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

Sex-dependent GOAL screening performance in adults at risk for obstructive sleep apnea

OBJECTIVE

To evaluate possible sex-related differences in the performance of the GOAL, a 4-item obstructive sleep apnea (OSA) screening instrument in adults.

METHODS

Between July 2019 and June 2021, this cross-sectional study included consecutively recruited patients from one Brazilian sleep laboratory undergoing overnight polysomnography. Individuals with GOAL scores ≥ 2 of a maximum of 4 points are classified at high risk for OSA diagnosis. Actual OSA severity was based on the apnea-hypopnea index: ≥ 5.0/h as any OSA, ≥ 15.0/h as moderate-to-severe OSA, and ≥ 30.0/h as severe OSA. Performance of the GOAL instrument in women and men was assessed by the discriminatory ability (obtained from area under the curve [AUC]-Receiver Operating Characteristic curves) and 2×2 contingency tables.

RESULTS

A total of 2,978 subjects (55.3% males) were evaluated. The frequency of GOAL-defined OSA high-risk was statistically higher in men when compared to women (p < 0.001). The GOAL predictive parameters for screening all severity OSA levels were as follows: in females, sensitivity ranging from 58.2% to 78.3% and specificity ranging from 60.0% to 77.6%, while in males, sensitivity ranging from 90.5% to 96.9% and specificity from 20.7% to 46.8%. The GOAL questionnaire had similar discriminatory properties, assessed by AUC, in women and in men: i) any OSA: 0.741 vs. 0.771 (p = 0.204), ii) moderate-to-severe OSA: 0.727 vs. 0.737 (p = 0.595), and iii) severe OSA: 0.728 vs. 0.703 (p = 0.240); respectively.

CONCLUSIONS

The GOAL instrument emerges as a useful tool for screening adult individuals and displays similar performance in both women and men.

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

Objective

Obstructive sleep apnea (OSA) is a common sleep-disordered breathing disease. We aimed to establish an improved screening questionnaire without physical examinations for OSA named the CNCQ-OSA (Chinese community questionnaire for OSA).

Methods

A total of 2585 participants who visited sleep medicine center and underwent overnight polysomnography were grouped into two independent cohorts: derivation (n = 2180) and validation (n = 405). The CNCQ-OSA was designed according to the baseline of patients in derivation cohort. We comprehensively analyzed the data to evaluate the predictive value of the CNCQ-OSA, compared to the GOAL questionnaire, STOP-Bang questionnaire (SBQ) and NoSAS questionnaire.

Results

The CNCQ-OSA included seven variables: loud snoring, BMI ≥ 25 kg/m², male gender, apnea, sleepiness, hypertension and age ≥30, with a total score ranging from 7 to 16.7 points (≥13.5 points indicating high risk of OSA, ≥14.5 points indicating extremely high risk). In the derivation and validation cohorts, the areas under the curve of the CNCQ-OSA were 0.761 and 0.767, respectively. In the validation cohort, the sensitivity and specificity of a CNCQ-OSA score ≥13.5 points for the apnea-hypopnea index (AHI) ≥5/h were 0.821 and 0.559, respectively (Youden index, 0.380), and the score ≥14.5 points were 0.494 and 0.887, respectively (Youden index, 0.375). The CNCQ-OSA had a better predictive value for AHI ≥ 5/h, AHI > 15/h and AHI > 30/h, with the highest Youden index, compared to the other questionnaires.

Conclusion

The CNCQ-OSA can effectively identify the risk of OSA, which is appropriate for self-screening at home without physical examinations.

Comparison of six assessment tools to screen for obstructive sleep apnea in patients with hypertension

Background

Obstructive sleep apnea (OSA) is often accompanied by other complications, especially hypertension.

Hypothesis

The purpose of this study is to compare the application value of six tools in the screening of OSA in patients with hypertension. Compared with other questionnaires, we hypothesized that Berlin performed better in screening hypertensive patients suspected of OSA.

Methods

In this study, we collected the basic data and polysomnography (PSG) data of patients diagnosed with hypertension who underwent PSG at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University from April 2012 to March 2021. The sensitivity, specificity, positive predictive value, negative predictive value, area under the curv (AUC) and diagnostic odds ratio (DOR) of the six screening tools were then calculated, and their correlation with the sleep apnea hypopnea index (AHI) analyzed.

Results

There were 303 males (303/398, 76.1%) out of 398 hypertension patients suspected of OSA. The area under the curve of the Berlin questionnaire's receiver operating characteristic (ROC) curve reached 0.753 (95%CI: 0.707–0.794). When the AHI was 5, 15 and 30 times/h as the cut‐off points, the sensitivity and negative predictive value of Berlin were the highest at 0.947 and 0.630, 0.970 and 0.851, and 0.988 and 0.957 respectively, while the specificity and positive predictive value of the Epworth Sleepiness Scale (ESS) were the highest at 0.696 and 0.729, 0.750 and 0.887, and 0.674 and 0.575 respectively. The DOR value of the Berlin questionnaire could reach 18.333 when the AHI cut‐off point was 30 times/h. Berlin had the largest rank correlation coefficient with AHI at 0.466.

Conclusion

The Berlin questionnaire can be considered a priority for the screening and stratifying of hypertensive patients suspected of OSA.

Age-specific markers of adiposity in patients with obstructive sleep apnea

OBJECTIVES

Adiposity can have varying effects on the individual depending upon its distribution pattern. We assessed age-related distribution of adipose tissue by anthropometric measures and bioelectrical impedance analysis, as well as their association with obstructive sleep apnea (OSA) severity.

METHODS

Participants were 169 elderly (aged ≥ 65 years) and 142 non-elderly (aged < 65 years) referred for overnight polysomnography. The associations between obesity parameters and apnea-hypopnea index (AHI) were determine by univariate and multivariate linear regression analyses. Area under receiver operating characteristic curve (AUC) was used to access the predicting performance of some parameters.

RESULTS

Compared with non-elderly, elderly showed higher conicity index and visceral adiposity (VA)/subcutaneous adiposity (SA), lower body mass index (BMI), neck circumference, waist circumference, hip circumference and SA. Multiple regression analyses revealed that VA and VA/SA were independently associated with AHI in elderly (explained 17.2% of the AHI ^0.5 variability), while BMI and VA/SA were independently associated with AHI in non-elderly (explained 25.9% of the AHI ^0.5 variability), after adjusting for age, sex, cigarette smoking, alcohol drinking and main comorbidities. In elderly, VA over 128 cm² and VA/SA less than 0.41 resulted in sensitivity, specificity and AUC of 0.382, 0.790, 0.580 and 0.176, 0.947, 0.553 in predicting moderate-to-severe OSA, respectively. In non-elderly, BMI over 24.7 kg/m² and VA/SA over 0.54 resulted in sensitivity, specificity and AUC of 0.883, 0.484, 0.704 and 0.550, 0.710, 0.667 in predicting moderate-to-severe OSA, respectively.

CONCLUSIONS

VA is strongly associated with OSA severity in elderly, independently of general obesity as per BMI standards, while general adiposity appears to be more strongly associated with OSA severity in non-elderly. Our study supports age-specific approaches should be developed with respect to prediction and treatment of OSA.

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research

Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.