Prediction of obstructive sleep apnea syndrome in a large Greek population

Relationship between airflow limitation in response to upper airway negative pressure during wakefulness and obstructive sleep apnea severity

PURPOSE

The objective was to determine if alteration in airflow induced by negative pressure (NP) applied to participants' upper airways during wakefulness, is related to obstructive sleep apnea (OSA) severity as determined by the apnea-hypopnea index (AHI).

METHODS

Adults 18 years of age or greater were recruited. All participants underwent overnight polysomnography to assess their apnea-hypopnea index (AHI). While awake, participants were twice exposed, orally, to -3 cm H2O of NP for five full breaths. The ratio of the breathing volumes of the last two breaths during NP exposure to the last two breaths prior to NP exposure was deemed the NP ratio (NPR).

RESULTS

Eighteen participants were enrolled. A strong relationship between the AHI and the exponentially transformed NPR (ExpNPR) for all participants was observed (R2 = 0.55, p < 0.001). A multivariable model using the independent variable ExpNPR, age, body mass index and sex accounted for 81% of variability in AHI (p = 0.0006). A leave-one-subject-out cross-validation analysis revealed that predicted AHI using the multivariable model, and actual AHI from participants' polysomnograms, were strongly related (R2 = 0.72, p < 0.001).

CONCLUSION

We conclude that ExpNPR, was strongly related to the AHI, independently of demographic factors known to be related to the AHI.

Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity

As the incidence of obstructive sleep apnea syndrome (OSAS) increases worldwide, the need for a new screening method that can compensate for the shortcomings of the traditional diagnostic method, polysomnography (PSG), is emerging. In this study, data from 4014 patients were used, and both supervised and unsupervised learning methods were used. Clustering was conducted with hierarchical agglomerative clustering, K-means, bisecting K-means algorithm, Gaussian mixture model, and feature engineering was carried out using both medically researched methods and machine learning techniques. For classification, we used gradient boost-based models such as XGBoost, LightGBM, CatBoost, and Random Forest to predict the severity of OSAS. The developed model showed high performance with 88%, 88%, and 91% of classification accuracy for three thresholds for the severity of OSAS: Apnea-Hypopnea Index (AHI) [Formula: see text] 5, AHI [Formula: see text] 15, and AHI [Formula: see text] 30, respectively. The results of this study demonstrate significant evidence of sufficient potential to utilize machine learning in predicting OSAS severity.

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

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is common in Parkinson disease (PD). Questionnaires can be used as screening tools and have been used as a surrogate definition of OSA in large-scale research. This study aimed to validate the performance of STOP, STOP-BANG, STOP-BAG, STOP-B28, and GOAL and OSA predictors as tools to identify OSA in PD.

METHODS

Data were analyzed from a PD cohort study in which OSA was diagnosed using laboratory polysomnography. We calculated sensitivity and specificity of each questionnaire for OSA using different definitions and performed receiver operating characteristics curve analysis. Linear regression was used to assess adjusted associations between questionnaires and outcomes: Montreal Cognitive Assessment, Epworth Sleepiness Scale, and Movement Disorder Society revision of the Unified Parkinson Disease Rating Scale.

RESULTS

Questionnaire data were available for 68 PD patients (61.8% male, mean age 64.5 [standard deviation 9.9] years, and Hoehn and Yahr score 2.1 [0.8]). OSA (apnea-hypopnea index ≥ 15 events/h) occurred in 69.4% of participants. STOP-B28 ≥ 2 presented a higher sensitivity for OSA than STOP ≥ 2 (0.76 vs 0.65, respectively) and slightly lower specificity (0.65 vs 0.70, respectively). GOAL ≥ 2 had the highest sensitivity but poor specificity. Loud snoring had sensitivity 0.63 and specificity 0.65. STOP and snoring were significantly associated with Montreal Cognitive Assessment, Epworth Sleepiness Scale, and Movement Disorder Society revision of the Unified Parkinson Disease Rating Scale (total, motor, and nonmotor); STOP-BANG, STOP-BAG, and STOP-B28 showed associations with most outcomes, but the GOAL showed none.

