor derived respiratory disturbance index (RDI) was used from the identical night. Bivariate correlations (spearman coefficient) as well as the Bland-Altmann method were applied to assess diagnostic accuracy of the apnea sensor. Results Overall, 80 patients were analysed, out of whom the majority (n = 35, 43.8%) were diagnosed with central sleep apnea. Sixteen (20.0%) patients received a conventional ICD system, whereas 21 (26.3%) were implanted a CRT-P and 43 (53.8%) a CRT-D. Atrial fibrillation was known in 50.0% (n=40) of patients, 45 patients suffered from periodic leg movement syndrome (PLMS, 56.3%). In the overall cohort, the sensor derived RDI correlated significantly with the AHI (r = 0.61, p < 0.001) with a mean difference (MD) of -12.6 (95% CI -38.2 – 13.0). The sensor did specifically perform well in patients without atrial fibrillation (r = 0.80, p < 0.001; MD -9.0, 95% CI -30.4 – 12.5), while the presence of PLMS did not affect the accuracy of measurements (PLMS: r = 0.60, p = 0.007, MD -16.1, 95% CI -43.9 – 11.8 vs. no PLMS: r = 0.62, p = 0.001, MD -9.9, 95% CI -32.8 – 12.9). Furthermore, the RDI was found to correlate well in patients with central sleep apnea (r = 0.61, p = 0.013), although similar overestimation was observed (MD -11.6, 95% CI -35.4 – 12.2) as in other subgroups. Conclusion In an exclusive heart failure cohort, the apnea sensor did correlate well with the PSG derived AHI. Although similar correlation is found in most subgroups, there remains to be a decent overestimation of events.

Characterization of sleep in emerging adults with cystic fibrosis on elexacaftor/tezacaftor/ivacaftor

Characterization of sleep in emerging adults with cystic fibrosis on elexacaftor/tezacaftor/ivacaftor

Contactless screening for sleep apnea with breathing vibration signals based on modified U-Net

BackgroundObstructive sleep apnea (OSA) is a chronic sleep disorder characterized by frequent cessations or reductions of breathing during sleep. Polysomnography (PSG) is a definitive diagnostic tool for OSA. The costly and obtrusive nature of PSG and poor access to sleep clinics have created a demand for accurate home-based screening devices. MethodsThis paper proposes a novel OSA screening method based solely on breathing vibration signals with a modified U-Net, allowing patients to be tested at home. Sleep recordings over a whole night are collected in a contactless manner, and sleep apnea-hypopnea events are labeled by a deep neural network. The apnea-hypopnea index (AHI) calculated from events estimation is then used to screen for the apnea. The performance of the model is tested by event-based analysis and comparing the estimated AHI with the manually obtained values. ResultsThe accuracy and sensitivity of sleep apnea events detection are 97.5% and 76.4%, respectively. The mean absolute error of AHI estimation for the patients is 3.0 events/hour. The correlation between the ground truth AHI and predicted AHI shows an R2 of 0.95. In addition, 88.9% of all participants are classified into correct AHI categories. ConclusionsThe proposed scheme has great potential as a simple screening tool for sleep apnea. It can accurately detect potential OSA and help the patients to be referred for differential diagnosis of home sleep apnea test (HSAT) or polysomnographic evaluation.

Sleep disorders in patients with overweight and acute and exacerbated chronic heart failure

