NoSAS Score Research Articles

Utility of the psychomotor vigilance task in screening for obstructive sleep apnoea.

The study aimed to assess the performance of the PVT in patients with suspected OSA, evaluate its role in population screening for OSA. The NoSAS, STOP-Bang, ESS scores and PVT tests were performed after suspected OSA patients' admission, followed by PSG. Then we compared the PVT results, calculated the sensitivity, specificity and ROC curve of PVT, and analyzed the accuracy of STOP-Bang and NoSAS questionnaire combined with PVT in predicting OSA. A total of 308 patients were divided into four groups based on AHI: primary snoring (2.74 ± 1.4 events/h, n = 37); mild OSA (9.96 ± 3.25 events/h, n = 65); moderate OSA (22.41 ± 4.48 events/h, n = 76); and, severe OSA (59.42 ± 18.37 events/h, n = 130). There were significant differences in PVT lapses (p < 0.001) and reaction time (RT, p = 0.03) among the four groups. The PVT lapses and RT were positively correlated with AHI (p < 0.001) and ODI (p < 0.001), and negatively correlated with LSpO2 (p < 0.001). When diagnosing OSA (AHI ≥ 5 events/h), the AUCs of PVT, ESS, STOP-Bang, and NoSAS were 0.679, 0.579, 0.727, and 0.653, respectively; the AUCs of STOP-Bang and NoSAS combined with PVT increased. After combined PVT, the diagnostic specificity of STOP-Bang and NoSAS at nodes with AHI ≥ 5, ≥ 15 and ≥ 30 events/h increased to varying degrees. Patients with OSA exhibited impairment in the PVT, and the combination of the PVT and STOP-Bang or NoSAS scores can improve the diagnostic efficacy and specificity for OSA.

Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.

The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combination of simple clinical information and imaging recognition based on facial photos may be a useful toolto screen for OSA. We recruited consecutivesubjects suspected of OSA who had received sleep examination and photographing. Sixty-eight points from 2-dimensional facial photos were labelled by automated identification. An optimized model with facial features and basic clinical information was established and tenfold cross-validation was performed. Area under the receiver operating characteristic curve (AUC) indicated the model's performance using sleep monitoring as the reference standard. A total of 653 subjects (77.2% males, 55.3% OSA) were analyzed. CATBOOST was the most suitable algorithm for OSA classification with a sensitivity, specificity, accuracy, and AUC of 0.75, 0.66, 0.71, and 0.76 respectively (P < 0.05), which was better than STOP-Bang questionnaire, NoSAS scores, and Epworth scale. Witnessed apnea by sleep partner was the most powerful variable, followed by body mass index, neck circumference, facial parameters, and hypertension. The model's performance became more robust with a sensitivity of 0.94, for patients with frequent supine sleep apnea. Thefindings suggest that craniofacial features extracted from 2-dimensional frontal photos, especially in the mandibular segment, have the potential to become predictors of OSA in the Chinese population. Machine learning-derived automatic recognition may facilitate the self-help screening for OSA in a quick, radiation-free, and repeatable manner.

Utilization of the GOAL questionnaire as a standardized screening tool for obstructive sleep apnea

The purpose of our study was to evaluate the application value of the GOAL questionnaire in screening obstructive sleep apnea (OSA) and to compare it with the other three questionnaires in sleep clinics. A cross-sectional study was conducted in 436 patients who had undergone nocturnal polysomnography in the sleep unit of the First Hospital of Shanxi Medical University between September 2021 and May 2022, and all patients completed the four questionnaires (GOAL questionnaire, STOP-Bang questionnaire, NoSAS score and No-Apnea score) truthfully, and the patients were divided into 3 groups: AHI ≥ 5 events/h group, AHI ≥ 15 events/h group and AHI ≥ 30 events/h group. The predictive effect of the questionnaire on different AHI cut-off values was calculated, and performance of four questionnaires was assessed by the discriminatory ability. This study ultimately included 410 patients, and there were statistically significant differences in gender, age, BMI, neck circumference, clinical symptoms, hypertension, diabetes, AHI, and minimum oxygen saturation between OSA and non-OSA groups (P < 0.05). The AUC for No-Apnea score was 0.79, the AUC for STOP-Bang questionnaire was 0.86, the AUC for NoSAS score was 0.81, and the AUC for GOAL questionnaire was 0.77. These four questionnaires were effective in screening OSA when AHI ≥ 15 events/h. Similar to No-Apnea score, STOP-Bang score and NoSAS score, GOAL questionnaire has a good predictive value for OSA, which is a questionnaire suitable for primary health-care centers and clinics.

