Snoring Sound Characteristics are Associated with Common Carotid Artery Profiles in Patients with Obstructive Sleep Apnea

Chuang, Hai Hua; Liu, Chi‐Hung; Wang, Chao Yung; Lo, Yu Lun; Lee, Guo She; Yi, Chao; Li, Hsueh Yu; Kuo, Terry B.J.; Yang, Cheryl C.H.; Shyu, Liang Yu; Lee, Li‐Ang

doi:10.2147/nss.s311125

Cited by 5 publications

(5 citation statements)

References 63 publications

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“…The co-existence of MetS and OSA may aggravate the severity of carotid intimamedia thickness and atherosclerosis. Additionally, it potentiates other underlying mechanisms of CVD, which may explain the increased incidence risk for MACE (30)(31)(32). Thus, our study further investigated the impact of concomitant MetS on the long-term risk of MACE in elderly patients with OSA.…”

Section: Discussionmentioning

confidence: 95%

Association of Metabolic Syndrome With Long-Term Cardiovascular Risks and All-Cause Mortality in Elderly Patients With Obstructive Sleep Apnea

Liu

Zhao

et al. 2022

Front. Cardiovasc. Med.

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BackgroundEvidence suggests that an increased risk of major adverse cardiac events (MACE) and all-cause mortality is associated with obstructive sleep apnea (OSA), particularly in the elderly. Metabolic syndrome (MetS) increases cardiovascular risk in the general population; however, less is known about its influence in patients with OSA. We aimed to assess whether MetS affected the risk of MACE and all-cause mortality in elderly patients with OSA.MethodsFrom January 2015 to October 2017, 1,157 patients with OSA, aged ≥60 years, no myocardial infarction (MI), and hospitalization for unstable angina or heart failure were enrolled at baseline and were followed up prospectively. OSA is defined as an apnea-hypopnea index of ≥5 events per hour, as recorded by polysomnography. Patients were classified on the basis of the presence of MetS, according to the definition of the National Cholesterol Education Program (NCEP). Incidence rates were expressed as cumulative incidence. Cox proportional hazards analysis was used to estimate the risk of all events. The primary outcomes were MACE, which included cardiovascular death, MI, and hospitalization for unstable angina or heart failure. Secondary outcomes were all-cause mortality, components of MACE, and a composite of all events.ResultsMetS was present in 703 out of 1,157 (60.8%) elderly patients with OSA. During the median follow-up of 42 months, 119 (10.3%) patients experienced MACE. MetS conferred a cumulative incidence of MACE in elderly patients with OSA (log-rank, P < 0.001). In addition, there was a trend for MACE incidence risk to gradually increase in individuals with ≥3 MetS components (P = 0.045). Multivariate analysis showed that MetS was associated with an incidence risk for MACE [adjusted hazard ratio (aHR), 1.86; 95% confidence interval (CI), 1.17–2.96; P = 0.009], a composite of all events (aHR, 1.54; 95% CI, 1.03–2.32; P = 0.036), and hospitalization for unstable angina (aHR, 2.01; 95% CI, 1.04–3.90; P = 0.039). No significant differences in the risk of all-cause mortality and other components of MACE between patients with and without MetS (P > 0.05). Subgroup analysis demonstrated that males (aHR, 2.23; 95% CI, 1.28–3.91, P = 0.05), individuals aged <70 years (aHR, 2.36; 95% CI, 1.27–4.39, P = 0.006), overweight and obese individuals (aHR, 2.32; 95% CI, 1.34–4.01, P = 0.003), and those with moderate-severe OSA (aHR, 1.81;95% CI: 1.05–3.12, P = 0.032) and concomitant MetS were at a higher risk for MACE.ConclusionMetS is common in elderly patients with OSA in the absence of MI, hospitalization for unstable angina or heart failure. Further, it confers an independent, increased risk of MACE, a composite of all events, and hospitalization for unstable angina. Overweight and obese males, aged <70 years with moderate-severe OSA combined with MetS presented a significantly higher MACE risk.

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Section: Discussionmentioning

confidence: 95%

Association of Metabolic Syndrome With Long-Term Cardiovascular Risks and All-Cause Mortality in Elderly Patients With Obstructive Sleep Apnea

Liu

Zhao

et al. 2022

Front. Cardiovasc. Med.

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“…Lee et al and Chuang at al. also revealed that snoring sound energy may be related to common carotid artery intima–media injury in patients with OSA [ 10 , 14 ]. Furthermore, Ekin et al found extended high-frequency hearing loss in OSA patients, and even in simple snorers [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

“…The energy levels (between snores and noise) of various frequency domains (in dB) were acquired using a specially developed software program called snoring sound energy [ 13 ]. For further analysis, the average snoring sound energy of three different frequency domains (B1, 4–300 Hz; B2, 301–850 Hz; B3, 851–1500 Hz) was calculated according to our previous study [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

Obstructive Sleep Apnea and Auditory Dysfunction—Does Snoring Sound Play a Role?

