To our knowledge, although snoring is the most common symptom of OSAS, no international consensus exists regarding its objective definition based on PSG results. Our study suggested that the Snoring Episode Index – the first concept we developed as an indicator related to snoring – was strongly correlated with AHI. This showed the potential of using the Snoring Episode Index as a definition of snoring related to OSA severity. We conducted this study to identify an index strongly correlated with sleep quality or OSA severity using snoring-related data that can be easily obtained using PSG, and found that the snoring episode index served this purpose.
Many attempts have been made to define snoring both objectively and easily, but most of them have had certain limitations. Initially, attempts were made to assess the severity of snoring by administering questionnaires to snorers or their sleeping partners. In one such questionnaire study of snorers and their spouses, subjective factors inevitably played a role, as men underestimated their own snoring and women overestimated their spouse’s snoring.11. Another study aimed to define snoring using a VAS. The 10 snoring samples were rated by 53 raters on a 50-step VAS. The results of this study showed high consistency and concordance, but when the maximum level was adjusted to exclude the effect of different volumes of each snore, the concordance was found to be low.19. Efforts have also been made to objectively define the intensity of snoring. However, due to a lack of international consensus on sound intensity for the definition of snoring, different criteria have been applied according to the investigators. For example, 40 dB or 50 dB was used as the loudness threshold to define snoring12.20. Efforts have also been made to define snoring according to its frequency. In a study examining the relationship between hourly snoring frequency and sleep-related parameters in 74 people, hourly snoring frequency and AHI showed a weak positive correlation. However, the criteria used to define the frequency of snoring were unclear21. In another study, snoring frequency was defined as the percentage of inspiratory breaths during sleep with peak sound intensity ≥ 40 dB, and snoring loudness was defined as the average peak inspiratory sound intensity, which showed a moderate positive correlation with AHI.22. However, in a literature search, we found no study using a definition of snoring that showed as strong a correlation with AHI as did the newly suggested Snoring Episode Index.
Before using the snoring episode, classically used in our PSG laboratory, as a new definition of snoring, we needed to validate the new concept of snoring episode. In this study, we finally defined 3 consecutive snoring events as one snoring episode and produced a snoring episode index by dividing the total number of snoring episodes by the total sleep time, proving that the index of snoring The snoring episode was very strongly correlated with AHI. We defined a single snoring event as nasal airflow pressure > 200 microbar recorded using a nasal pressure transducer. However, this threshold was derived from a consensus between the authors since the polysomnography manual indicated that the snoring setting value was not validated. We conducted a study to optimize the number of snoring events included in a single snoring episode. The number of consecutive snoring events included in a snoring episode was set at 1, 2, 3, 4, and 5; subsequently, the entire PSG record was replayed to calculate the snoring episode index in each case, and the number of consecutive snoring events with the best correlation with AHI was defined to define the snoring episode. When 2 or 3 consecutive snoring events were included in a snoring episode, the correlation between snoring episode index and AHI was highest. Therefore, we determined that the conventional use of 3 consecutive snoring events for the definition of a snoring episode was reasonable.
In this study, four snoring-related parameters were used, namely snoring episode index, snoring percentage, average snoring episode duration and longest snoring episode duration, and their correlations with several sleep-related factors were analyzed. BMI, which is closely related to snoring, showed the highest positive correlation with the Snoring Episode Index among the four parameters. The ESS, a questionnaire reflecting daytime sleepiness (one of the symptoms that indirectly reflects sleep quality), also showed the highest positive correlation with the snoring episode index. Meanwhile, the PSQI, a comprehensive sleep quality survey, showed a negative correlation with the Snoring Episode Index, indicating that it was difficult to accurately predict sleep quality based on snoring. Other major PSG parameters, such as sleep latency, sleep efficiency, percentage of rapid eye movement sleep, and percentage of supine sleep time, did not show high correlations with snore settings.
Establishing a common definition of snoring is important for both research and clinical purposes. Since sleep events such as apnea, hypopnea, oxygen desaturation, and wakefulness are systematically defined, all researchers can conduct research and interpret research results according to common definitions. Moreover, it is possible to interpret the clinical significance and explain it to the patient according to common definitions of the actual medical situation. On the other hand, even though snoring is an important symptom that interferes with a bed partner’s quality of sleep, and it is an important sleep event in OSAS, the definition used differs depending on the researcher or the sleep laboratory. Therefore, there is room for confusion when interpreting snoring-related research findings or explaining the degree of snoring to patients in real-world clinical practice. In particular, the definition of snoring that we tried to establish through this study was the definition in terms of correlation with AHI. The reason for this is that snoring can be easily measured and can be useful for pre-screening for OSA. In a study in the United States, 93% of women and 82% of men with moderate to severe OSA were undiagnosed, as were 98% of women and 90% of men with mild OSA.23. To overcome this problem of underdiagnosis, prompt pre-screening for OSA is important. If the concept of the snoring episode index presented in this study is included in non-contact home sleep testing, the performance of prescreening based on smartphone recording apps can be improved. Nevertheless, the index of snoring episodes proposed in this study has a limit. The Snoring Episode Index was calculated based on snoring assessed using an airflow pressure sensor. Therefore, it must be validated when snoring is assessed using the sound recording system of a smartphone. In the future, we plan to conduct a prospective study to validate the Snoring Episode Index using snoring data obtained using sound recording apps on smartphones or computer speakers. artificial intelligence. Although our study showed that the snoring episode index was lowest in primary snorers, we did not validate the utility of this index in the population of primary snorers. Future studies are also needed to determine the role that the Snoring Episode Index may play in predicting sleep quality and health outcomes in primary snoring.
In conclusion, we propose a new index to diagnose snoring, namely the snoring episode index, and show that this index is strongly correlated with AHI. Although snoring can be defined from a variety of perspectives, the strong correlation between Snoring Episode Index and OSA severity has clinical implication as this index can be used for pre-screening for OSA. Future validation studies using various sound recording systems are needed as they will extend the usefulness of the Snoring Episode Index to home sleep screening apps and devices.