CONCLUSIONS

The STOP-B28 followed by STOP and presence of loud snoring alone seem to have the best overall properties to identify PD patients with OSA, whose clinical characteristics differ from the general population with OSA.

CITATION

Gomes T, Benedetti A, Lafontaine A-L, Kimoff RJ Robinson A, Kaminska M. Validation of STOP, STOP-BANG, STOP-BAG, STOP-B28, and GOAL screening tools for identification of obstructive sleep apnea in patients with Parkinson disease. J Clin Sleep Med. 2023;19(1):45-54.

Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea

Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5-14 percent among adults aged 30-70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disease, and risks related to excessive daytime sleepiness. The gold standard for diagnosis of OSAS is the polysomnography (PSG) test which requires overnight evaluation in a sleep laboratory and expensive infrastructure, which renders it unsuitable for mass screening and diagnosis. Alternatives such as home sleep testing need patients to wear diagnostic instruments overnight, but accuracy continues to be suboptimal while access continues to be a barrier for many. Hence, there is a continued significant underdiagnosis and under-recognition of sleep apnea in the community, with at least one study suggesting that 80-90% of middle-aged adults with moderate to severe sleep apnea remain undiagnosed. Recently, we have seen a surge in applications of artificial intelligence and neural networks in healthcare diagnostics. Several studies have attempted to examine its application in the diagnosis of OSAS. Signals included in data analytics include Electrocardiogram (ECG), photo-pletysmography (PPG), peripheral oxygen saturation (SpO2), and audio signals. A different approach is to study the application of machine learning to use demographic and standard clinical variables and physical findings to try and synthesize predictive models with high accuracy in assisting in the triage of high-risk patients for sleep testing. The current paper will review this latter approach and identify knowledge gaps that may serve as potential avenues for future research.

A predictive model for optimal continuous positive airway pressure in the treatment of pure moderate to severe obstructive sleep apnea in China

Background

Numerous predictive formulas based on different ethnics have been developed to determine continuous positive airway pressure (CPAP) for patients with obstructive sleep apnea (OSA) without laboratory-based manual titrations. However, few studies have focused on patients with OSA in China. Therefore, this study aimed to develop a predictive equation for determining the optimal value of CPAP for patients with OSA in China.

Methods

526 pure moderate to severe OSA patients with attended CPAP titrations during overnight polysomnogram were spited into either formula derivation (419 patients) or validation (107 patients) group according to the treatment time. Predictive model was created in the derivation group, and the accuracy of the model was tested in the validation group.

Results

Apnea hypopnea index (AHI), body mass index (BMI), longest apnea time (LAT), and minimum percutaneous oxygen saturation (minSpO₂) were considered as independent predictors of optimal CPAP through correlation analysis and multiple stepwise regression analysis. The best equation to predict the optimal value of CPAP was: CPAPpred = 7.581 + 0.020*AHI + 0.101*BMI + 0.015*LAT-0.028*minSpO₂ (R² = 27.2%, p < 0.05).The correlation between predictive CPAP and laboratory-determined manual optimal CPAP was significant in the validation group (r = 0.706, p = 0.000). And the pressure determined by the predictive formula did not significantly differ from the manually titrated pressure in the validation cohort (10 ± 1 cmH₂O vs. 11 ± 3 cmH₂O, p = 0.766).

Conclusions

The predictive formula based on AHI, BMI, LAT, and minSpO₂ is useful in calculating the effective CPAP for patients with pure moderate to severe OSA in China to some extent.

Helping early obstructive sleep apnea diagnosis with machine learning: A systematic review (Preprint)

Background

American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.

Objective

We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.

Methods

We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.

Results

Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.

Conclusions

Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.

Trial Registration

PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339

Predicting sleep apnea from three-dimensional face photography

STUDY OBJECTIVES

Craniofacial anatomy is recognized as an important predisposing factor in the pathogenesis of obstructive sleep apnea (OSA). This study used three-dimensional (3D) facial surface analysis of linear and geodesic (shortest line between points over a curved surface) distances to determine the combination of measurements that best predicts presence and severity of OSA.

