Vibration signals of snoring as a simple severity predictor for obstructive sleep apnea

Snoring Index and Neck Circumference as Predictors of Adult Obstructive Sleep Apnea

Background. Snoring is the cardinal symptom of obstructive sleep apnea (OSA). The acoustic features of snoring sounds include intra-snore (including snoring index [SI]) and inter-snore features. However, the correlation between snoring sounds and the severity of OSA according to the apnea−hypopnea index (AHI) is still unclear. We aimed to use the snoring index (SI) and the Epworth Sleepiness Scale (ESS) to predict OSA and its severity according to the AHI among middle-aged participants referred for polysomnography (PSG). Methods. In total, 50 participants (mean age, 47.5 ± 12.6 years; BMI: 29.2 ± 5.6 kg/m2) who reported snoring and were referred for a diagnosis of OSA and who underwent a whole night of PSG were recruited. Results. The mean AHI was 30.2 ± 27.2, and the mean SI was 87.9 ± 56.3 events/hour. Overall, 11 participants had daytime sleepiness (ESS > 10). The correlation between SI and AHI (r = 0.33, p = 0.021) was significant. Univariate linear regression analysis showed that male gender, body mass index, neck circumference, ESS, and SI were associated with AHI. SI (β = 0.18, p = 0.004) and neck circumference (β = 2.40, p < 0.001) remained significantly associated with AHI by the multivariate linear regression model. Conclusion. The total number of snores per hour of sleep and neck circumference were positively associated with OSA among adults referred for PSG.

Correlation between snoring sounds and obstructive sleep apnea in adults: a meta-regression analysis

OBJECTIVE

Snoring is a dominant clinical symptom in patients with obstructive sleep apnea (OSA), and analyzing snoring sounds might be a potential alternative to polysomnography (PSG) for the assessment of OSA. This study aimed to systematically examine the correlation between the snoring sounds and the apnea-hypopnea index (AHI) as the measures of OSA severity.

MATERIAL AND METHODS

A comprehensive literature review using the MEDLINE, Embase, Cochrane Library, Scopus, and PubMed databases identified the published studies reporting the correlations between and severity of snoring and the AHI values by meta-regression analysis.

RESULTS

In total, 13 studies involving 3,153 adult patients were included in this study. The pooled correlation coefficient for snoring sounds and AHI values was 0.71 (95%CI: 0.49, 0.85) from the random-effects meta-analysis with the Knapp and Hartung adjustment. The I 2 and chi-square Q test demonstrated significant heterogeneity (97.6% and p<0.001). After adjusting for the effects of the other covariates, the mean value of the Fisher's r-to-z transformed correlation coefficient would have 0.80 less by the snoring rate (95%CI = -1.02, -0.57), 1.46 less by the snoring index (95%CI = -1.85, -1.07), and 0.21 less in the mean body mass index (95%CI = -0.31, -0.11), but 0.15 more in the mean age (95%CI = 0.10, 0.20). It fitted the data very well (R 2=0.9641).

CONCLUSION

A high correlation between the severity of snoring and the AHI was found in the studies with PSG. As compared to the snoring rate and the snoring index, the snoring intensity, the snoring frequency, and the snoring time interval index were more sensitive measures for the severity of snoring.

The B-APNEIC score: distilling the STOP-Bang questionnaire to identify patients at high risk for severe obstructive sleep apnoea

The STOP-Bang questionnaire is an established clinical screening tool to identify the risk of having mild, moderate or severe obstructive sleep apnoea using eight variables. It is unclear whether all eight variables contribute equally to the risk of clinically significant obstructive sleep apnoea. We analysed each variable for its contribution to detecting obstructive sleep apnoea; based on the results, we investigated whether the STOP-Bang questionnaire could be abbreviated to identify patients at high risk for severe obstructive sleep apnoea. We recruited patients with suspected obstructive sleep apnoea who were referred for overnight polysomnography. We used multivariable logistic regression to investigate the association of STOP-Bang parameters with severe obstructive sleep apnoea based on clinical and polysomnography data. Regression estimates were used to select variables to create the novel B-APNEIC score. We constructed receiver operating characteristic curves for the STOP-Bang questionnaire and B-APNEIC scores to identify patients with severe obstructive sleep apnoea and compared the areas under the curve using the DeLong method. Of the 275 patients enrolled, 32% (n = 88) had severe obstructive sleep apnoea. Logistic regression demonstrated that neck circumference (OR 2.20; 95%CI 1.10-4.40, p = 0.03) was the only variable independently associated with severe obstructive sleep apnoea. Observed apnoea during sleep, blood pressure and body mass index were the three next most closely trending predictors of severe obstructive sleep apnoea and were included along with neck circumference in the B-APNEIC score. Receiver operating curves demonstrated that the areas under the curve for STOP-Bang vs. B-APNEIC were comparable for identifying patients with severe obstructive sleep apnoea (OR 0.75; 95%CI 0.68-0.81 vs. OR 0.75; 95%CI 0.68-0.81: p = 0.99, respectively). Our results suggest that the B-APNEIC score is a simplified adaptation of the STOP-Bang questionnaire with equivalent effectiveness in identifying patients with severe obstructive sleep apnoea. Further studies are needed to validate and build on our findings.

Objective Relationship Between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning

STUDY OBJECTIVES

Snoring is perceived to be directly proportional to sleep apnea severity, especially obstructive sleep apnea (OSA), but this notion has not been thoroughly and objectively evaluated, despite its popularity in clinical practice. This might lead to overdiagnosis or underdiagnosis of OSA. The goal of this study is to examine this notion and objectively quantify the relationship between sleep apnea and snoring detected using advanced signal processing algorithms.

METHODS

We studied adults referred for polysomnography, from which the apnea-hypopnea index (AHI) was derived. Breath sounds were recorded simultaneously, from which snoring was accurately quantified using acoustic analysis of breath sounds and machine-learning computer algorithms. The snore index (SI) was calculated as the number of snores per hour of sleep.

RESULTS

In 235 patients, the mean AHI was 20.2 ± 18.8 and mean SI was 320.2 ± 266.7 events/h. On the one hand, the overall correlation between SI and AHI was weak but significant (r = .32, P < .0001). There was a significant stepwise increase in SI with increasing OSA severity, but with a remarkable overlap in SI among OSA severity categories. On the other hand, SI had weak negative correlation with central AHI (r = -.14, P = .035). SI had modest positive and negative predictive values for OSA (0.63 and 0.62 on average, respectively) and good sensitivity but low specificity (0.91 and 0.31 on average, respectively) attributed to the large number of snorers without OSA.

CONCLUSIONS

Snoring on its own is probably of limited usefulness in assessing sleep apnea presence and severity, because of its weak relationship with AHI. Thus, the complaint of snoring should be interpreted with caution to avoid unnecessary referrals for sleep apnea testing. Conversely, clinicians should be aware of the possibility of missing diagnosis of patients with sleep apnea who have minimal snoring.