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.

Incorporating neck circumference or neck-to-height ratio into the GOAL questionnaire to better detect and describe obstructive sleep apnea with application to clinical decisions.

ObjectiveAlthough neck circumference (NC) and neck-to-height ratio (NHR) have been recognized as effective predictors of the clinical diagnosis of adult obstructive sleep apnea (OSA), they have not been included in the widely used GOAL questionnaire. Not coincidentally, the NHR has not been adequately considered in the development and validation of the STOP-Bang questionnaire, No-Apnea score and the NoSAS score. The motivation for the study was (1) to combine the GOAL questionnaire with the NC and NHR, respectively, to evaluate its predictive performance and (2) to compare it with the STOP-Bang questionnaire, the No-Apnea score, the NOSAS score, and the GOAL questionnaire.Materials and methodsThis retrospectively allocated cross-sectional study was conducted from November 2017 to March 2022 in adults who underwent nocturnal polysomnography (PSG) or home sleep apnea testing (HSAT). In this paper, the GOAL questionnaire was combined with the NC and NHR, respectively, using logistic regression. The performance of the six screening tools was assessed by discriminatory ability [area under the curve (AUC) obtained from receiver operating characteristic (ROC) curves] and a 2 × 2 league table [including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR−)] and compared under AHI ≥5/h, AHI ≥15/h, and AHI ≥30/h conditions.ResultsA total of 288 patients were enrolled in the study. For all severity OSA levels, the sensitivity of GOAL+NC ranged from 70.12 to 70.80%, and specificity ranged from 86.49 to 76.16%. The sensitivity of GOAL+NHR ranged from 73.31 to 81.75%, while specificity ranged from 83.78 to 70.86%. As for area under the curve (AUC) value under ROC curve, when AHI ≥5/h, compared with GOAL (0.806), No-Apnea (0.823), NoSAS (0.817), and GOAL+NC (0.815), GOAL+NHR (0.831) obtained the highest AUC value, but lower than STOP-Bang (0.837).ConclusionThe predictive power of incorporating NC or NHR into the GOAL questionnaire was significantly better than that of the GOAL itself. Furthermore, GOAL+NHR was superior to GOAL+NC in predicting OSA severity and better than the No-Apnea score and the NoSAS score.

An effective model for screening moderate-to-severe obstructive sleep apnea based on the STOP-BANG questionnaire.

BackgroundThis study aimed to develop a more effective screening model for moderate-to-severe obstructive sleep apnea (OSA) based on the best tool among Epworth Sleepiness Scale (ESS), NoSAS score and STOP-BANG questionnaire (SBQ).MethodsThis study screened 2,031 consecutive subjects referred with suspected OSA from 2012 to 2016, including the test cohort from 2012 to 2014 and the validation cohort from 2014 to 2016. Anthropometric measurements, polysomnographic data, ESS, NoSAS scores and SBQ scores were recorded. Receiver operating characteristic curve analyses were performed and the final predictive models were verified in a validation cohort.ResultsA total of 1,840 adults were finally included. The performance of ESS, NoSAS score and SBQ in screening OSA was compared. The diagnostic accuracy of SBQ was superior to ESS and NoSAS. A predictive model based on SBQ yielded an area under the curve (AUC) of 0.931 (95% CI: 0.915–0.946), and the sensitivity and specificity were 84.47 (95% CI: 81.4–87.2) and 87.36 (95% CI: 83.9–90.3) respectively. In the validation cohort, the AUC was 0.955 (95% CI: 0.938–0.969), with a sensitivity and specificity of 86.79 (95% CI: 83.2–89.9) and 90.88 (95% CI: 87.2–93.8) respectively. In addition, the model performed moderately in screening mild OSA with the AUC being 0.771 (95% CI: 0.721–0.815).ConclusionsThe SBQ was effective in screening moderate-to-severe OSA. And a SBQ -based predictive model afforded excellent diagnostic efficacy, which could be applied in clinical practice.