METHODS

3D face photographs were obtained in 100 adults without OSA (apnea-hypopnea index [AHI] < 5 events/h), 100 with mild OSA (AHI 5 to < 15 events/h), 100 with moderate OSA (AHI 15 to < 30 events/h), and 100 with severe OSA (AHI ≥ 30 events/h). Measurements of linear distances and angles, and geodesic distances were obtained between 24 anatomical landmarks from the 3D photographs. The accuracy with which different combinations of measurements could classify an individual as having OSA or not was assessed using linear discriminant analyses and receiver operating characteristic analyses. These analyses were repeated using different AHI thresholds to define presence of OSA.

RESULTS

Relative to linear measurements, geodesic measurements of craniofacial anatomy improved the ability to identify individuals with and without OSA (classification accuracy 86% and 89% respectively, P < .01). A maximum classification accuracy of 91% was achieved when linear and geodesic measurements were combined into a single predictive algorithm. Accuracy decreased when using AHI thresholds ≥ 10 events/h and ≥ 15 events/h to define OSA although greatest accuracy was always achieved using a combination of linear and geodesic distances.

CONCLUSIONS

This study suggests that 3D photographs of the face have predictive value for OSA and that geodesic measurements enhance this capacity.

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample

STUDY OBJECTIVES

Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice.

METHODS

The SVM model was developed using the features routinely collected at the clinical evaluation from 6,875 Chinese patients referred to sleep clinics for suspected OSA. Three apnea-hypopnea index (AHI) cutoffs, ≥5/h, ≥15/h, and ≥30/h were used to define the severity of OSA. The continuous and categorized features were selected separately and were further selected through stepwise forward feature selection. The modeling was achieved through fivefold cross-validation. The model discriminative ability was evaluated for the whole data set and four subgroups categorized with gender and age (<65 versus ≥65 years old [y/o]).

RESULTS

Two features were selected to predict AHI cutoff ≥5/h with six features selected for ≥15/h, and six features selected for ≥30/h, respectively, to reach Area under the Receiver Operating Characteristic (AUROC) 0.82, 0.80, and 0.78, respectively. The sensitivity was 74.14%, 75.18%, and 70.26%, while the specificity was 74.71%, 68.73%, and 70.30%, respectively. Compared to logistic regression, Berlin questionnaire, NoSAS Score, and Supersparse Linear Integer Model (SLIM) scoring system, the SVM model performs better with a more balanced sensitivity and specificity. The discriminative ability was best for male <65 y/o and modest for female ≥65 y/o.

CONCLUSION

Our model provides a simple and accurate modality for early identification of patients with OSA and may potentially help prioritize them for sleep study.

Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques

This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, n = 213; no OSA, n = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, n = 149; no OSA, n = 46) and test data (OSA, n = 64; no OSA, n = 20). Using the seven clinical indices, the OSA prediction models were trained using four types of machine learning models—logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB)—and each model was validated using the test data. In the validation, the SVM showed the best OSA prediction result with a sensitivity, specificity, and area under curve (AUC) of 80.33%, 86.96%, and 0.87, respectively, while the XGB showed the lowest OSA prediction performance with a sensitivity, specificity, and AUC of 78.69%, 73.91%, and 0.80, respectively. The machine learning algorithms showed high OSA prediction performance using data from South Koreans with suspected OSA. Hence, machine learning will be helpful in clinical applications for OSA prediction in the Korean population.

Metabolic Syndrome in Patients with Coexistent Obstructive Sleep Apnea Syndrome and Chronic Obstructive Pulmonary Disease (Overlap Syndrome)

Background: Evidence suggests that metabolic syndrome (MetS) is highly prevalent in patients with obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD). However, data on the prevalence of MetS in patients having both OSAS and COPD, or overlap syndrome (OS), are scarce. The aim of this study was to evaluate the prevalence and identify predictors of MetS in patients with OS. Methods: MetS was evaluated in consecutive patients who were diagnosed with OS by polysomnography and pulmonary function testing. Results: A total of 163 subjects (138 males and 25 females) were included. MetS was present in 38% of OS patients. Patients were divided into group A (OS without MetS group: 101 patients) and group B (OS with MetS group: 62 patients). Groups were similar in terms of pulmonary function and sleep parameters. In group B, abdominal obesity was the most prevalent component of MetS (100%), followed by hypertension (82.3%), hypertriglyceridemia (72.6%), and hyperglycemia (51.6%). Age (P = 0.009) and body mass index (P = 0.029) were independent predictors of MetS in patients with OS. Conclusions: An increased prevalence of MetS was observed in a group of patients with OS. Early identification and treatment of MetS may play a significant role in prevention of complications related to OS.