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): Medical University - Sofia Background Often accompanying comorbidity in patients with acute heart failure and overweight is sleep disorders. Searching and treatment of sleep apnea will be helpful in these patients. CPAP therapy may improve the prognosis. Purpose To determine the frequency and the phenotypic characteristics and of sleep apnea in patients with overweight and exacerbated heart failure. To add continuous positive airway pressure (CPAP) therapy in patients with obstructive sleep apnea (OSA) and monitor changes in systolic and diastolic function. To assess the survival rate in CPAP therapy group and the control group. Methods 100 consecutive patients hospitalized for exacerbation heart failure in the cardiology department. After the selection, 61 patients meet inclusion criteria – Apnea-Hypopnea index (AHI) > 5, Epworth Sleepiness Scale (ESS) > 6, NTproBNP>900 pg/ml, and Body mass index(BMI) >25. All patients receiving optimal medical treatment. The follow-up period was 2 years. The primary endpoint was death for any reason. Sleep apnea screening was performed with ApneaLinkTM. Echocardiographic assessment of left ventricular ejection fraction (LVEF) and the E/e‘m ratio. Results From 61 with sleep disorders 82% (n=50) has OSA and 18% (n=11) has central sleep apnea (CSA). Detected significant changes in LVEF between the OSA group and CSA group (EF% 49.6±8.5vs41.8±11.4;p=0.013). Also statistical significant changes there was about E/e’m ratio and BMI (BMI-38.5±7.1vs31.9±4.5;p=0.005 and E/e’m-17.1±3.7vs20.9±2.5;p=0.002)(Tab. 1). We found a strong reverse correlation between the LVEF and the number of central sleep apnea events (r=-0,52;p<0,001). There was a strong correlation between BMI and ESS (r=0,649;p<0,001). In the OSA group, CPAP therapy was started in 13 patients, the rest of the group (n=37) continue on optimal medication therapy. At the end of the follow-up period in statistical analysis were included 11 patients from the CPAP group and 20 patients from the control group. There was improvement about LVEF, ESS and BMI in CPAP group at end of follow-up (EF%-start: 46,82±9,61vs end of study:49,45±8,23;p=0,019, ESS–start 13±2,6vs end of study 5,9 ±1,5;p<0,001, BMI–start: 39,9±5,6vs end of study: 35,9±4,4;p=0,001). There were no significant changes in E/e'm ratio (E/e'm:start 16,3±3,2vs end of study:15±5,1;p=0,327). In the control group, there were no significant changes. Kaplan-Meier analysis confirmed that the CPAP group has a better survival rate than the control group in the follow-up period (Log-Rank p=0.049). Conclusions Obstructive sleep apnea was more common in obese heart failure patients. The left ventricular systolic function is lower in patients with central sleep apnea. Additional CPAP treatment can improved ejection fraction in patients with heart failure and obstructive sleep apnea. There is a positive effect on BMI. Base on ESS, subjective daytime sleepiness was improved. CPAP therapy can improve the survival rate.

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.

Convolutional neural network for screening of obstructive sleep apnea using snoring sounds

Screening and assessment for obstructive sleep apnea hypopnea syndrome (OSAHS) has attracted growing attention to improve the life of patients with sleep apnea. The gold standard for diagnosing OSAHS is an overnight polysomnography (PSG) in a dedicated sleep laboratory. Yet, the PSG test is professional, expensive and unsuitable for mass screening of the population. OSAH affects about 10 % of population in the world, and there are about 80 % suffers remaining undiagnosed. The automatic and cheap OSAHS patients screening methods are urgently needed. As a major sign of undiagnosed OSAHS, snoring has been used for diagnosis of OSAHS. An automated snore detection method would allow a faster diagnosis and more patients to be analyzed. In this paper, we build a database including more than 80 thousand of snoring sound episodes from 124 subjects. These sounds are recorded by non-contact microphone in the subject’s private room, and labeled by trained sleep clinicians. And then the visibility graph method is modified as a novel framework for encoding these snoring time series into images to fully take advantage of two-dimension convolutional neural networks (CNN) in computer vision tasks. At last, a CNN model based on visibility graph method (VG) is applied to automatically extract features and recognize severity of OSAHS based on these snoring sounds. The experimental results show that our approach achieves an accuracy of 92.5 %, sensitivity of 93.9 %, and specificity of 91.2 % for OSAHS recognition. Our research provides an alternative method for rapid and massive screening and diagnosis of OSAHS.