The prevalence of obstructive sleep apnea in sarcoidosis and its impact on sleepiness, fatigue, and sleep-associated quality of life: a cross-sectional study with matched controls (the OSASA study).

Patients with sarcoidosis experience fatigue and excessive daytime sleepiness (EDS). However, the underlying pathomechanism is unclear. Studies suggested undiagnosed obstructive sleep apnea (OSA) to be an important contributor, but reliable data on prevalence and impact of OSA in sarcoidosis are scarce. 71 adult patients with sarcoidosis, 1-to-1 matched to 71 adult controls according to sex, age, and body mass index were included. Participants underwent structured interviews (including Epworth Sleepiness Scale [ESS], Fatigue Assessment Scale [FAS], and Functional Outcome of Sleep Questionnaire [FOSQ-30]) and level-3 respiratory polygraphy. OSA was defined as apnea-hypopnea index ≥ 5 events/h. Prevalence of OSA was assessed and possible risk factors for OSA in sarcoidosis were investigated. Mild OSA (AHI ≥ 5 events/h) was prevalent in 32 (45%) sarcoidosis patients vs 22 (31%) controls (P = .040). Sarcoidosis patients presented higher ESS compared with matched controls (P = .037). FAS scores (median [quartile] of 21.5 [16, 27.5]) indicated fatigue in sarcoidosis patients. Patients with EDS (ESS ≥ 11) presented reduced FOSQ-30 results (median [quartile] of 16.7 [15.2, 17.8]). ESS, FAS, and FOSQ were not associated with AHI in sarcoidosis patients. Body mass index, sex, neck circumference, and NoSAS score were predictors for OSA in sarcoidosis. The risk for mild OSA is 2.5-fold higher in sarcoidosis patients compared with matched controls. OSA seems not to be the reason for increased sleepiness or fatigue in sarcoidosis. Risk factors such as body mass index, sex, neck circumference, and NoSAS score can be used to screen for OSA in sarcoidosis patients. Registry: ClinicalTrials.gov; Name: Obstructive Sleep Apnoea in Sarcoidosis (OSASA); URL: https://clinicaltrials.gov/ct2/history/NCT04156789?V_2=View; Identifier: NCT04156789. Roeder M, Sievi NA, Schneider A, etal. The prevalence of obstructive sleep apnea in sarcoidosis and its impact on sleepiness, fatigue, and sleep-associated quality of life: a cross-sectional study with matched controls (the OSASA study). J Clin Sleep Med. 2022;18(10):2415-2422.

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.

Prediction models and morbidities associated to obstructive sleep apnea: An updated systematic review

Objective: We aim to present a systematic review using a literature update on the performance of clinical questionnaires in predicting the diagnosis of obstructive sleep apnea syndrome compared with polysomnography considered as the reference test to confirm obstructive sleep apnea syndrome. Source of evidence: Medline, the Cochrane Database of Systematic Reviews, Scopus, and ScienceDirect. Methods: The article selection stage implemented the PRISMA diagram. Results: The selected studies state that the Berlin questionnaire, the Epworth sleepiness scale, the STOP questionnaire, the STOP-BANG questionnaire, the ASA checklist, and the No-SAS score are the best performing screening tools to predict obstructive sleep apnea syndrome, as their performance was evaluated for different thresholds of apnea hypopnea index obtained using polysomnography. Conclusion: The DES-OSA 50 and the No-SAS score are simples, effectives, and easy-to-implement scores to identify individuals at risk of sleep-disordered breathing; it can help clinicians decide which patients should be referred for polysomnography.

