The reliability of presepsin as a biomarker of sepsis may be reduced in patients with acute kidney injury (AKI) requiring continuous kidney replacement therapy (CKRT). This study analyzed the utility of plasma presepsin values in predicting mortality in patients with AKI requiring CKRT, particularly those with sepsis-associated AKI. Methods: This single-center retrospective study included 57 patients who underwent CKRT, with plasma presepsin measurements, from April 2022 to March 2023; 35 had sepsis-associated AKI. The predictive values of plasma presepsin, as well as Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores, for 28-day mortality were analyzed using receiver operating characteristic curves. Multivariate Cox regression analysis was performed to identify risk factors for 28-day mortality in the sepsis-associated AKI subgroup. Results: Overall, plasma presepsin showed a lower area under the curve value (0.636; 95% confidence interval [CI], 0.491–0.781) than the APACHE II (0.663; 95% CI, 0.521–0.804) and SOFA (0.731; 95% CI, 0.599–0.863) scores did. However, in sepsis-associated AKI, the area under the curve increased to 0.799 (95% CI, 0.653–0.946), which was higher than that of the APACHE II (0.638; 95% CI, 0.450–0.826) and SOFA (0.697; 95% CI, 0.519–0.875) scores. In the multivariate Cox regression analysis, a high presepsin level was an independent risk factor for 28-day mortality in sepsis-associated AKI (hazard ratio, 3.437; p = 0.03). Conclusion: Presepsin is a potential prognostic marker in patients with sepsis-associated AKI requiring CKRT.

OBJECTIVES: The purpose of this study was to identify and compare the difference and related factors with general characteristic and health behaviors, a experience of diagnosis and treatment of chronic diseases between rural and urban among elderly in Korea. METHODS: We used the data of Community Health Survey 2017 which were collected by the Korean Center for Disease Control and Prevention. The study population comprised 67,835 elderly peopled aged 65 years or older who participated in the survey. The chi-square test, univariate and multivariate logistic regression analysis were used to analyze data. RESULTS: We identified many significant difference of health behaviors, an experience of diagnosis and treatment with chronic diseases between rural and urban. Compared to urban elderly, the odds ratios (ORs) (95% confidence interval) of rural elderly were 1.136 (1.092–1.183) for diagnosis of diabetes, 1.278 (1.278–1.386) for diagnosis of dyslipidemia, 0.940 (0.904–0.977) for diagnosis of arthritis, 0.785(0.736–0.837) for treatment of arthritis, 1.159 (1.116–1.203) for diagnosis of cataracts, and 1.285(1.200–1.375) for treatment of cataracts. In the experience of diagnosis and treatment of chronic diseases, various variables were derived as contributing factors for each disease. Especially, there were statistically significant difference in the experience of diabetes diagnosis, arthritis diagnosis, cataract diagnosis and dyslipidemia except for hypertension diagnosis (p<0.01) between urban and rural elderly. There were statistically significant differences in the experience of treatment for arthritis and cataract (p<0.01), but there was no significant difference in the experience of treatment for hypertension, diabetes, dyslipidemia between urban and rural elderly. CONCLUSION Therefore, it would be necessary to implement a strategic health management project for diseases that showed significant experience of chronic diseases with diagnosis and treatment, reflecting the related factors of the elderly chronic diseases among the urban and rural areas.