Study Objectives:We evaluated the inter-rater reliabilities of manual polysomnography (PSG) sleep stage scoring. We included all studies that employed Rechtschaffen and Kales (R&K) rules or America...

Choi JH, et al. J Korean Med Sci. 2016 Oct;31(10):1624-1630. https://doi.org/10.3346/jkms.2016.31.10.1624

The Korean Society of Otorhinolaryngology-Head and Neck Surgery and Korean Rhinologic Society appointed a guideline development group (GDG) to establish a clinical practice guideline, and the GDG developed a guideline for nasal irrigation for adult patients with chronic rhinosinusitis (CRS). The guideline focuses on knowledge gaps, practice variations, and clinical concerns associated with nasal irrigation. Nasal irrigation has been recommended as the first-line treatment for CRS in various guidelines, and its clinical effectiveness has been demonstrated through a number of studies with robust evidence. However, no guidelines have presented a consistent nasal irrigation method. Several databases, including OVID Medline, Embase, the Cochrane Library, and KoreaMed, were searched to identify all relevant papers using a predefined search strategy. When insufficient evidence was found, the GDG sought expert opinions and attempted to fill the evidence gap. Evidence-based recommendations for practice were ranked according to the American College of Physicians grading system. The committee developed 11 evidence-based recommendations. This guideline focuses on the evidence-based quality improvement opportunities deemed the most important by the GDG. Moreover, the guideline addresses whether nasal lavage helps treat CRS, what type of rinsing solution should be used, and the effectiveness of using additional medications to increase the therapeutic effect.

The use of sleep tracking devices is increasing as people become more aware of the importance of sleep and interested in monitoring their patterns. With many devices on the market, we conducted a meta-analysis comparing sleep-scoring data from consumer wrist-worn sleep tracking devices with polysomnography to validate the accuracy of devices. We retrieved studies from the databases of SCOPUS, EMBASE, Cochrane Library, PubMed, Web of Science, and KoreaMed, and OVID Medline up to March 2024. We compared personal data about participants and information on objective sleep parameters. From 24 studies, data of 798 patient using Fitbit, Jawbone, myCadian watch, WHOOP strap, Garmin, Basis B1, Zulu Watch, Huami Arc, E4 wristband, Fatigue Science Readiband, Apple Watch, or Xiaomi Mi Band 5 were analyzed. There were significant differences in total sleep time {mean difference (MD) -16.854, 95% confidence interval (CI) [-26.332; -7.375]}, sleep efficiency (MD -4.691, 95% CI [-7.079; -2.302]), sleep latency (MD 2.574, 95% CI [0.606; 4.542]), and wake after sleep onset (MD 13.255, 95% CI [4.522; 21.988]) between all consumer sleep tracking devices and polysomnography. In subgroup analysis, there was no significant difference of wake after sleep onset between Fitbit and polysomnography. There was also no significant difference sleep latency between other devices and polysomnography. Fitbit measured sleep latency longer than other devices, and other devices measured wake after sleep onset longer. Based on Begg and Egger's test, there was no publication bias in total sleep time and sleep efficiency. Wrist-worn sleep tracking devices, while popular, are not as reliable as polysomnography in measuring key sleep parameters like total sleep time, sleep efficiency, and sleep latency. Physicians and consumers should be aware of their limitations and interpret results carefully, though they can still be useful for tracking general sleep patterns. Further improvements and clinical studies are needed to enhance their accuracy.

Background and Objectives: Polysomnography is manually scored by sleep experts. However, manual scoring is a time-consuming and labor-intensive task. The goal of this study was to verify the accuracy of automated sleep-stage scoring based on a deep learning algorithm compared to manual sleep-stage scoring. Materials and Methods: A total of 602 polysomnography datasets from subjects (Male:Female = 397:205) aged 19 to 65 years (mean age, 43.8, standard deviation = 12.2) were included in the study. The performance of the proposed model was evaluated based on kappa value and bootstrapped point-estimate of median percent agreement with a 95% bootstrap confidence interval and R = 1000. The proposed model was trained using 482 datasets and validated using 48 datasets. For testing, 72 datasets were selected randomly. Results: The proposed model exhibited good concordance rates with manual scoring for stages W (94%), N1 (83.9%), N2 (89%), N3 (92%), and R (93%). The average kappa value was 0.84. For the bootstrap method, high overall agreement between the automated deep learning algorithm and manual scoring was observed in stages W (98%), N1 (94%), N2 (92%), N3 (99%), and R (98%) and total (96%). Conclusions: Automated sleep-stage scoring using the proposed model may be a reliable method for sleep-stage classification.

This study aimed to analyze the association of low vitamin D status with thyroid autoimmunity and dysfunction in the Korean population according to sex and menopausal status in women. This study was based on the data acquired from the 6th Korea National Health and Nutrition Examination Survey. We enrolled 4,356 subjects who had data of thyroid function, antithyroid peroxidase antibody (TPOAb), and serum 25-hydroxyvitamin D (25[OH]D) levels. We excluded subjects who were pregnant and who had a history of thyroid disease or thyroid cancer, and those with transient thyroid dysfunction who tested negative for TPOAb (TPOAb[-]). TPOAb positivity (TPOAb[+]) with thyroid dysfunction (subclinical and overt hypothyroidism) was more prevalent in the vitamin D deficient group than in the vitamin D insufficient and sufficient groups including premenopausal (P=0.046) and postmenopausal women (P=0.032), although no significant differences were observed in men. The mean serum 25(OH)D level was significantly lower in the TPOAb(+) with thyroid dysfunction group than in the TPOAb(+) with euthyroidism and TPOAb(-) groups of premenopausal women (P=0.001), although no significant differences were observed in men and postmenopausal women. Multivariate binary logistic regression analysis, adjusted for age, body mass index, and current smoking status, showed that vitamin D insufficiency and deficiency were significantly associated with TPOAb(+) with thyroid dysfunction in premenopausal women (P<0.001), although no significant associations were observed in men and postmenopausal women. Low vitamin D status was significantly associated with thyroid autoimmunity and dysfunction in the Korean population, especially in premenopausal women.