Rotating shift workers with vitamin D deficiency have a higher risk of obstructive sleep apnea.

The study aimed to evaluate the association between obstructive sleep apnea (OSA) and vitamin D deficiency (VDD) in shift workers. This cross-sectional study included male rotating shift workers in an iron ore extraction company. Participants were classified as VDD when 25(OH)D < 20ng/mL for a healthy population and 25(OH)D < 30ng/mL for groups at risk for VDD. Risk of developing OSA wasclassified by Berlin questionnaire (BQ) and NoSAS score. Data were compared using chi-square analysis with Cramer's V as effect size, and Bonferroni correction. Multivariate logistic regression analysis was performed to investigate whether or notVDD was associated with OSA risk assessment. Among 1423 male workers, mostly younger, aged 30 to 39years (53%), worked shifts for more than 5years (76%). The prevalence of high risk of OSA by BQ was 16%, and 33% by NoSAS score. Additionally, 29% had VDD. In multivariate analysis, controlled for confounding factors, workers with VDD had a 52% increased chance of OSA by BQ (OR 1.52; CI95% 1.06-2.18) and a 64% increased chance of OSA by NoSAS score (OR 1.64; CI95% 1.09-2.48). After subgroup analyses, similar results were not observed in workers aged 20-29 and 30-39years. Rotating shift workers with vitamin D deficiency are more likely to have obstructive sleep apnea, assessed by the Berlin questionnaire and NoSAS score.

Risk of obstructive sleep apnea among taxi-motorbike drivers in Parakou city in West Africa and associated factors with road traffic accidents.

ObjectivesObstructive sleep apnea (OSA) is an underdiagnosed chronic respiratory disease, causing excessive daytime sleepiness (EDS) and road traffic accidents (RTA). This study aimed to determine the risk of OSA among taxi–motorbike drivers (TMD) and factors associated with RTA in Parakou, Benin.MethodsA cross-sectional comparative study was carried out between July and September 2020, including 225 TMD and 450 motorbike drivers other than TMD (non-TMD). A multiple logistic regression was then performed to determine factors associated with RTA.ResultsThe mean age of participants was 38.2 ± 10.2 and 36.6 ±10.9 years, respectively, for TMD and non-TMD (p = .048). The average daily working time was 10.7 ± 2.3H and 9.1±3.5H, respectively, for TMD and non-TMD (p < .001). Average sleeping time was comparable in both groups (7.5±1.4H vs 7.4±1.4H; p = .415). TMD significantly more complained of non-restorative sleep (38.7% vs 18.4%; p < .001) but less of EDS (20.0% vs 28.7%; p = .015). Abdominal obesity predominated in TMD (13.8% vs 4.4%; p < .001). An increased risk of OSA (NoSAS score ≥8) was diagnosed in 25.8% TMD and 26.7% non-TMD (p = .805). Overall, 25.8% of TMD and 18.4% of non-TMD (p = .027) reported at least one RTA in the last 12 months. After adjusted analysis, the unique factor associated with RTA was a daily sleeping pills consumption (aOR=2.2; 95%CI = 1.2–3.8; p = .006).ConclusionThere is need to improve systematic screening and diagnosis of OSA in both TMD and non-TMD and reinforce the regulation and consumption of sleeping pills.

