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: The diet is an important route of exposure to endocrinedisrupting chemicals (EDCs). However, few studies have investigated the association between dietary intake and EDC exposure levels among Koreans. In an earlier study, we showed that the bioactivity of serum aryl hydrocarbon receptor ligands (AhRLs) could be a surrogate biomarker to indicate exposure to EDCs and that they inhibit mitochondrial function. We also found that the mitochondria-inhibiting substances (MIS) in serum ascertained by intracellular adenosine triphosphate (MIS-ATP) and reactive oxygen species (MIS-ROS) levels could be biomarkers of exposure to EDCs, as they showed a strong correlation with AhRL and the levels of EDCs in the blood. Here, we investigated the association between the consumption of specific foods and surrogate serum biomarkers for EDCs, namely AhRL, MIS-ATP, and MIS-ROS, among middle-aged Korean adults. SUBJECTS/METHODS: A total of 1,466 participants aged 45–76 yrs from the Ansung cohort of the Korean Genome and Epidemiology Study were included. Food consumption, including that of meat, fish, vegetables, and fruits, was measured using a semiquantitative food frequency questionnaire. RESULTS: Fish intake was positively associated with AhRL (β = 0.0035, P = 0.0166), whereas cruciferous vegetable intake was negatively associated with AhRL (β = −0.0007, P = 0.0488).Cruciferous vegetable intake was positively associated with the MIS-ATP levels (β = 0.0051, P = 0.0420). A higher intake of fish was significantly associated with an increased risk of high AhRL (tertile: odds ratio [OR], 1.49; 95% confidence intervals (CIs), 1.08–2.06; P for trend = 0.0305). In addition, the second-highest tertile of cruciferous vegetable intake had lower odds of high AhRL than the lowest tertile (OR, 0.73; 95% CIs, 0.54–0.97), although no significant linear trend was observed. CONCLUSION Consumption of different types of foods may be differentially associated with EDC exposure in middle-aged Korean adults.