S2 Fig -

Distribution of urine albumin (upper two figures), urinary creatinine (middle figure), and urinary albumin creatinine ratio (lower two figures) in Korean men. (DOCX)</p

Fig 4 -

Relationship between albuminuria and FRS in Korean women (A) and men (B). Urine albumin-to-creatine ratio was categorized into 20 groups (x-axes) based on ascending order. FRS was calculated based on the equation provided by a study [22]. Abbreviation: FRS, Framingham risk score.</p

S1 Fig -

Distribution of urine albumin (upper two figures), urinary creatinine (middle figure), and urinary albumin creatinine ratio (lower two figures) in Korean women. (DOCX)</p

Fig 1 -

UACRs according to CVD status in Korean women (A) and men (B). Left side boxplots (grey and brown colored boxes) indicate median-based summary statistics; specifically, the middle, upper, and lower lines describe median, 75, and 25 percentile values, respectively. Right side boxplots indicate mean-based summary statistics, in which the middle, upper, and lower lines illustrate mean, one standard deviation values, respectively.</p

Fig 3 -

Relationship between albuminuria and cardiometabolic risk factors in Korean women (A) and men (B). Top four graphs (i.e., age, TC, HDL-C, SBP) were obtained by multivariate linear regression after setting the four predictors arranged separately as dependent variables. UACR was determined as the independent variable, and other remnant six predictors as covariates. The lower three graphs (i.e., AHM, smoking, diabetes) were obtained by multivariate logistic regression set to the same conditions as the multivariate linear regression. All x-axes indicate beta-coefficients obtained from the multivariate linear or logistic regressions. UACR levels were log-transformed for the associational analyses. Abbreviations: TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; HTN Med, hypertension medication; DM, diabetes mellitus.</p

Sex-specific characteristics according to UACR tertile.

Sex-specific characteristics according to UACR tertile.</p

Fig 2 -

Relationship between UACR and cardiometabolic risk factors in Korean women (A) and men (B). Beta values were measured by linear regression after setting continuous variables, including age, TC, HDL-C, and SBP as dependent variables and UACR subgroups as independent variables. In case of features exhibiting binomial distribution, such as AHM use, smoking, and diabetes, the ratio of presence of disease or status was set as the dependent variable in the linear regression for the calculation of the Beta value. Abbreviations: UACR, urinary albumin-creatinine ratio; Beta, beta-coefficient; AHM, anti-hypertensive medication; HDL-C. high-density lipoprotein-cholesterol; SBP, systolic blood pressure; TC, total cholesterol.</p

Relationship between the fluconazole usage and the percentage of isolates with non-susceptible to fluconazole, or isolates with decreased susceptibility to fluconazole (MIC ≥4 μg/ml) at nine university hospitals in Korea.

<p>The usage of fluconazole, defined as the daily dose/1,000 patient days (DDD/1,000 PD) at the individual hospital was represented by the grey columns. The percentage of isolates with non-susceptible to fluconazole (closed circle with solid line) and the percentage of isolates with decreased susceptibility to fluconazole (MIC ≥4 μg/ml) (open rectangle with dotted line) showed positive correlations with the usage of fluconazole at the individual hospitals.</p

Data_Sheet_1_Major Bloodstream Infection-Causing Bacterial Pathogens and Their Antimicrobial Resistance in South Korea, 2017–2019: Phase I Report From Kor-GLASS.docx

To monitor national antimicrobial resistance (AMR), the Korea Global AMR Surveillance System (Kor-GLASS) was established. This study analyzed bloodstream infection (BSI) cases from Kor-GLASS phase I from January 2017 to December 2019. Nine non-duplicated Kor-GLASS target pathogens, including Staphylococcus aureus, Enterococcus faecalis, Enterococcus faecium, Streptococcus pneumoniae, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter spp., and Salmonella spp., were isolated from blood specimens from eight sentinel hospitals. Antimicrobial susceptibility testing, AMR genotyping, and strain typing were carried out. Among the 20,041 BSI cases, 15,171 cases were caused by one of the target pathogens, and 12,578 blood isolates were collected for the study. Half (1,059/2,134) of S. aureus isolates were resistant to cefoxitin, and 38.1% (333/873) of E. faecium isolates were resistant to vancomycin. Beta-lactamase-non-producing ampicillin-resistant and penicillin-resistant E. faecalis isolates by disk diffusion method were identified, but the isolates were confirmed as ampicillin-susceptible by broth microdilution method. Among E. coli, an increasing number of isolates carried the blaCTX–M–27 gene, and the ertapenem resistance in 1.4% (30/2,110) of K. pneumoniae isolates was mostly (23/30) conferred by K. pneumoniae carbapenemases. A quarter (108/488) of P. aeruginosa isolates were resistant to meropenem, and 30.5% (33/108) of those carried acquired carbapenemase genes. Over 90% (542/599) of A. baumannii isolates were imipenem-resistant, and all except one harbored the blaOXA–23 gene. Kor-GLASS provided comprehensive AMR surveillance data, and the defined molecular mechanisms of resistance helped us to better understand AMR epidemiology. Comparative analysis with other GLASS-enrolled countries is possible owing to the harmonized system provided by GLASS.</p

Table_2_Major Bloodstream Infection-Causing Bacterial Pathogens and Their Antimicrobial Resistance in South Korea, 2017–2019: Phase I Report From Kor-GLASS.XLSX

To monitor national antimicrobial resistance (AMR), the Korea Global AMR Surveillance System (Kor-GLASS) was established. This study analyzed bloodstream infection (BSI) cases from Kor-GLASS phase I from January 2017 to December 2019. Nine non-duplicated Kor-GLASS target pathogens, including Staphylococcus aureus, Enterococcus faecalis, Enterococcus faecium, Streptococcus pneumoniae, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter spp., and Salmonella spp., were isolated from blood specimens from eight sentinel hospitals. Antimicrobial susceptibility testing, AMR genotyping, and strain typing were carried out. Among the 20,041 BSI cases, 15,171 cases were caused by one of the target pathogens, and 12,578 blood isolates were collected for the study. Half (1,059/2,134) of S. aureus isolates were resistant to cefoxitin, and 38.1% (333/873) of E. faecium isolates were resistant to vancomycin. Beta-lactamase-non-producing ampicillin-resistant and penicillin-resistant E. faecalis isolates by disk diffusion method were identified, but the isolates were confirmed as ampicillin-susceptible by broth microdilution method. Among E. coli, an increasing number of isolates carried the blaCTX–M–27 gene, and the ertapenem resistance in 1.4% (30/2,110) of K. pneumoniae isolates was mostly (23/30) conferred by K. pneumoniae carbapenemases. A quarter (108/488) of P. aeruginosa isolates were resistant to meropenem, and 30.5% (33/108) of those carried acquired carbapenemase genes. Over 90% (542/599) of A. baumannii isolates were imipenem-resistant, and all except one harbored the blaOXA–23 gene. Kor-GLASS provided comprehensive AMR surveillance data, and the defined molecular mechanisms of resistance helped us to better understand AMR epidemiology. Comparative analysis with other GLASS-enrolled countries is possible owing to the harmonized system provided by GLASS.</p