Sex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos

Risk stratification by sex and menopausal status in the multivariable apnea prediction index

STUDY OBJECTIVES

To determine the sensitivity of the Multivariable Apnea Prediction (MAP) index for obstructive sleep apnea (OSA) in pre- and post-menopausal women with the goal of developing a tailored scoring classification approach.

METHODS

Data from two studies (N = 386); the diabetes sleep treatment trial (N = 236) and EMPOWER (N = 150) were used to assess the sensitivity and specificity of the MAP index by comparing men (n = 129) to women (n = 257), and premenopausal (n = 100) to post-menopausal women (n = 136). We evaluated participants at two cut points, apnea-hypopnea index (AHI) values of ≥ 5 and ≥ 10, using 0.5 as a predicted probability cut point to establish baseline sensitivity and specificity. Contingency tables and receiver operating characteristic (ROC) analysis were conducted to evaluate the accuracy of the MAP index in predicting OSA in men versus women, and in pre-versus post-menopausal women. To select optimal predicted probabilities for classification by sex and menopausal status, Youden's J statistic was generated from ROC coordinates.

RESULTS

The MAP index was more sensitive to women in the AHI ≥ 5 group (76%) compared to AHI ≥ 10 group (30%). Among post-menopausal women with AHI ≥ 5, sensitivity was similar to men (98%), but less than men when AHI ≥ 10 (32%). Suggested probability cut points for women with an AHI ≥ 10 are 0.24 overall; 0.15 for premenopausal, and 0.38 for postmenopausal women.

CONCLUSIONS

Because women's risk for OSA (AHI ≥ 10) was underestimated by the MAP index, we suggest the use of tailored cut points based on sex and menopausal status or assessing for OSA risk with an AHI of ≥ 5.

Sex-Specific Associations of Obstructive Sleep Apnea Risk With Patient Characteristics and Functional Outcomes After Acute Myocardial Infarction: Evidence From the VIRGO Study

Background Though associations between obstructive sleep apnea (OSA) and cardiovascular outcomes are well described, limited data exist regarding the impact of OSA on sex-specific outcomes after acute myocardial infarction (AMI). Methods and Results The VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study enrolled 3572 adults aged 18 to 55 years with AMI from the United States and Spain during 2008 to 2012. We included patients for whom the Berlin Questionnaire for OSA was scored at the time of AMI admission (3141; 2105 women, 1036 men). We examined the sex-specific association between baseline OSA risk with functional outcomes including health status and depressive symptoms at 1 and 12 months after AMI. Among both groups, 49% of patients were at high risk for OSA (1040 women; 509 men), but only 4.7% (148) of patients had a diagnosed history of OSA. Though patients with a high OSA risk reported worse physical and mental health status and depression than low-risk patients in both sexes, the difference in these functional outcomes was wider in women than men. Moreover, women with a high OSA risk had worse health status, depression, and quality of life than high-risk men, both at baseline and at 1 and 12 months after AMI. Conclusions Young women with a high OSA risk have poorer health status and more depressive symptoms than men at the time of AMI, which may place them at higher risk of poorer health outcomes over the year following the AMI. Further, the majority of patients at high risk of OSA are undiagnosed at the time of presentation of AMI.

Disparities in Sleep-Disordered Breathing: Upstream Risk Factors, Mechanisms, and Implications

Sleep-disordered breathing (SDB) refers to a spectrum of disorders ranging from habitual snoring without frank episodes of obstructed breathing or desaturation during sleep to obstructive sleep apnea, where apneas and hypopneas repetitively occur with resultant intermittent hypoxia, arousal, and sleep disruption. Disparities in SDB reflect its overall high prevalence in children and adults from racially and ethnically minoritized or low socioeconomic status backgrounds coupled with high rates of underdiagnosis and suboptimal treatment.

Brazilian Thoracic Association Consensus on Sleep-disordered Breathing

Sleep is essential for the proper functioning of all individuals. Sleep-disordered breathing can occur at any age and is a common reason for medical visits. The objective of this consensus is to update knowledge about the main causes of sleep-disordered breathing in adult and pediatric populations, with an emphasis on obstructive sleep apnea. Obstructive sleep apnea is an extremely prevalent but often underdiagnosed disease. It is often accompanied by comorbidities, notably cardiovascular, metabolic, and neurocognitive disorders, which have a significant impact on quality of life and mortality rates. Therefore, to create this consensus, the Sleep-Disordered Breathing Department of the Brazilian Thoracic Association brought together 14 experts with recognized, proven experience in sleep-disordered breathing.