Lee

et al. 2022

Diagnostics

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The objective of the study was to investigate the relationship between obstructive sleep apnea (OSA) and auditory dysfunction, and to clarify the role of snoring sounds in contributing to auditory dysfunction. A comprehensive assessment of OSA and the auditory system was performed, including overnight polysomnography, detection of the intra-ear canal snoring sound energy (SSE), pure tone average (PTA), tinnitus pitch matching, the tinnitus handicap inventory (THI), and the Epworth sleepiness scale (ESS). The patients were identified as having tinnitus if their THI score was higher than zero or their tinnitus pitches were matched to specific frequencies. The median age, body mass index, and apnea–hypopnea index score were 41 years, 26.4 kg/m2, and 29.9 events/h, respectively. Among the 50 participants, 46 (92%) had a normal PTA, and only 4 (8%) patients had mild hearing loss. There was no significant difference in PTA among OSA severities (p = 0.52). Among the 50 participants, 33 patients (66%) were identified as having tinnitus. In the tinnitus group (n = 33), the ESS score (p = 0.01) and intra-ear canal SSE of 851–1500 Hz (p = 0.04) were significantly higher than those in the non-tinnitus group (n = 17). OSA patients with a higher ESS score had a higher risk of tinnitus (odds ratio 1.22 [95% CI: 1.01–1.46]). OSA-related auditory dysfunction emerged in tinnitus rather than in hearing impairment. OSA patients with daytime sleepiness had a higher risk of tinnitus. High-frequency SSE can jeopardize cochlea and is a potential mechanism contributing to tinnitus. Detection of snoring sounds through an intra-ear canal device may be more precise in assessing acoustic trauma from snoring sounds to vulnerable auditory system and thus warrants further research.

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“…The voice recorder was placed one meter away from the head of the participants. 20 The audio recordings were annotated with apnea and hypopnea labels according to the simultaneous PSG data. Each participant was asked to write down the time of sleep onset and sleep ending as estimated sleep time.…”

Section: Methodsmentioning

confidence: 99%

Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning

Wang¹,

Tang²,

Ai³

et al. 2022

NSS

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This study aimed to propose a novel deep-learning method for automatic sleep apneic event detection and thus to estimate the apnea hypopnea index (AHI) and identify obstructive sleep apnea (OSA) in an event-by-event manner solely based on sleep sounds obtained by a noncontact audio recorder. Methods: We conducted a cross-sectional study of participants with habitual snoring or heavy breathing sounds during sleep to train and test a deep convolutional neural network named OSAnet for the detection of OSA based on sleep sounds. Polysomnography (PSG) was conducted, and sleep sounds were recorded simultaneously in a regular room without noise attenuation. The study was conducted in two phases. In phase one, eligible participants were enrolled and randomly allocated into training and validation groups for deep learning algorithm development. In phase two, eligible patients were enrolled in a test group for algorithm assessment. Sensitivity, specificity, accuracy, unweighted Cohen kappa coefficient (κ) and the area under the curve (AUC) were calculated using PSG as the reference standard. Results: A total of 135 participants were randomly divided into a training group (n, 116) and a validation group (n, 19). An independent test group of 59 participants was subsequently enrolled. Our algorithm achieved a precision of 0.81 and sensitivity of 0.78 in the test group for overall sleep event detection. The algorithm exhibited robust diagnostic performance to identify severe cases with a sensitivity of 95.6% and specificity of 91.6%. Conclusion: Our results showed that a deep learning algorithm based on sleep sounds recorded by a noncontact voice recorder served as a feasible tool for apneic event detection and OSA identification. This technique may hold promise for OSA assessment in the community in a relatively comfortable and low-cost manner. Further studies to develop a tool based on a home-based setting are warranted.

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Snoring Sound Characteristics are Associated with Common Carotid Artery Profiles in Patients with Obstructive Sleep Apnea

Cited by 5 publications

References 63 publications

Association of Metabolic Syndrome With Long-Term Cardiovascular Risks and All-Cause Mortality in Elderly Patients With Obstructive Sleep Apnea

Association of Metabolic Syndrome With Long-Term Cardiovascular Risks and All-Cause Mortality in Elderly Patients With Obstructive Sleep Apnea

Obstructive Sleep Apnea and Auditory Dysfunction—Does Snoring Sound Play a Role?

Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning

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