THE NH-OSA SCORE IN PREDICTION OF CLINICALLY SIGNIFICANT OSA AMONG THAI POPULATION: DERIVATION AND VALIDATION STUDIES

TOPIC: Sleep Disorders TYPE: Original Investigations PURPOSE: Obstructive sleep apnea (OSA) is a common condition which leads to morbidity, especially cardiovascular events. Polysomnography (PSG) is considered the gold standard for diagnosis of OSA however has limited availability and high cost. Despite the development of various screening tools, such as the STOP-BANG Score, the Berlin Questionnaire, the Epworth Sleepiness Scale (ESS), and the NoSAS Score, these tools are validated in Caucasian populations and mainly consist of anthropometric obesity index. This differs from the OSA among Asian population which usually, appears in non-obese patients, and are caused by craniofacial abnormalities. We aimed to develop and validate a risk score in prediction of clinically significant OSA among Thai patients. METHODS: The NH-OSA score (NH-OSA), which consists of the follows; having neck circumference of 13 inches (in female) or 15 inches (in male), or greater (total of 1 point), presence of hypertension (total of 4 points), having body mass index; BMI of 23-24.9 kg/m2, 25-30 kg/m2, greater than 30 kg/m2 (Total of 3,5,7 points, respectively), presence of moderate or severe snoring (total of 9 points), and age of 40 years or greater (total of 5 points), were collected from 1,798 adult patients underwent full-night or split-night PSG in King Chulalongkorn Memorial Hospital from year 2017 to 2019. Clinical risk score was developed using multiple factor analysis and logistic regression. Patients at risk of clinically significant OSA were defined by apnea/hypopnea index (AHI) of 15 or greater. Of the enrolled, 450 patients were randomly included in the internal validation study. Moreover, the NH-OSA was externally validated at the sleep center of Bangkok Christian Hospital. RESULTS: With cutoff value at 14 points, the sensitivity, specificity, and positive likelihood ratio of NH-OSA were 82.1%, 68.7% and 2.62 respectively. The AUC was 0.75 (95%CI 0.73 – 0.78). Both internal and external validation study revealed high AUC of 0.74 (0.68-0.80), 0.75 (0.60-0.90), respectively. These were greater when compared to the STOP-BANG Score (AUC 0.68, 95% CI 0.65-0.71, p < 0.0001), the Berlin Questionnaire (AUC 0.59, 95%CI 0.56-0.62, p < 0.0001), ESS score (AUC 0.49, 95% CI 0.46-0.51, p < 0.0001), and NoSAS Score (AUC 0.69, 95% CI 0.67-0.72, p = 0.0001). CONCLUSIONS: NH-OSA has good performance in prediction of clinically significant OSA with high validity among Thai population. It could help screening patients at risks of OSA for further investigation, particularly in the resource limited area. CLINICAL IMPLICATIONS: NH-OSA is a newly developed tool, which could help screening patients at risks of OSA for further investigation DISCLOSURES: no disclosure on file for Nattapong Jaimchariyatam; No relevant relationships by Jeerath Phannajit, source=Web Response No relevant relationships by Supakorn Srichan, source=Web Response No relevant relationships by Somkanya Tungsanga, source=Web Response

Subjective Poor Sleep Quality is Associated with Higher Blood Pressure and Prevalent Hypertension in General Population Independent of Sleep Disordered Breathing.

