Purpose The prevalence of Group B Streptococcus (GBS) colonization in pregnant Korean women is increasing; however, nationwide studies are lacking. Therefore, we aimed to analyze regional colonization rates and antimicrobial susceptibility for GBS in pregnant Korean women through a nationwide survey. Materials and Methods From January 2018 to December 2020, data from the Seoul Clinical Laboratories on vaginal swab cultures were retrospectively analyzed to detect maternal GBS carriers. Each swab specimen was inoculated onto a 5% blood agar plate and incubated at 35℃–37℃ in a 5% CO 2 incubator for 24 h. GBS isolates were identified using a Microflex MALDI Biotyper. Antimicrobial susceptibility tests were performed using the Vitek 2 automated system. Results The overall nationwide GBS colonization rate in pregnant Korean women was found to be 10.6% (3578/33721). The maternal GBS colonization rates ranged from 10.5%–10.8% over the 3-year study period. The GBS colonization rates by province, in descending order, were as follows: Jeolla-do, 13.2%; Gangwon-do, 12.0%; Chungcheong-do, 11.8%; Gyeonggi-do, 11.3%; Seoul, 10.2%; and Gyeongsang-do, 9.6%. During the study period, the resistance rates against chloramphenicol, levofloxacin, clindamycin, erythromycin, and tetracycline were 2.6%–2.7%, 18.2%–19.6%, 33.4%–35.7%, 35.6%–36.8%, and 50.5%–53.3%, respectively. Conclusion In pregnant Korean women, GBS colonization rates were in the range of 9.6%–13.2%, with Gyeongsang-do being the lowest and Jeolla-do the highest. The resistance rate against clindamycin was high (33.4%–35.7%). GBS colonization rates during pregnancy should be studied nationwide according to the Centers for Disease Control and Prevention-recommended guidelines with periodic antimicrobial resistance monitoring.

Background Low-density lipoprotein cholesterol is an important marker highly associated with cardiovascular disease. Since the direct measurement of it is inefficient in terms of cost and time, it is common to estimate through the Friedewald equation developed about 50 years ago. However, various limitations exist since the Friedewald equation was not designed for Koreans. This study proposes a new low-density lipoprotein cholesterol estimation equation for South Koreans using nationally approved statistical data. Methods This study used data from the Korean National Health and Nutrition Examination Survey from 2009 to 2019. The 18,837 subjects were used to develop the equation for estimating low-density lipoprotein cholesterol. The subjects included individuals with low-density lipoprotein cholesterol levels directly measured among those with high-density lipoprotein cholesterol, triglycerides, and total cholesterol measured. We compared twelve equations developed in the previous studies and the newly proposed equation (model 1) developed in this study with the actual low-density lipoprotein cholesterol value in various ways. Results The low-density lipoprotein cholesterol value estimated using the estimation formula and the actual low-density lipoprotein cholesterol value were compared using the root mean squared error. When the triglyceride level was less than 400 mg/dL, the root mean squared of the model 1 was 7.96, the lowest compared to other equations, and the model 2 was 7.82. The degree of misclassification was checked according to the NECP ATP III 6 categories. As a result, the misclassification rate of the model 1 was the lowest at 18.9%, and Weighted Kappa was the highest at 0.919 (0.003), which means it significantly reduced the underestimation rate shown in other existing estimation equations. Root mean square error was also compared according to the change in triglycerides level. As the triglycerides level increased, the root mean square error showed an increasing trend in all equations, but it was confirmed that the model 1 was the lowest compared to other equations. Conclusion The newly proposed low-density lipoprotein cholesterol estimation equation showed significantly improved performance compared to the 12 existing estimation equations. The use of representative samples and external verification is required for more sophisticated estimates in the future.