Examining changes in knowledge and practice in nurse practitioners after an online educational module on obstructive sleep apnea

AimsThe purpose of this study is to investigate primary care nurse practitioner (NP) knowledge and knowledge retention on obstructive sleep apnea (OSA) screening after an educational in-service. BackgroundThe prevalence of OSA is high and continues to rise amid the obesity epidemic. Approximately 75–90 % of individuals with moderate to severe OSA remain undiagnosed. Continuing education of OSA risk factors among primary care providers may increase screening rates, leading to early diagnosis and treatment. MethodsAn educational module was presented to NPs (n = 30) during a mandatory NP in-service at two sets of outpatient clinics. Knowledge was assessed with a pre-test and post-test survey consisting of 23 items. A 25-item follow-up test was administered five weeks after to assess knowledge retention. ResultsAn increase in total knowledge scores was demonstrated between the pre-test and post-test with a decrease in knowledge observed at follow-up. Follow-up test total mean scores remained above pre-test levels, indicating potential long-term learning. ConclusionsWhile learning was demonstrated, NPs acknowledged continued barriers to screening for OSA such as time and having no OSA screening tool available in the electronic medical record (EMR).

Obstructive sleep apnea screening in children with asthma.

Obstructive sleep apnea is highly prevalent in children with asthma, particularly in obese children. The sleep-related breathing disorder screening questionnaire has low screening accuracy for obstructive sleep apnea in children with asthma. Our goal was to identify the questions on the sleep-related breathing disorder survey associated with obstructive sleep apnea in children with asthma. Participants completed the survey, underwent polysomnography and their body mass index z-score was measured. Participants with survey scores above 0.33 were considered high risk for obstructive sleep apnea and those with an apnea-hypopnea index ≥ 2 events/h classified as having obstructive sleep apnea. Logistic regression was used to examine the association of each survey question and obstructive sleep apnea. Positive and negative predictive values were calculated to estimate screening accuracy. The prevalence of obstructive sleep apnea was 40% in our sample (n = 136). Loud snoring, morning dry mouth, and being overweight were the survey questions associated with obstructive sleep apnea. The composite survey score obtained from all 22 questions had positive and negative predictive values of 51.0% and 65.5%, while the combined model of loud snoring, morning dry mouth, and being overweight had positive and negative predictive values of 60.3% and 77.6%. On the other hand, the body mass index z-score alone had positive and negative predictive values of 76.3% and 72.2%. The body mass index z-score is useful for obstructive sleep apnea screening in children with asthma and should be applied routinely given its simplicity and concerns that obstructive sleep apnea may contribute to asthma morbidity.

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.

Prevalence of sleep apnoea in patients with type 1 diabetes and its association with comorbidities and diabetic complications: A French nationwide prospective study.

To investigate sleep apnoea prevalence, factors influencing severity, and associations between sleep apnoea severity and micro-/macrovascular complications in a large population of patients with type 1 diabetes. This French multicentre prospective cohort study was conducted between July 2016 and June 2020. Adults with type 1 diabetes using an insulin pump were eligible. Home care provider nurses collected demographic and clinical data and set up oximetry to determine the oxygen desaturation index (ODI). No, mild-moderate and severe sleep apnoea were defined as ODI <15 events/h, 15 to <30 events/h and ≥30 events/h, respectively. Univariate and multivariate analyses were performed to identify factors associated with sleep apnoea, and associations between sleep apnoea severity and micro-/macrovascular complications were determined using logistic regression. Of 769 participants, 12.4% and 3.4% had mild-to-moderate or severe sleep apnoea, respectively. Factors significantly associated with sleep apnoea on multivariate analysis were age, sex, body mass index (BMI) and hypertension. After adjustment for age, sex and BMI, presence of severe sleep apnoea was significantly associated with macrovascular complications (odds ratio vs. no sleep apnoea: 3.96 [95% confidence interval 1.43-11.11]; P< 0.01), while mild-to-moderate sleep apnoea was significantly associated with presence of diabetic retinopathy (odds ratio 2.09 [95% confidence interval 1.10-3.74]; P< 0.01). Sleep apnoea is a significant comorbidity in patients with type 1 diabetes, especially with respect to diabetic complications. This highlights the need for sleep apnoea screening and management in these individuals.

Establishment and Application Evaluation of an Improved Obstructive Sleep Apnea Screening Questionnaire for Chinese Community: The CNCQ-OSA.