ObjectiveTo explore the relationship of subjective sleep quality with blood pressure (BP) and hypertension by considering the influence of sleep disordered breathing (SDB) and age in the general population.MethodsWe evaluated sleep quality using the Pittsburgh sleep quality index (PSQI) and SDB using NoSAS score and measured BP in randomly selected adults in China in 2019. Sleep quality is categorized into four groups as very good, fairly good, fairly bad and very bad. SDB is defined as NoSAS score ≥8. Hypertension is defined as systolic BP ≥140 mmHg and/or diastolic BP ≥90 mmHg, and/or use of antihypertensive medicine within previous 2 weeks. Multi-variable linear and logistic regression analyses were used to assess the association of global PSQI score and sleep quality with BP and prevalent hypertension.ResultsIn the 33,341 participants (53.4% women, median age: 48 years), prevalence of hypertension significantly increased from very good to very bad sleepers in total (34.3 vs 42.6 vs 50.3 vs 58.5%), SDB (61.2 vs 68.3 vs 73.3 vs 75.5%) and non-SDB participants (26.8 vs 33.1 vs 40.1 vs 50.9%). In multiple linear regression, PSQI global score showed significant positive association with BP levels in total, SDB and non-SDB participants, consistent in sensitivity analysis by excluding participants who were taking antihypertensives, and in participants aged <60 years in age-stratified analysis. In multivariable logistic regression, odds ratio for presence of hypertension significantly increased from very good to very bad sleepers in total (1 vs 1.08 vs 1.22 vs 1.48), SDB (1 vs 1.17 vs 1.35 vs 1.28) and non-SDB participants (1 vs 1.05 vs 1.14 vs 1.53), consistent in participants aged <60 years.ConclusionPoor subjective sleep quality is significantly associated with higher BP and prevalent hypertension, independent of SDB in the young- and middle-aged general population, indicating potential of improving sleep quality to lower BP and optimize hypertension management at population level.

The value of screening questionnaires/scoring scales for obstructive sleep apnoea in patients with atrial fibrillation

Obstructive sleep apnoea (OSA) is an important modifiable risk factor for atrial fibrillation (AF) but is underdiagnosed in this population. Currently, polysomnography is the gold standard for diagnosing OSA, but is expensive and requires overnight examination. Alternatively, home sleep apnoea testing can be used as a diagnostic tool, but also requires a complete data review. Therefore, these OSA diagnostic modalities are not ideal screening methods. Several OSA screening tools exist, but their value in patients with AF remains unclear. To test the performance of existing screening questionnaires/scales for clinically relevant OSA in patients with AF referred for diagnostic polysomnography. This prospective study compared the performance of seven screening tools (Epworth Sleepiness Scale, Berlin Questionnaire, Sleep Apnea Clinical Score, NoSAS, OSA50, STOP-Bang and MOODS) with polysomnography in the detection of clinically relevant OSA in consecutive patients with AF referred to two sleep clinics. A total of 100 patients referred for polysomnography and known previous AF were included. Polysomnography indicated at least clinically relevant OSA (i.e., apnoea-hypopnoea index≥15 events/hour) in 69% of cases, and 33% had severe OSA (apnoea-hypopnoea index>30 events/hour). In screening for clinically relevant OSA, only the SACS and NoSAS scores had fair areas under the curve (0.704 and 0.712, respectively). None of the seven screening tools was performant enough (i.e., had a fair area under the curve>0.7) in the detection of severe OSA. In this AF cohort referred for polysomnography, clinically relevant OSA was prevalent. None of the selected screening tools showed sufficient performance as a good discriminative screening tool for clinically relevant OSA in patients with AF. Given these findings, other screening modalities for OSA should be considered in the work-up of patients with AF.

A meta-analysis of the diagnostic value of NoSAS in patients with sleep apnea syndrome.

The NoSAS score is a new tool widely used in recent years to screen for obstructive sleep apnea.A number of studies have shown that the NoSAS score is more accurate than previous tools, such as the Berlin, STOP-Bang, and STOP questionnaires.Therefore, this meta-analysis assessed the diagnostic value of the NoSAS score for sleep apnea syndrome in comparison to polysomnography. Two researchers searched the PubMed, EMBASE, Cochrane, and Web of Science databases through November 13, 2020. This paper used Endnote9.3 software to manage the literature and RevMan 5.3 and STATA12.0 software to perform the meta-analysis. A total of 10 studies were included in this meta-analysis, including 14,510 patients. The meta-analysis showed that the pooled sensitivity was 0.798 (95% CI 0.757, 0.833), the pooled specificity was 0.582 (95% CI 0.510, 0.651), the positive likelihood ratio was 1.909 (95% CI 1.652, 2.206), the negative likelihood ratio was 0.347 (95% CI 0.300, 0.403), the diagnostic OR was 5.495 (95% CI 4.348, 6.945), and the area under the SROC curve was 0.77 (95% CI 0.73, 0.80). The NoSAS score has good efficacy in identifying patients likely to have obstructive sleep apnea. The NoSAS score can accurately identify patients likely to have obstructive sleep apnea. Therefore, in the absence of polysomnography, one should use the NoSAS score to evaluate patients with suspected sleep apnea syndrome.