Utility of acoustic pharyngometry for screening of obstructive sleep apnea

OBJECTIVE

To determine whether combining acoustic pharyngometric parameters with cephalometric and clinical parameters could improve the predictive power for significant obstructive sleep apnea (OSA) in a Korean population.

METHODS

A total of 229 consecutive adult patients with suspected OSA were enrolled. The predictability for significant OSA using acoustic pharyngometric or cephalometric parameters or combining these parameters and clinical factors was calculated and compared using multivariate logistic regression and receiver operating characteristic (ROC) curves.

RESULTS

In multivariate logistic regression, age, sex, minimum upper airway cross-sectional area (UA-CSA), and mandibular plane to hyoid distance (MPH) were all significant independent predictors of significant OSA. The minimum UA-CSA of 0.85 cm² provided fair discrimination for OSA [area under the curve (AUC): 0.60, 95% confidence interval (CI): 0.52-0.67]. The MPH of 18.75 mm provided fair discrimination for OSA (AUC; 0.65, 95% CI: 0.58-0.72). The discriminative ability of the final model of multivariate ROC curve analyses that included the minimum UA-CSA, age, sex, body mass index (BMI), and MPH was better than the minimum UA-CSA alone (AUCs: 0.77 vs. 0.60). Optimal cut-off values of predictors for discriminating significant OSA were as follows: male for sex, 40 years for age, 25.5 kg/m² for BMI, 1.06 cm² for minimum UA-CSA, and 18 mm for MPH.

CONCLUSION

Minimum UA-CSA measured using acoustic pharyngometry while sitting might be a useful method to predict OSA. Combining minimum UA-CSA with age, sex, BMI and MPH improved the predictive value for significant OSA.

The utility of STOP-BANG questionnaire in the sleep-lab setting

Polysomnography (PSG) is considered the gold standard in obstructive sleep apnea-hypopnea syndrome (OSAS) diagnostics, but its availability is still limited. Thus, it seems useful to assess patients pre-diagnostic risk for OSAS to prioritize the use of this examination. The purpose of this study was to assess positive (PPV) and negative (NPV) predictive values of the STOP BANG questionnaire (SBQ) in patients with presumptive diagnosis of OSAS. From a database of 1,171 (880 men) patients of a university based sleep center, 1,123 (847 men) met eligibility criteria and their SBQ scores were subject to the Bayesian analysis. The analysis of PPV and NPV was conducted at all values of SBQ for all subjects, but also separately for males and females, and for total sleep time (TS) and for sleep in the lateral position (LP). The probability of OSAS (AHI ≥ 5) and at least moderate OSAS (AHI ≥ 15) for TS was 0.766 and 0.516, while for LP the values were 0.432 and 0.289, respectively. Overall, due to low specificity, SBQ had low PPV for TS and LP. Negative test result (SBQ < 3) revealed NPV of 0.620 at AHI < 5 and 0.859 at AHI < 15 for TS, while in LP NPV values were 0.935 at AHI < 5 and 1.0 at AHI < 15, (n = 31), while SBQ < 4 generated NPV of 0.943 in LP (n = 105). SBQ did not change probabilities of OSAS to confirm or rebut diagnosis for TS. However, it is highly probable that SQB can rule out OSAS diagnosis at AHI ≥ 15 for LP.

Diagnostic Accuracy of a Modified STOP-BANG Questionnaire with National Anthropometric Obesity Indexes

OBJECTIVES

Obstructive sleep apnea (OSA) is a very common sleep-related disorder and has many medical complications. Although the STOP-BANG questionnaire is an attractive screening tool because of high sensitivity, it lacks power in specificity. The aim of the present study was to evaluate and compare the diagnostic accuracy of standard STOP-BANG and a modified STOP-BANG questionnaire, using national cut-off values for neck circumference that determined OSA, in a sleep center population.