The Need for Social and Environmental Determinants of Health Research to Understand and Intervene on Racial/Ethnic Disparities in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a sleep-disordered breathing (SDB) disorder, affects at least 25 million adults in the United States and is associated with increased risk for hypertension, diabetes, and cardiovascular disease (CVD). Racial/ethnic minorities have a disproportionate burden of OSA along with the health sequelae associated with this condition. Despite supporting evidence of racial/ethnic disparities, few studies have investigated SDB including OSA among minoritized racial/ethnic groups. In this scoping review of the literature, the authors summarize current findings related to racial/ethnic disparities in OSA, identified social and environmental determinants of health, treatment inequities, and promising evidence-based interventions and conclude with future research directions.

Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters

BACKGROUND AND OBJECTIVE

The diagnosis of obstructive sleep apnea (OSA) relies on polysomnography which is time-consuming and expensive. We therefore aimed to develop two simple, non-invasive models to screen adults for OSA.

METHODS

The effectiveness of using body mass index (BMI) and a new visual prediction model to screen for OSA was evaluated using a development set (1769 participants) and confirmed using an independent validation set (642 participants).

RESULTS

Based on the development set, the best BMI cut-off value for diagnosing OSA was 26.45 kg/m², with an area under the curve (AUC) of 0.7213 (95% confidence interval (CI), 0.6861-0.7566), a sensitivity of 57% and a specificity of 78%. Through forward conditional logistic regression analysis using a stepwise selection model developed from observed data, seven clinical variables were evaluated as independent predictors of OSA: age, BMI, sex, Epworth Sleepiness Scale score, witnessed apnoeas, dry mouth and arrhythmias. With this new model, the AUC was 0.7991 (95% CI, 0.7668-0.8314) for diagnosing OSA (sensitivity, 75%; specificity, 71%). The results were confirmed using the validation set. A nomogram for predicting OSA was generated based on this new model using statistical software.

CONCLUSIONS

BMI can be used as an indicator to screen for OSA in the community. We created an internally validated, highly distinguishable, visual and parsimonious prediction model comprising BMI and other parameters that can be used to identify patients with OSA among outpatients. Use of this prediction model may help to improve clinical decision-making.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Validating the Lithuanian version of the STOP-BANG questionnaire for diagnosing obstructive sleep apnea

BACKGROUND

The aim of this study was to prepare and validate the Lithuanian version of the STOP-BANG questionnaire and evaluate its correlation with polysomnography results.

METHODS

In this study, we included patients ≥ 18 years of age who underwent overnight polysomnography between January 1 in 2018 and January 1 in 2019. All patients completed the STOP-BANG questionnaire before polysomnography. To assess the adequacy of the questionnaire, we used contingency tables and areas under the receiver operating characteristic curve.

RESULTS

The study included 236 patients. The mean age of the patients was 55.2 ± 11.5 years and 159 (68%) were men. The mean apnea-hypopnea index for the entire study group was 33.8 ± 28.4, and the mean STOP-BANG score was 5.4 ± 1.6 points. Moderate (0.3-0.7, p < 0.05) correlations were found between the STOP-BANG questionnaire scores and all measured objective anthropometric and polysomnography parameters. The questionnaire's Cronbach's alpha score was 0.408. Based on the analysis of the ROC curves, the cut-off STOP-BANG score of 3 points showed a sensitivity of 87% and a specificity of 50% (AUC = 0.717) for the identification of any OSA. The positive predictive value (PPV) for an identification of any OSA at a cut-off point of 3 was 96%, and the negative predictive value (NPV) was 26%.

CONCLUSIONS

The linguistic and cultural adaptation of the Lithuanian version of the STOP-BANG questionnaire was carried out in accordance with international recommendations. The Lithuanian version of the STOP-BANG questionnaire is characterized by high sensitivity and average specificity in diagnosing OSA.

A sleep apnea prediction model developed for African Americans: the Jackson Heart Sleep Study

STUDY OBJECTIVES

African Americans have a high prevalence of severe sleep apnea that is often undiagnosed. We developed a prediction model for sleep apnea and compared the predictive values of that model to other prediction models among African Americans in the Jackson Heart Sleep Study.

METHODS

Participants in the Jackson Heart Sleep Study underwent a type 3 home sleep apnea study and completed standardized measurements and questionnaires. We identified 26 candidate predictors from 17 preselected measures capturing information on demographics, anthropometry, sleep, and comorbidities. To develop the optimal prediction model, we fit logistic regression models using all possible combinations of candidate predictors. We then implemented a series of steps: comparisons of equivalent models based on the C-statistics, bootstrap to evaluate the finite sample properties of the C-statistics between models, and fivefold cross-validation to prevent overfitting.