414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

Comparison of NoSAS score with Berlin and STOP-BANG scores for sleep apnea detection in a clinical sample

BackgroundBecause of their high costs and low availability, sleep recordings cannot be used as routine procedures for sleep apnea screening. Therefore, it is important to have a performant screening tool allowing to select patients at higher risk for sleep apnea who need further investigations. The goal of the study is to compare the performances of the three commonly used sleep disordered breathing (SDB) screening questionnaires in a clinical sample. MethodsClinical data and sleep studies performed in consecutive adult patients referred to the Lausanne University sleep center for sleep recordings between November 2016 and February 2018 were analysed. Berlin, STOP-Bang and NoSAS screening scores were calculated and compared with the sleep studies’ results. ResultsNoSAS score showed a NPV of 0.88, a PPV of 0.43 and a correctly classified rate of 71% for an AHI >15/h. STOP-Bang score had a slightly higher negative predictive value (0.92) but a very low positive predictive value (0.30) and a poor correctly classified rate (47%). Berlin score showed globally poor results (NPV:0.85, PPV: 0.29 and a correctly classified rate: 56%). NoSAS score had the highest area under ROC curves (0.78) compared to STOP-bang (0.71) and Berlin (0.62). NoSAS score however yielded a slightly higher rate of false negative than STOP-Bang score. NoSAS false negatives were mainly female snorers with a low neck circumference. ConclusionNoSAS score showed a better discrimination capacity compared to Berlin and STOP Bang scores in an unselected clinical population referred to a sleep center.

Effects of age and sex on the performance of the NoSAS score as a screening tool for obstructive sleep apnea: a hospital-based retrospective study in China.

The NoSAS score has been shown to be a reliable screening tool for obstructive sleep apnea (OSA) in overall populations. This study aimed to explore the effects of age and sex on the predicting performance of this score. A retrospective analysis was conducted on 1119 subjects aged ≥ 18 years and with a total sleep time of ≥ 4 h during overnight polysomnography. Discrimination was assessed by using areas under receiver operating characteristic curve (AUCs), while predictive parameters were calculated by using contingency tables. Overall, a NoSAS score of 8 points or higher resulted in sensitivity, specificity, and AUC for predicting an apnea-hypopnea index (AHI) of ≥ 20 events/h of 74%, 36%, and 0.63 (in non-elderly 73%, 46%, and 0.65; in elderly 91%, 17%, and 0.59; in men 85%, 18%, and 0.56; in women 52%, 76%, and 0.71, respectively). The AUCs at all AHI cutoffs were significantly lower in men than in women (all with p < 0.01), while the AUCs at AHI cutoff of 5, 15, and 30 events/h were significantly lower in elderly than in non-elderly (p < 0.01, 0.05, and 0.05, respectively). In non-elderly, a conventional NoSAS with cutoff of 7 or a modified NoSAS with age cutoff of 50 years provided sensitivity and specificity for predicting an AHI of ≥ 20 events/h of 87%, 37% and 80%, 36%, respectively, with comparable AUCs. In women, a conventional NoSAS with cutoff of 6 or a modified NoSAS with neck circumference cutoff of 35 cm provided sensitivity and specificity for predicting an AHI of ≥ 20 events/h of 85%, 39% and 79%, 52%, respectively, with comparable AUCs. NoSAS score has better discrimination but lower sensitivity for predicting OSA in non-elderly and women than in their counterparts. Age- and sex-specific cutoff values reverse this imbalance. Our results underline the preference of age- and sex-specific cutoff values and the need for better age- and sex-specific screening algorithms.