Obstructive sleep apnea (OSA) is a common sleep-disordered breathing disease. We aimed to establish an improved screening questionnaire without physical examinations for OSA named the CNCQ-OSA (Chinese community questionnaire for OSA). A total of 2585 participants who visited sleep medicine center and underwent overnight polysomnography were grouped into two independent cohorts: derivation (n = 2180) and validation (n = 405). The CNCQ-OSA was designed according to the baseline of patients in derivation cohort. We comprehensively analyzed the data to evaluate the predictive value of the CNCQ-OSA, compared to the GOAL questionnaire, STOP-Bang questionnaire (SBQ) and NoSAS questionnaire. The CNCQ-OSA included seven variables: loud snoring, BMI ≥ 25 kg/m2, male gender, apnea, sleepiness, hypertension and age ≥30, with a total score ranging from 7 to 16.7 points (≥13.5 points indicating high risk of OSA, ≥14.5 points indicating extremely high risk). In the derivation and validation cohorts, the areas under the curve of the CNCQ-OSA were 0.761 and 0.767, respectively. In the validation cohort, the sensitivity and specificity of a CNCQ-OSA score ≥13.5 points for the apnea-hypopnea index (AHI) ≥5/h were 0.821 and 0.559, respectively (Youden index, 0.380), and the score ≥14.5 points were 0.494 and 0.887, respectively (Youden index, 0.375). The CNCQ-OSA had a better predictive value for AHI ≥ 5/h, AHI > 15/h and AHI > 30/h, with the highest Youden index, compared to the other questionnaires. The CNCQ-OSA can effectively identify the risk of OSA, which is appropriate for self-screening at home without physical examinations.

Sleep apnea in patients with exacerbated heart failure and overweight

Sleep disorders are a common concomitant comorbidity in patients with heart failure. The aims of our study are to determine the incidence and phenotypic characteristics of sleep apnea in overweight patients with exacerbated heart failure and to assess the degree of involvement of systolic and diastolic function impairment in the individual group. From 100 screened patients with heart failure in our department from 2015 to 2017, 61 met the inclusion criteria and participated in the study. 82% (n = 50) of the patients had obstructive sleep apnea (OSA), and 18% (n = 11) had central sleep apnea (CSA). The CSA group had a significantly lower left ventricular ejection fraction (LVEF) than the OSA group (EF% 49.6 ± 8.5 vs 41.8 ± 11.4; p = 0.013). A negative correlation was found between LVEF and the number of central apnea events (r = −0.52; p < 0.001). More frequent hospitalizations for heart failure (HF) and higher mortality rate were found in the CSA group. Screening for sleep apnea in patients with exacerbated heart failure and obesity is necessary for the complex treatment of these patients.

Safety incidents and obstructive sleep apnoea in railway workers.

Current evidence is lacking to justify more stringent screening for obstructive sleep apnoea (OSA) in the rail industry. Population-based studies indicate a complex association between body mass index, age, apnoea-hypopnoea index and vehicle crash risk. To study the association between OSA severity and the occurrence of safety incidents in safety-critical rail workers with a confirmed diagnosis of OSA, and to identify if OSA treatment is associated with the occurrence of fewer safety incidents. A retrospective medical file audit was conducted on railway workers attending health assessments between 2016 and 2018, who met the criteria to be referred for sleep studies. The association between OSA severity, treatment and the number of incidents in the 3 years before their health assessment appointment was explored using a Poisson log-linear regression. A substantial proportion (44%, 274/630) of railway workers with confirmed OSA had at least one incident in the 3 years prior to appointment. The odds of an incident were significantly reduced in those with moderate OSA compared with severe OSA (odds ratio [OR] 0.79, 95% confidence interval [CI] 0.64-0.98), but not those with mild OSA (OR 0.97, 95% CI 0.76-1.24). There was a statistically significant relationship between combined OSA severity and treatment status, where the likelihood of an incident increased in those with severe untreated OSA compared with those receiving treatment (OR 1.75, 95% CI 1.16-2.64). Our results suggest that more stringent screening to identify severe OSA early, and close surveillance of treatment of those with severe OSA, should be considered.