MATERIALS AND METHODS

One hundred eighty-five participants who were referred to the sleep-disordered breathing clinic were consecutively enrolled. We used 40 cm and 36 cm as the cut-off values for neck circumference, thus scoring patients accordingly and creating a modified STOP-BANG score with national anthropometric obesity indexes.

RESULTS

The median neck circumferences were 41 (39-44) cm, 40 (37-42) cm, and 43 (40-45) cm for total population, female gender, and male gender, respectively. The mean STOP-BANG score was 4.5±1.5, and the mean modified STOP-BANG score was 4.9±1.5. Discrimination of OSA measured by area under the curve for both questionnaires is comparable (p>0.05). Sensitivity to define OSA (apnea-hypopnea index (AHI)≥5) was 92.2% and 93.8% for original and modified STOP-BANG questionnaire, respectively. Sensitivity for moderate (AHI≥15) and severe OSA (AHI≥30) was identical for both questionnaires.

CONCLUSION

The STOP-BANG questionnaire has an excellent sensitivity, but modest specificity and adding national obesity indexes for neck circumference achieved similar results in terms of sensitivity and specificity with the original questionnaire.

High Negative Predictive Value of Normal Body Mass Index for Obstructive Sleep Apnea in the Lateral Sleeping Position

STUDY OBJECTIVES

Obesity is a major risk factor for obstructive sleep apnea (OSA). Patients who are not obese and who have OSA usually present with a low apnea-hypopnea index (AHI) in the lateral sleeping position. Hence, sleep-disordered breathing (SDB) seems more dependent on body mass index (BMI) in the lateral sleeping position than the supine sleep position. This makes obesity a better predictor of SDB in the lateral sleeping position. The objective of this study was to find a negative predictive value of normal BMI for SDB in relation to sleep positions, thus defining a group of patients who could be treated by positional intervention, and prioritizing the use of polysomnography diagnostics.

METHODS

This study comprises a retrospective and prospective part run on groups of 1,181 and 821 consecutive patients, respectively. All had been referred to the university-based sleep laboratory because of suspected OSA and underwent polysomnography.

RESULTS

In the retrospective study, areas under the receiver operating characteristic curves for normal BMI at AHI ≥ 5 and AHI ≥ 15 events/h were found to be larger in the lateral sleeping positing than supine: 0.79 versus 0.69 and 0.80 versus 0.68, respectively (P < .05). Comparable results were obtained in the prospective study. For normal BMI, the negative predictive value for AHI < 15 events/h in the lateral sleep position was 97.5% and 97.1% in the retrospective and prospective study, respectively.

CONCLUSIONS

Normal BMI offers a high negative predictive value for moderate or severe OSA in the lateral sleeping position.

Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomno graphy Resources?

Objectives: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination.

Methods: In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used.

Results: Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model.

Conclusions: Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.

Formula for predicting OSA and the Apnea-Hypopnea Index in Koreans with suspected OSA using clinical, anthropometric, and cephalometric variables

PURPOSE

This study developed formulas to predict obstructive sleep apnea (OSA) and the Apnea-Hypopnea Index (AHI) in Korean patients with suspected OSA using clinical, anthropometric, and cephalometric variables.

METHODS

We evaluated relevant variables in 285 subjects with suspected OSA. These included demographic characteristics, sleep-related symptoms, medical history, clinical scales, anthropometric measurements including facial surface measurements, and cephalometric measurements. All participants underwent full-night laboratory polysomnography. The prediction formula for the probability of OSA was created by logistic regression analysis and confirmed by the bootstrap resampling technique. The formula for predicting the AHI was developed using multiple linear regression analysis.

RESULTS

The probability of having OSA was as follows: p = 1 / (1 + exponential (exp)^-f ), where f = -16.508 + 1.445 × loudness of snoring 4 + 0.485 × loudness of snoring 3 + 0.078 × waist circumference + 0.209 × subnasale-to-stomion distance + 0.183 × thickness of the uvula (UTH) supine + 0.041 × age. The AHI prediction formula was as follows: -112.606 + 3.516 × body mass index + 0.683 × mandibular plane-hyoid supine + 10.915 × loudness of snoring 4 + 6.933 × loudness of snoring 3 + 1.297 × UTH supine + 0.272 × age.