Oxygen desaturation during flexible bronchoscopy with propofol sedation is associated with sleep apnea: the PROSA-Study

BackgroundObstructive sleep apnea (OSA) is characterized by repetitive episodes of complete or partial obstruction of the upper airways during sleep. Conscious sedation for flexible bronchoscopy (FB) places patients in a sleep-like condition. We hypothesize that oxygen desaturation during flexible bronchoscopy may help to detect undiagnosed sleep apnea.MethodsSingle-centre, investigator-initiated and driven study including consecutive patients undergoing FB for clinical indication. Patients completed the Epworth Sleepiness Scale (ESS), Lausanne NoSAS score, STOP-BANG questionnaire and the Berlin questionnaire and underwent polygraphy within 7 days of FB. FB was performed under conscious sedation with propofol. Oxygen desaturation during bronchoscopy was measured with continuous monitoring of peripheral oxygen saturation with ixTrend (ixellence GmbH, Germany).Results145 patients were included in the study, 62% were male, and the average age was 65.8 ± 1.1 years. The vast majority of patients (n = 131, 90%) proved to fulfill OSA criteria based on polygraphy results: 52/131 patients (40%) had mild sleep apnea, 49/131 patients (37%) moderate sleep apnea and 30/131 patients (23%) severe sleep apnea. Patients with no oxygen desaturation had a significantly lower apnea–hypopnea index than patients with oxygen desaturation during bronchoscopy (AHI 11.94/h vs 21.02/h, p = 0.011). This association remained significant when adjusting for the duration of bronchoscopy and propofol dose (p = 0.023; 95% CI 1.382; 18.243) but did not hold when also adjusting for age and BMI.ConclusionThe severity of sleep apnea was associated to oxygen desaturation during flexible bronchoscopy under conscious sedation. Patients with oxygen desaturation during bronchoscopy might be considered for sleep apnea screening.Trial registration: The Study was approved by the Ethics Committee northwest/central Switzerland, EKNZ (EK 16/13) and was carried out according to the Declaration of Helsinki and Good Clinical Practice guidelines. Due to its observational character, the study did not require registration at a clinical trial registry.

Prediction of obstructive sleep apnea: comparative performance of three screening instruments on the apnea-hypopnea index and the oxygen desaturation index

PurposeTo evaluate the performance of the NoSAS (neck, obesity, snoring, age, sex) score, the STOP-Bang (snoring, tiredness, observed apneas, blood pressure, body mass index, age, neck circumference, gender) questionnaire, and the Epworth sleepiness score (ESS) as a screening tool for obstructive sleep apnea (OSA) severity based on the apnea-hypopnea index (AHI) and the oxygen desaturation index (ODI).MethodsData from 235 patients who were monitored by ambulant polysomnography (PSG) were retrospectively analyzed. OSA severity was classified based on the AHI; similar classification categories were made based on the ODI. Discrimination was assessed by the area under the curve (AUC), while predictive parameters were calculated by four-grid contingency tables.ResultsThe NoSAS score and the STOP-Bang questionnaire were both equally adequate screening tools for the AHI and the ODI with AUC ranging from 0.695 to 0.767 and 0.684 to 0.767, respectively. Both questionnaires perform better when used as a continuous variable. The ESS did not show adequate discrimination for screening for OSA (AUC ranging from 0.450 to 0.525). Male gender, age, and BMI proved to be the strongest individual predictors in this cohort.ConclusionThis is the first study to evaluate the predictive performance of three different screening instruments with respect to both the AHI and the ODI. This is important, due to increasing evidence that the ODI may have a higher reproducibility in the clinical setting. The NoSAS score and the STOP-Bang questionnaire proved to be equally adequate to predict OSA severity based on both the AHI and the ODI.