RESULTS

Of 719 participants, 38% had moderate or severe sleep apnea, 34% were male, and 38% reported habitual snoring. The average age and body mass index were 63.2 (standard deviation:10.7) years and 32.2 (standard deviation: 7.0) kg/m². The final prediction model included age, sex, body mass index, neck circumference, depressive symptoms, snoring, restless sleep, and witnessed apneas. The final model has an equal sensitivity and specificity of 0.72 and better predictive properties than commonly used prediction models.

CONCLUSIONS

In comparing a prediction model developed for African Americans in the Jackson Heart Sleep Study to widely used screening tools, we found a model that included measures of demographics, anthropometry, depressive symptoms, and sleep patterns and symptoms better predicted sleep apnea.

Sea level nocturnal minimal oxygen saturation can accurately detect the presence of obstructive sleep apnea in a population with high pretest probability

PURPOSE

To evaluate whether a predictive model based on nocturnal minimal oxygen saturation (SpO₂) alone can accurately detect the presence of obstructive sleep apnea (OSA) in a population with suspected OSA.

METHODS

A total of 4297 participants with suspected OSA were enrolled in this study, and laboratory-based polysomnography (PSG) tests were performed at sea level in all subjects. Nocturnal minimal SpO₂ was obtained automatically as part of the PSG test. Stratified sampling was used to divide the participants' data into the training set (75%) and the test set (25%). An OSA detection model based on minimal SpO₂ alone was created using the training set data and its performance was evaluated using the independent test set data ("hold-out" evaluation). Gender-specific models, and models based on minimal SpO₂ in combination with other predictive factors (age, body mass index, waist-to-hip ratio, snoring grade, Epworth Sleepiness Scale score, and comorbidities), were also created and compared in terms of OSA detection performance.

RESULTS

The prevalence of OSA was 85.6% in our study population. The models including multiple predictors, and the gender-specific models, failed to outperform the model based solely on minimal SpO₂, which showed good predictive performance (C statistic, 0.922) having an overall accuracy rate of 0.86, sensitivity of 0.87, specificity of 0.84, positive predictive value of 0.97, and positive likelihood ratio of 5.34. In addition, the model based on minimal SpO₂ alone could also accurately predict the presence of moderate-to-severe OSA and severe OSA, with C statistics of 0.914 and 0.900, respectively.

CONCLUSIONS

A predictive model based on nocturnal minimal SpO₂ alone may be an alternative option to detect the presence of OSA in a high-risk population when standard diagnostic tests are unavailable.

Validation of the STOP-Bang questionnaire as a means of screening for obstructive sleep apnea in adults in Brazil

Objective:

To validate the Portuguese-language version of the STOP-Bang (acronym for Snoring, Tiredness, Observed apnea, high blood Pressure, Body mass index, Age, Neck circumference, and Gender) questionnaire, culturally adapted for use in Brazil, as a means of screening for obstructive sleep apnea (OSA) in adults.

Methods:

In this validation study, we enrolled patients ≥ 18 years of age, recruited between May of 2015 and November of 2016. All patients completed the STOP-Bang questionnaire and underwent overnight polysomnography. To evaluate the performance of the questionnaire, we used contingency tables and areas under the (receiver operating characteristic) curve (AUCs).

Results:

We included 456 patients. The mean age was 43.7 ± 12.5 years, and 291 (63.8%) of the patients were male. On the basis of the apnea-hypopnea index (AHI), we categorized OSA as mild/moderate/severe (any OSA; AHI ≥ 5 events/h), moderate/severe (AHI ≥ 15 events/h), or severe (AHI ≥ 30 events/h). The overall prevalence of OSA was 78.3%, compared with 52.0%, and 28.5% for moderate/severe and severe OSA, respectively. The most common score on the STOP-Bang questionnaire was 4 points (n = 106), followed by 3 points (n = 85) and 5 points (n = 82). An increase in the score was paralleled by a reduction in sensitivity with a corresponding increase in specificity for all AHI cut-off points. The AUCs obtained for the identification of any, moderate/severe, and severe OSA were: 0.743, 0.731, and 0.779, respectively. For any OSA, the score on the questionnaire (cut-off, ≥ 3 points) presented sensitivity, specificity, and accuracy of 83.5%, 45.5%, and 75.2%, respectively.

Conclusions:

The STOP-Bang questionnaire performed adequately for OSA screening, indicating that it could be used as an effective screening tool for the disorder.