CONCLUSION

This is the first study to establish formulas to predict OSA and the AHI in Koreans with suspected OSA using cephalometric and other variables. These results will contribute to prioritizing the order in which patients with suspected OSA are referred for polysomnography.

Use of Ambulatory Blood Pressure Monitoring for the Screening of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a frequent and underdiagnosed disease in hypertensive individuals who experience cardiovascular events. The aim of this study was to define the best model that combined the ambulatory blood pressure (BP) monitoring (ABPM), anthropometric, sociodemographic, and biological variables to identify moderate to severe OSA. A total of 105 ABPM-confirmed hypertensive patients were evaluated using their clinical histories, blood analyses, ABPM, and home respiratory polygraphic results. A multivariate logistic regression analysis was performed to identify the significant variables. The best model included sex, presence of obesity (body mass index ≥30 kg/m(2) and abdominal obesity), mean daytime BP, mean nocturnal heart rate, and minimal diastolic nighttime BP to achieve an area under the curve of 0.804. Based on this model, a validated scoring system was developed to identify the patients with an apnea-hypopnea index ≥15. Therefore, in untreated hypertensive patients who snored, ABPM variables might be used to identify patients at risk for OSA.

Neck circumference percentile: A screening tool for pediatric obstructive sleep apnea

RATIONALE

Large neck circumference (NC) is associated with obstructive sleep apnea (OSA) in adults, especially males. Since NC changes with age and sex, a lack of reference ranges makes neck size difficult to assess as a screening tool in children.

METHODS

Using a population-based dataset of 1,913 children, we developed reference ranges for NC by age and sex for children aged 6-17 years. In this study, we collected NC data on 245 children aged 6-17 years presenting to the Children's Hospital of Eastern Ontario for polysomnography. The association between NC>the 95th percentile and OSA (total apnea-hypopnea-index>5 events/hr and/or obstructive-apnea-index ≥ 1 event/hr) was explored. Thresholds of BMI percentile and waist circumference were also examined.

RESULTS

Individuals with NC>95th percentile for age and sex had increased risk of OSA (relative risk 1.7 [95% CI 1.0-3.0], P=0.04), compared to those with NC ≤ 95th percentile. BMI ≥ 95th percentile gave similar results (relative risk 1.8 [95% CI 1.1-2.9], P=0.02). When examined by sex, the association was significant in males ≥ 12 years (relative risk 3.3 [95% CI 1.0-10.4], P=0.04), but not females (P=0.63). Neither BMI  ≥ 95th percentile nor waist circumference>95th percentile was significant.

CONCLUSIONS

Children and youth with NC>95th percentile for age and sex have significantly increased risk of OSA. This effect is significant in males ≥ 12 years, whereas BMI is not. NC percentile may be an additional screening tool for OSA in children and youth.

Sex-specific characteristics of anthropometry in patients with obstructive sleep apnea: neck circumference and waist-hip ratio

OBJECTIVE

This study aimed to investigate the sex-specific effects of anthropometric profiles on the occurrence and severity of obstructive sleep apnea (OSA).

METHODS

We evaluated 151 patients with suspected OSA undergoing polysomnography and anthropometric measurements such as body mass index (BMI), neck and waist circumference (NC and WC), and waist-hip ratio (WHR).

RESULTS

In men, NC (P = .006), WC (P = .035), and WHR (P = .003) were significantly increased in OSA and all were significantly correlated with apnea hypopnea index (AHI). However, in female OSA patients, BMI (P = .05), WC (P = .008), and WHR (P = .001) were elevated, but only WHR was significantly correlated with AHI. Correlation analyses showed significant correlations between NC and other anthropometric indexes in men but not in women. The receiver operating characteristic curves revealed that NC and WHR in men, and WHR in women, were significant in both model I (AHI > or = 5) and model 2 (AHI > or = 15).

CONCLUSION

Waist-hip ratio is the most reliable correlate of OSA in both sexes. Neck circumference is an independent risk factor for male, but not for female, OSA patients. These different aspects of obesity may contribute to the pathogenesis of OSA and provide helpful guidance in the screening of OSA.

