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.

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.

Oxidative stress contributes to the onset of chronic diseases in various organs, including muscles. Morroniside, a type of iridoid glycoside contained in Cornus officinalis, is reported to have advantages as a natural compound that prevents various diseases.However, the question of whether this phytochemical exerts any inhibitory effect against oxidative stress in muscle cells has not been well reported. Therefore, the current study aimed to evaluate whether morroniside can protect against oxidative damage induced by hydrogen peroxide (H 2O2) in murine C2C12 myoblasts. Our results demonstrate that morroniside pretreatment was able to inhibit cytotoxicity while suppressing H2O2-induced DNA damage and apoptosis. Morroniside also significantly improved the antioxidant capacity in H2O2-challenged C2C12 cells by blocking the production of cellular reactive oxygen species and mitochondrial superoxide and increasing glutathione production. In addition, H2O2-induced mitochondrial damage and endoplasmic reticulum (ER) stress were effectively attenuated by morroniside pretreatment, inhibiting cytoplasmic leakage of cytochrome c and expression of ER stress-related proteins. Furthermore, morroniside neutralized H2O2-mediated calcium (Ca2+ ) overload in mitochondria and mitigated the expression of calpains, cytosolic Ca2+ -dependent proteases. Collectively, these findings demonstrate that morroniside protected against mitochondrial impairment and Ca2+ -mediated ER stress by minimizing oxidative stress, thereby inhibiting H2O2-induced cytotoxicity in C2C12 myoblasts.

BACKGROUND/OBJECTIVES: To encourage schools to transform school meal programs to be more educational, it is necessary to evaluate the related environment using a whole school approach. We developed a school food culture evaluation tool to quantitatively evaluate school food culture in Gyeonggi Province, Korea. SUBJECTS/METHODS: Based on a literature review, a school food culture evaluation system consisting of areas, subareas, indicators, and questions (scored on a 5-point scale) was constructed. The validity of the tool was reviewed using focus group interviews, the Delphi technique, and a preliminary survey. Subsequently, evaluation tool was applied to elementary, middle, and high schools in Gyeonggi Province. Data from 115 schools were used for the final analysis. This included 64 elementary schools, 29 middle schools, and 22 high schools. At least one respondent from each group—school administrators, teachers, and nutrition teachers (or dietitians)—participated. The results were compared at the school level. RESULTS: The evaluation tool consisted of 66 questions in 5 areas (institutional environment, physical environment, educational environment, educational governance, and school meal quality). The total average score for school food culture was 3.83 points (elementary school 3.89 points, middle school 3.76 points, and high school 3.76 points) and did not differ significantly among school levels. Among the 5 evaluation areas, scores were highest for institutional environment (4.43 points) and lowest for physical environment (3.07 points).Scores for educational environment, educational governance, and school meal quality were 3.86, 3.85, and 3.97 points, respectively. CONCLUSION It is necessary to improve the physical environment to create a desirable school food culture in Gyeonggi Province. To effectively promote healthy eating, ongoing investment and interventions by local authorities at improving school food culture are needed, with an emphasis on particular factors, such as the eating environment and staff training.