Development and validation of a simple-to-use clinical nomogram for predicting obstructive sleep apnea

Background

The high cost and low availability of polysomnography (PSG) limits the timely diagnosis of OSA. Herein, we developed and validated a simple-to-use nomogram for predicting OSA.

Methods

We collected and analyzed the cross-sectional data of 4162 participants with suspected OSA, seen at our sleep center between 2007 and 2016. Demographic, biochemical and anthropometric data, as well as sleep parameters were obtained. A least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensionality, select factors, and construct the nomogram. The performance of the nomogram was assessed using calibration and discrimination. Internal validation was also performed.

Results

The LASSO regression analysis identified age, sex, body mass index, neck circumference, waist circumference, glucose, insulin, and apolipoprotein B as significant predictive factors of OSA. Our nomogram model showed good discrimination and calibration in terms of predicting OSA, and had a C-index value of 0.839 according to the internal validation. Discrimination and calibration in the validation group was also good (C-index = 0.820). The nomogram identified individuals at risk for OSA with an area under the curve (AUC) of 0.84 [95% confidence interval (CI), 0.83–0.86].

Conclusions

Our simple-to-use nomogram is not intended to replace standard PSG, but will help physicians better make decisions on PSG arrangement for the patients referred to sleep center.

Electronic supplementary material

The online version of this article (10.1186/s12890-019-0782-1) contains supplementary material, which is available to authorized users.

Prevalence and correlates of obstructive sleep apnea among African Americans: the Jackson Heart Sleep Study

Study Objectives

African Americans have been under-represented in obstructive sleep apnea (OSA) research. This study determined the prevalence and correlates of OSA overall and by sex among African Americans in the Jackson Heart Sleep Study.

Methods

Participants (N = 852) underwent a type 3 in-home sleep apnea study, 7 day wrist actigraphy and completed standardized measurements and questionnaires. OSA was defined as an apnea-hypopnea index (AHI) of ≥15, where hypopneas were defined as ≥ 4% associated desaturation. Physician diagnosis of OSA was self-reported. Logistic regression models were fit to determine the associations of demographics, socioeconomic status, sleep symptoms, actigraphy-based sleep, body mass index (BMI), and comorbidities with OSA.

Results

Average age was 63.1 (standard deviation = 10.7), 66% were female, and mean BMI was 32.0 (6.9) kg/m2. Approximately 24% had an AHI ≥ 15; of those, 5% had a physician diagnosis of OSA. Prevalence of OSA increased across BMI categories, but not age groups. Men had a 12% higher prevalence of OSA compared with women, p < 0.01. Older age, male sex, higher BMI, larger neck circumference, and report of habitual snoring were independently associated with higher odds of OSA, all p < 0.05. Associations between sleep symptoms and OSA were similar for men and women. Sleepiness and waist circumference were not associated with OSA.

Conclusions

There was a high prevalence of objectively measured but undiagnosed OSA in this sample of African Americans. Snoring, BMI, and neck circumference were important markers of OSA for men and women. Our results suggest that screening tools that incorporate information on sleepiness and waist circumference may be suboptimal in this population.

Simplifying the Screening of Obstructive Sleep Apnea With a 2-Item Model, No-Apnea: A Cross-Sectional Study

STUDY OBJECTIVES

To develop and validate a practical model for obstructive sleep apnea (OSA) screening in adults based on objectively assessed criteria, and then compare it with two widely used tools, namely STOP-BANG and NoSAS.

METHODS

This is a retrospective study of an existing database of consecutive outpatients who were referred for polysomnography for suspected sleep-disordered breathing by their primary care physicians. Area under the curve (AUC) and 2 × 2 contingency tables were employed to obtain the performance of the new instrument.

RESULTS

A total of 4,072 subjects were randomly allocated into two independent cohorts: one for derivation (n = 2,037) and one for validation (n = 2,035). A mnemonic model, named No-Apnea, with two variables (neck circumference and age) was developed (total score: 0-9 points). We used the cutoff ≥ 3 to classify patients at high risk of having OSA. OSA severity was categorized by apnea-hypopnea index (AHI): any OSA (AHI 5 ≥ events/h; OSA-5), moderate/ severe OSA (AHI 15 ≥ events/h; OSA-15); and severe OSA (AHI 30 ≥ events/h; OSA-30). In the derivation cohort, the AUCs for screening of OSA-5, OSA-15, and OSA-30 were: 0.784, 0.758, and 0.754; respectively. The rate of subjects correctly screened was 78.1%, 68.8%, and 54.4%, respectively for OSA-5, OSA-15, and OSA-30. Subsequently, the model was validated confirming its reproducibility. In both cohorts, No-Apnea discrimination was similar to STOP-BANG or NoSAS.

