Snoring sound energy as a potential biomarker for disease severity and surgical response in childhood obstructive sleep apnoea: A pilot study

Abstract

Objective To investigate the relationship between baseline snoring sound energy (SSE) and disease severity, changes in SSE after adenotonsillectomy, and the predictors of surgical success in children with obstructive sleep apnoea (OSA). Design Prospective cohort study. Setting Tertiary referral medical centre. Participants Thirty‐two children with OSA whose apnoea‐hypopnoea index ≥5 or apnoea‐hypopnoea index ≥1.5 with OSA comorbidities were recruited. Patients with complicated OSA were excluded. All participants underwent snoring sound analysis, polysomnography, and adenotonsillectomy. Main outcome measures Snoring sound energy and apnoea‐hypopnoea index were assessed at baseline and 6 months after adenotonsillectomy. Surgical success was defined as a postoperative apnoea‐hypopnoea index <1.5. Results The median age, body mass index, and apnoea‐hypopnoea index was 9 years, 19.0 kg/m2, and 13.2 events/h, respectively. Multivariate logistic regression showed that a baseline tonsil size of IV (odds ratio 15.7 [95% CI: 1.5‐166.3]) and SSE of 801‐1000 Hz > 21.9 dB (odds ratio 32.3 [95% CI: 2.6‐396.6]) were significantly related to severe OSA. Following adenotonsillectomy, apnoea‐hypopnoea index decreased significantly (P < 0.001). SSE of 41‐200 Hz, 201‐400 Hz and 801‐1000 Hz also decreased significantly (P = 0.04, 0.01 and 0.006, respectively). Baseline SSE of 801‐1000 Hz < 8.5 dB significantly predicted surgical success (odds ratio 11.0 [95% CI: 1.4‐85.2]). Conclusions Our findings suggest the potential utility of SSE of 801‐1000 Hz to screen for severe OSA, predict surgical success and assess therapeutic outcomes. Specific baseline SSE may represent a potential biomarker for childhood OSA.

Relationships Among and Predictive Values of Obesity, Inflammation Markers, and Disease Severity in Pediatric Patients with Obstructive Sleep Apnea Before and After Adenotonsillectomy

“…The best predictive models included age < 7.0 years, BMI z-score < 1.44, GM-CSF < 0.2 pg/mL, MCP-1 < 51.2 pg/mL, and RANTES > 15,435.5 pg/mL. Previous studies have reported that age [16,56], BMI z-score [16,56], and AHI [16,28,56,57] are predictors of post-AT AHI, in addition to asthma [16], OSA-18 [28], and snoring sound energy [28]. However, GM-CSF, MCP-1, and RANTES as a surgical outcome predictor have never been reported.…”

“…Paired Student's t-tests were used to compare changes in the variables 3 months postoperatively. Variables with a p-value < 0.05 in the Student's t and ANOVA tests were further dichotomized according to the optimal cut-off value using receiver operating characteristic curves [28]. The dichotomized variables were then assessed using multivariate logistic regression models.…”

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

“…We aimed to detect the severity of OSA with snoring sound analysis and used the MFCC, FF, and VI. Although the MFCC and FF were also used in past studies, they were not adequate for determining the severity of OSA [3,5,21]. U. R. Abeyratne et al used some of these features to investigate snore sounds for OSA/non-OSA classifications [22].…”

“…These complicated devices impose stress on not only patients but also clinical staff because they need to spend a long time connecting the patient to the equipment. For these reasons, although there are many patients with potential sleep apnoea, treatments for patients with potential sleep apnoea have not progressed [5].…”

“…Early recognition and prompt, appropriate intervention reduces mortality and improves the quality of life of OSA patients. Although PSG is the current gold standard for the diagnosis of OSA, the cumbersome process and a prolonged waiting period for this examination may affect the patient's sleep and delay timely treatment [5].…”