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.

Objectives: ：This study was aimed to determine whether the presence or absence of psychotic symptoms inmood disorders is statistically significantly related to the difference between NLR, MLR, PLR. Methods: ：We retrospectively reviewed the medical records of 408 patients who were hospitalized with a diagnosis of bipolar disorder type 1 (BP-I) and major depressive disorder (MDD) and underwent complete blood count.Groups were divided based on the presence or absence of psychotic symptoms. The statistical significance of the differences in NLR, MLR, and PLR between each group was examined using t-test. Results: ：When 382 mood disorder patients were divided into groups based solely on the presence or absence ofpsychotic symptoms, the difference between NLR and MLR was statistically significant (NLR: p=0.009, MLR:p=0.016). When dividing the mood disorder patients into subgroups of 176 BP-I patients and 206 MDD patients and using the same method for each subgroup, the tendency for higher NLR and MLR was maintained, but the sta-tistical significance disappeared. Conclusions ：This study suggests the possibility of relationship between psychotic symptoms and NLR and MLR in mood disorders, but additional research appears to be necessary to clarify the possibility.

Background: The increasing rate of excess body weight (EBW) in the global population has led to growing health concerns, including cancer-related EBW. We aimed to estimate the population attributable fraction (PAF) of cancer incidence and deaths linked to EBW in Korean individuals from 2015 to 2030 and to compare its value with various body mass index cutoffs. Methods: Levin’s formula was used to calculate the PAF; the prevalence rates were computed using the Korean National Health and Nutrition Examination Survey data, while the relative risks of specific cancers related to EBW were estimated based on the results of Korean cohort studies. To account for the 15-year latency period when estimating the PAF in 2020, the prevalence rates from 2015 and attributable cases or deaths from 2020 were used. Results: The PAF attributed to EBW was similar for both cancer incidence and deaths using either the World Health Organization (WHO) Asian-Pacific region standard or a modified Asian standard, with the WHO standard yielding the lowest values. In the Korean population, the PAFs of EBW for cancer incidence were 2.96% in men and 3.61% in women, while those for cancer deaths were 0.67% in men and 3.06% in women in 2020. Additionally, PAFs showed a gradual increase in both sexes until 2030. Conclusion The EBW continues to have a significant impact on cancer incidence and deaths in Korea. Effective prevention strategies targeting the reduction of this modifiable risk factor can substantially decrease the cancer burden.

Objectives: ：This study was aimed to determine whether the presence or absence of psychotic symptoms inmood disorders is statistically significantly related to the difference between NLR, MLR, PLR. Methods: ：We retrospectively reviewed the medical records of 408 patients who were hospitalized with a diagnosis of bipolar disorder type 1 (BP-I) and major depressive disorder (MDD) and underwent complete blood count.Groups were divided based on the presence or absence of psychotic symptoms. The statistical significance of the differences in NLR, MLR, and PLR between each group was examined using t-test. Results: ：When 382 mood disorder patients were divided into groups based solely on the presence or absence ofpsychotic symptoms, the difference between NLR and MLR was statistically significant (NLR: p=0.009, MLR:p=0.016). When dividing the mood disorder patients into subgroups of 176 BP-I patients and 206 MDD patients and using the same method for each subgroup, the tendency for higher NLR and MLR was maintained, but the sta-tistical significance disappeared. Conclusions ：This study suggests the possibility of relationship between psychotic symptoms and NLR and MLR in mood disorders, but additional research appears to be necessary to clarify the possibility.

Objectives: ：This study was aimed to determine whether the presence or absence of psychotic symptoms inmood disorders is statistically significantly related to the difference between NLR, MLR, PLR. Methods: ：We retrospectively reviewed the medical records of 408 patients who were hospitalized with a diagnosis of bipolar disorder type 1 (BP-I) and major depressive disorder (MDD) and underwent complete blood count.Groups were divided based on the presence or absence of psychotic symptoms. The statistical significance of the differences in NLR, MLR, and PLR between each group was examined using t-test. Results: ：When 382 mood disorder patients were divided into groups based solely on the presence or absence ofpsychotic symptoms, the difference between NLR and MLR was statistically significant (NLR: p=0.009, MLR:p=0.016). When dividing the mood disorder patients into subgroups of 176 BP-I patients and 206 MDD patients and using the same method for each subgroup, the tendency for higher NLR and MLR was maintained, but the sta-tistical significance disappeared. Conclusions ：This study suggests the possibility of relationship between psychotic symptoms and NLR and MLR in mood disorders, but additional research appears to be necessary to clarify the possibility.

Background: The increasing rate of excess body weight (EBW) in the global population has led to growing health concerns, including cancer-related EBW. We aimed to estimate the population attributable fraction (PAF) of cancer incidence and deaths linked to EBW in Korean individuals from 2015 to 2030 and to compare its value with various body mass index cutoffs. Methods: Levin’s formula was used to calculate the PAF; the prevalence rates were computed using the Korean National Health and Nutrition Examination Survey data, while the relative risks of specific cancers related to EBW were estimated based on the results of Korean cohort studies. To account for the 15-year latency period when estimating the PAF in 2020, the prevalence rates from 2015 and attributable cases or deaths from 2020 were used. Results: The PAF attributed to EBW was similar for both cancer incidence and deaths using either the World Health Organization (WHO) Asian-Pacific region standard or a modified Asian standard, with the WHO standard yielding the lowest values. In the Korean population, the PAFs of EBW for cancer incidence were 2.96% in men and 3.61% in women, while those for cancer deaths were 0.67% in men and 3.06% in women in 2020. Additionally, PAFs showed a gradual increase in both sexes until 2030. Conclusion The EBW continues to have a significant impact on cancer incidence and deaths in Korea. Effective prevention strategies targeting the reduction of this modifiable risk factor can substantially decrease the cancer burden.