CONCLUSIONS

The No-Apnea, a 2-item model, appears to be a useful and practical tool for OSA screening, mainly when limited resources constrain referral evaluation. Despite its simplicity when compared to previously validated tools (STOP-BANG and NoSAS), the instrument exhibits similar performance characteristics.

Management of Obstructive Sleep Apnea in Pregnancy

The spectrum of sleep-disordered breathing (SDB) ranges from mild snoring to obstructive sleep apnea, the most severe form of SDB. Current recommendations are to treat these women with continuous positive airway pressure despite limited data. SDB in early and mid-pregnancy is associated with preeclampsia and gestational diabetes. Pregnant women with a diagnosis of obstructive sleep apnea at delivery were at significantly increased risk of having cardiomyopathy, congestive heart failure, pulmonary embolism, and in-hospital death. These effects were exacerbated in the presence of obesity. Postpartum, these women are at risk for respiratory suppression and should be monitored.

Predictors of sleep-disordered breathing in pregnancy

BACKGROUND

Sleep-disordered breathing (SDB) is common in pregnancy, but there are limited data on predictors.

OBJECTIVES

The objective of this study was to develop predictive models of sleep-disordered breathing during pregnancy.

STUDY DESIGN

Nulliparous women completed validated questionnaires to assess for symptoms related to snoring, fatigue, excessive daytime sleepiness, insomnia, and restless leg syndrome. The questionnaires included questions regarding the timing of sleep and sleep duration, work schedules (eg, shift work, night work), sleep positions, and previously diagnosed sleep disorders. Frequent snoring was defined as self-reported snoring ≥3 days per week. Participants underwent in-home portable sleep studies for sleep-disordered breathing assessment in early (6-15 weeks gestation) and mid pregnancy (22-31 weeks gestation). Sleep-disordered breathing was characterized by an apnea hypopnea index that included all apneas, plus hypopneas with ≥3% oxygen desaturation. For primary analyses, an apnea hypopnea index ≥5 events per hour was used to define sleep-disordered breathing. Odds ratios and 95% confidence intervals were calculated for predictor variables. Predictive ability of the logistic models was estimated with area under the receiver-operating-characteristic curves, along with sensitivities, specificities, and positive and negative predictive values and likelihood ratios.

RESULTS

Among 3705 women who were enrolled, data were available for 3264 and 2512 women in early and mid pregnancy, respectively. The corresponding prevalence of sleep-disordered breathing was 3.6% and 8.3%, respectively. At each time point in gestation, frequent snoring, chronic hypertension, greater maternal age, body mass index, neck circumference, and systolic blood pressure were associated most strongly with an increased risk of sleep-disordered breathing. Logistic regression models that included current age, body mass index, and frequent snoring predicted sleep-disordered breathing in early pregnancy, sleep-disordered breathing in mid pregnancy, and new onset sleep-disordered breathing in mid pregnancy with 10-fold cross-validated area under the receiver-operating-characteristic curves of 0.870, 0.838, and 0.809. We provide a supplement with expanded tables, integrated predictiveness, classification curves, and an predicted probability calculator.

CONCLUSION

Among nulliparous pregnant women, logistic regression models with just 3 variables (ie, age, body mass index, and frequent snoring) achieved good prediction of prevalent and incident sleep-disordered breathing. These results can help with screening for sleep-disordered breathing in the clinical setting and for future clinical treatment trials.

Obstructive Sleep Apnea Phenotypes and Markers of Vascular Disease: A Review

Obstructive sleep apnea (OSA) is a chronic and heterogeneous disorder that leads to early mortality, stroke, and cardiovascular disease (CVD). OSA is defined by the apnea–hypopnea index, which is an index of OSA severity that combines apneas (pauses in breathing) and hypopneas (partial obstructions in breathing) associated with hypoxemia. Yet, other sleep metrics (i.e., oxygen nadir, arousal frequency), along with clinical symptoms and molecular markers could be better predictors of stroke and CVD outcomes in OSA. The recent focus on personalized medical care introduces the possibility of a unique approach to the treatment of OSA based on its phenotypes, defined by pathophysiological mechanisms and/or clinical presentation. We summarized what is known about OSA and its phenotypes, and review the literature on factors or intermediate markers that could increase stroke risk and CVD in patients with OSA. The OSA phenotypes where divided across three different domains (1) clinical symptoms (i.e., daytime sleepiness), (2) genetic/molecular markers, and (3) experimental data-driven approach (e.g., cluster analysis). Finally, we further highlight gaps in the literature framing a research agenda.