A clinical prediction formula for apnea-hypopnea index

Objectives. There are many studies regarding unnecessary polysomnography (PSG) when obstructive sleep apnea syndrome (OSAS) is suspected. In order to reduce unnecessary PSG, this study aims to predict the apnea-hypopnea index (AHI) via simple clinical data for patients who complain of OSAS symptoms. Method. Demographic, anthropometric, physical examination and laboratory data of a total of 390 patients (290 men, average age 50 ± 11) who were subject to diagnostic PSG were obtained and evaluated retrospectively. The relationship between these data and the PSG results was analyzed. A multivariate linear regression analysis was performed step by step to identify independent AHI predictors. Results. Useful parameters were found in this analysis in terms of body mass index (BMI), waist circumference (WC), neck circumference (NC), oxygen saturation measured by pulse oximetry (SpO₂), and tonsil size (TS) to predict the AHI. The formula derived from these parameters was the predicted AHI = (0.797 × BMI) + (2.286 × NC) − (1.272 × SpO₂) + (5.114 × TS) + (0.314 × WC). Conclusion. This study showed a strong correlation between AHI score and indicators of obesity. This formula, in terms of predicting the AHI for patients who complain about snoring, witnessed apneas, and excessive daytime sleepiness, may be used to predict OSAS prior to PSG and prevent unnecessary PSG.

Gender-specific anthropometric markers of adiposity, metabolic syndrome and visceral adiposity index (VAI) in patients with obstructive sleep apnea

Obstructive sleep apnea often coexists with visceral adiposity and metabolic syndrome. In this study, we analysed gender-related differences in anthropometrics according to sleep apnea severity and metabolic abnormalities. In addition, the visceral adiposity index, a recently introduced marker of cardiometabolic risk, was analysed. Consecutive subjects with suspected obstructive sleep apnea (n = 528, 423 males, mean age ± standard deviation: 51.3 ± 12.8 years, body mass index: 31.0 ± 6.2 kg m(-2) ) were studied by full polysomnography (apnea-hypopnea index 43.4 ± 27.6 h(-1) ). Variables of general and visceral adiposity were measured (body mass index, neck, waist and hip circumferences, waist-to-hip ratio). The visceral adiposity index was calculated, and metabolic syndrome was assessed (NCEP-ATP III criteria). The sample included controls (apnea-hypopnea index <10 h(-1) , n = 55), and patients with mild-moderate (apnea-hypopnea index 10-30 h(-1) , n = 144) and severe sleep apnea (apnea-hypopnea index >30 h(-1) , n = 329). When anthropometric variables were entered in stepwise multiple regression, body mass index, waist circumference and diagnosis of metabolic syndrome were associated with the apnea-hypopnea index in men (adjusted R(2)  = 0.308); by contrast, only hip circumference and height-normalized neck circumference were associated with sleep apnea severity in women (adjusted R(2)  = 0.339). These results changed little in patients without metabolic syndrome; conversely, waist circumference was the only correlate of apnea-hypopnea index in men and women with metabolic syndrome. The visceral adiposity index increased with insulin resistance, but did not predict sleep apnea severity. These data suggest gender-related interactions between obstructive sleep apnea, obesity and metabolic abnormalities. The visceral adiposity index was a good marker of metabolic syndrome, but not of obstructive sleep apnea.

Neck circumference, metabolic syndrome and obstructive sleep apnea syndrome; evaluation of possible linkage

Background

This study was performed to evaluate neck circumference (NC) and metabolic syndrome (MS) parameters in severe and non-severe (mild-moderate) obstructive sleep apnea syndrome (OSAS) patients according to apnea-hypopnea index (AHI).

Material/Method

We enrolled 44 patients diagnosed with OSAS based on overnight polysomnography. The diagnosis of OSAS was based on AHI. Apnea is a pause of airflow for more than 10 seconds. and hypopnea is a decrease of airflow for more than 10 seconds and oxygen desaturation of 4% or greater. AHI score. per hour; below 5 normal. 5–29 mild-moderate. 30 and above were grouped as severe OSAS. Height. weight. neck circumference (NC). waist circumference (WC) and body mass index (BMI) of the patients were measured. MS was diagnosed by the Adult Treatment Panel (ATP) III criteria (≥3 of the following abnormalities): 1) WC ≥94 cm for males, ≥80 cm for females; 2) arterial blood pressure ≥130/85 mmHg; 3) fasting blood glucose ≥100 mg/dl; 4) high density lipoprotein (HDL) cholesterol <40 mg/dl in man, <50 mg/dl in women; 5) triglycerides ≥150 mg/dl.

