Sleep Research Laboratory and Home and Community Team, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
Sleep Research Laboratory, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
J Clin Sleep Med. 2019 Mar 15;15(3):463-470. doi: 10.5664/jcsm.7676.
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.
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.
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 ( = .32, < .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 ( = -.14, = .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.
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.
打鼾被认为与睡眠呼吸暂停严重程度直接相关，尤其是阻塞性睡眠呼吸暂停（OSA），但尽管在临床实践中很流行，这种观点尚未得到彻底和客观的评估。这可能导致 OSA 的过度诊断或漏诊。本研究旨在检验这一观点，并使用先进的信号处理算法客观量化睡眠呼吸暂停与打鼾之间的关系。
我们研究了因多导睡眠图而被转诊的成年人，从中得出呼吸暂停低通气指数（AHI）。同时记录呼吸声，使用呼吸声的声学分析和机器学习计算机算法准确量化打鼾。鼾指数（SI）计算为每小时睡眠中的鼾声次数。
在 235 名患者中，平均 AHI 为 20.2 ± 18.8，平均 SI 为 320.2 ± 266.7 次/h。一方面，SI 与 AHI 之间的总体相关性较弱但有统计学意义（ =.32， <.0001）。随着 OSA 严重程度的增加，SI 呈显著递增趋势，但在 OSA 严重程度类别中，SI 有明显的重叠。另一方面，SI 与中枢性 AHI 呈弱负相关（ = -.14， =.035）。SI 对 OSA 具有中等的阳性和阴性预测值（平均分别为 0.63 和 0.62），且具有较高的敏感性但较低的特异性（平均分别为 0.91 和 0.31），这归因于大量无 OSA 的打鼾者。
单独打鼾在评估睡眠呼吸暂停的存在和严重程度方面可能用处不大，因为它与 AHI 的关系较弱。因此，对于打鼾的抱怨应谨慎解释，以避免不必要的睡眠呼吸暂停测试转诊。相反，临床医生应意识到对轻度打鼾的睡眠呼吸暂停患者可能会漏诊。
