Background: Rosacea is a chronic inflammatory skin disorder characterized by erythema, papules, ocular symptoms, and heightened sensitivity. Patients with neurogenic symptoms such as burning or stinging remain particularly difficult to manage. Current guidelines often underrepresent energy-based devices (EBDs), pigmentary sequelae, psychosocial burden, and ocular comorbidities. Objective: To examine Korean dermatologists’ expert perspectives on rosacea management, focusing on skin sensitivity, neurogenic symptoms, pigmentary changes, psychosocial impact, ocular involvement, and EBD use. Methods: A web-based, 29-item survey was administered to 25 board-certified Korean dermatologists (May–June 2025). Quantitative and qualitative responses were analyzed. Results: Erythematotelangiectatic and papulopustular phenotypes with sensitivity skin predominated. EBDs (pulsed dye laser, intense pulsed light) were frequently used but limited by cost and sensitivity issues. Neurogenic symptoms were recognized but rarely treated with neuromodulators. Post-inflammatory hyperpigmentation was infrequent, yet monitoring was inconsistent.Psychosocial and ocular aspects were acknowledged but seldomly systematically addressed.Respondents expressed interest in emerging adjunctive treatments such as cold plasma, skin boosters, and holistic care approaches. Conclusion Korean dermatologists adopt individualized strategies for rosacea, yet practice gaps remain regarding neurogenic symptoms, pigmentary complications, and psychosocial and ocular comorbidities. Findings support the need for updated multidisciplinary, phenotype-driven guidelines aligned with real-world practice.

Background: With the aging population and advancements in medical care worldwide, the number of older patients with end-stage kidney disease continues to rise. This study aimed to identify factors associated with osteoporosis and osteopenia in older patients undergoing incident hemodialysis and assess their impact on mortality. Methods: We analyzed a large multicenter retrospective cohort of patients aged ≥70 years undergoing incident hemodialysis to identify factors associated with osteoporosis using logistic regression analysis and to assess the association of death with osteoporosis and osteopenia using Cox multivariable analysis. Results: Among 710 patients, 39.0% and 19.6% had osteoporosis and osteopenia, respectively. Osteoporosis was significantly associated with female sex, a history of fractures, and the absence of phosphate binder use. During a median follow-up of 36.8 months, 348 participants (58.8%) died. Mortality rates were the highest in the osteoporosis group (79.8%), followed by the osteopenia (77.2%) and normal bone mineral density (BMD) groups (35.2%). Cox regression analysis revealed that even after adjusting for covariates, the osteoporosis group was significantly associated with a higher mortality risk than the normal BMD group. Osteoporosis at the start of hemodialysis was significantly associated with higher mortality. Conclusion We should consider the importance of bone health in patients undergoing incident hemodialysis and pay attention to the use of phosphate binders and fracture prevention.

Background: Obesity is a well-known risk factor for chronic kidney disease and its progression. However, the impact of obesity on the renal function of the elderly population is uncertain. We investigated the association between obesity and renal outcomes in the elderly. Methods: We analyzed 130,504 participants from the Korean National Health Insurance Service-Senior cohort. Obesity was classified according to body mass index (BMI), sex-specific waist circumference (WC), and the presence of metabolic syndrome. The primary outcome was renal function decline, defined as a decline in the estimated glomerular filtration rate (eGFR) of at least 50% from baseline or new-onset end-stage renal disease. Results: During a follow-up period of 559,531.1 person-years (median, 4.3 years), 2,486 participants (19.0%; incidence rate of 4.44 per 1,000 person-years) showed renal function decline. A multivariate Cox proportional hazards model revealed that BMI/WC was not associated with renal function decline. However, the group with metabolic syndrome had a significantly increased risk of renal function decline compared to the group without metabolic syndrome (adjusted hazard ratio [HR], 1.24; 95% confidence interval [CI], 1.13–1.36). Compared with the non-metabolic syndrome group, the adjusted HRs (95% CI) for participants with one through five components were 0.96 (0.84–1.11), 1.10 (0.96–1.27), 1.24 (1.06–1.45), 1.37 (1.12–1.66), and 1.99 (1.42–2.79), respectively (p for trend < 0.001). Conclusion In elderly Korean adults, metabolic syndrome and the number of its components were associated with a higher risk of renal function decline, but BMI or WC was not significant.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Objective: It has been 1 year since the start of the worldwide coronavirus disease-19 (COVID-19) pandemic. This study analyzed the indirect effects of COVID-19 on treating patients with non-infectious diseases by comparing the incidence of complicated appendicitis before and after the pandemic. Methods: The target group included patients aged at least 16 years diagnosed with acute appendicitis between February 23 and July 31, 2020. Patients diagnosed during the same period in 2019 were selected as the control group. The differences in the incidence of complicated appendicitis before and after COVID-19 were investigated, and the association with various variables was analyzed using the odds ratios and 95% confidence intervals (CIs). Results: The study included 120 subjects in 2019 (pre-COVID group) and 119 cases in 2020 (post-COVID group). The pre-COVID group included 25 cases (20.8%) of complicated appendicitis, while the post-COVID group included 48 cases (40.3%). The median time from symptom onset to visit (pre-hospital time) increased from 15 to 22 hours, and the median time from the visit to surgery (in-hospital time) increased from 7 to 11 hours. Multivariate regression analysis of the three variables revealed odds ratios (95% CIs) of pre-hospital time, erythrocyte sedimentation rate, and inclusion in the post-COVID group of 1.02 (1.01-1.02), 2.07 (1.11-3.86), and 2.15 (1.12-4.11), respectively. Conclusion The incidence of complicated appendicitis increased after the COVID-19 pandemic. Therefore, a healthcare system that can minimize the delay in treating non-infectious emergency patients is needed.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Objective: It has been 1 year since the start of the worldwide coronavirus disease-19 (COVID-19) pandemic. This study analyzed the indirect effects of COVID-19 on treating patients with non-infectious diseases by comparing the incidence of complicated appendicitis before and after the pandemic. Methods: The target group included patients aged at least 16 years diagnosed with acute appendicitis between February 23 and July 31, 2020. Patients diagnosed during the same period in 2019 were selected as the control group. The differences in the incidence of complicated appendicitis before and after COVID-19 were investigated, and the association with various variables was analyzed using the odds ratios and 95% confidence intervals (CIs). Results: The study included 120 subjects in 2019 (pre-COVID group) and 119 cases in 2020 (post-COVID group). The pre-COVID group included 25 cases (20.8%) of complicated appendicitis, while the post-COVID group included 48 cases (40.3%). The median time from symptom onset to visit (pre-hospital time) increased from 15 to 22 hours, and the median time from the visit to surgery (in-hospital time) increased from 7 to 11 hours. Multivariate regression analysis of the three variables revealed odds ratios (95% CIs) of pre-hospital time, erythrocyte sedimentation rate, and inclusion in the post-COVID group of 1.02 (1.01-1.02), 2.07 (1.11-3.86), and 2.15 (1.12-4.11), respectively. Conclusion The incidence of complicated appendicitis increased after the COVID-19 pandemic. Therefore, a healthcare system that can minimize the delay in treating non-infectious emergency patients is needed.