Results

Mean BMI and NC were higher in severe OSAS patients compared to non-severe patients (p=0.021. p<0.001). According to ATP III criteria. 64% of severe and 61.1% of non-severe OSAS patients were MS (p=0.847). A logistic regression model displayed an association with NC as a risk factor for severe OSAS (p=0.01). but not with MS.

Conclusions

In this study. NC in severe OSAS patients was significantly higher than in non-severe OSAS patients. The prevalence of metabolic syndrome was not correlated with OSAS severity. NC is an independent risk factor for severe OSAS.

Obstructive sleep apnea syndrome and anthropometric obesity indexes

BACKGROUND

The purpose of this study is to investigate whether the general body adiposity or regional adiposity was a risk factor in the evolution of obstructive sleep apnea syndrome (OSAS) by examining the relationships between the anthropometric obesity indexes such as waist (WC) and neck circumference index (NC), body mass index (BMI), and OSAS in Turkish adult population, and to access the possible differences by gender.

METHODS

The data related to polysomnographic, demographic, and anthropometric indexes of the 499 subjects were examined retrospectively. The patients whose apnea-hypopnea index was ≥5 were determined as OSAS group.

RESULTS

The avarage BMI, WC, and NC of the OSAS group (n = 431) were statistically higher than the control group (p < 0.001). According to logistic regression analysis, BMI, WC, and NC enlargement were observed as significant risk factors for OSAS development. Risk coefficients were determined 5.53 for NC, 4.48 for WC, and 2.22 for BMI. Cutoff point values for anthropometric obesity indexes as OSAS determiner were recorded as below: BMI for male >27.77 kg/m(2) and female >28.93 kg/m(2), NC index for male >40 cm and female >36 cm, and WC index for male >105 cm and female >101 cm.

CONCLUSIONS

BMI, WC, and NC enlargement were determined as significant risk factors for OSAS development. This was an initial study to determine the cutoff points of which increase the OSAS risk in BMI, WC, and NC index in Turkish adult population.

Determination of new prediction formula for nasal continuous positive airway pressure in Turkish patients with obstructive sleep apnea syndrome

BACKGROUND

Race/ethnicity may play an important role in determining body size, severity of obstructive sleep apnea syndrome (OSAS), and effective continuous positive airway pressure (CPAP) (Peff). Turkey is composed of different ethnic groups. Therefore, the aims of this study were to determine new prediction formula for CPAP (Ppred) in Turkish OSAS patients, validate performance of this formula, and compare with Caucasian and Asian formulas.

METHODS

Peff of 250 newly diagnosed moderate-to-severe OSAS patients were calculated by in-laboratory manual titration. Correlation and multiple linear regression analysis were used to model effects of ten anthropometric and polysomnographic variables such as neck circumference (NC) and oxygen desaturation index (ODI) on Peff. New formula was validated in different 130 OSAS patients and compared with previous formulas.

RESULTS

The final prediction formula was [Formula: see text]. When Peff of control group was assessed, it was observed that mean Peff was 8.39 ± 2.00 cmH(2)O and Ppred was 8.23 ± 1.22 cmH(2)O. Ppred was within ±3 cmH(2)O of Peff in 96.2% patients. Besides, Peff was significantly correlated with new formula, and prediction formulas developed for Caucasian and Asian populations (r = 0.651, p < 0.001, r = 0.648, p < 0.001, and r = 0.622, p < 0.001, respectively).

CONCLUSIONS

It is shown that level of CPAP can be successfully predicted from our prediction formula, using NC and ODI and validated in Turkish OSAS patients. New equation correlates with other formulas developed for Caucasian and Asian populations. Our simple formula including ODI, marker of intermittent hypoxia, may be used easily in different populations.