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.

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.

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.

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.

The bond strength to the silicate-based CAD/CAM blocks may be affected by the composition and microstructure of the silicatebased blocks, surface treatment, and the properties of the applied adhesive. In this study, the shear bond strength to the silicate blocks with various universal adhesives after sandblasting or HF-treatment was measured, and the effects of the additional silane application after surface treatments are evaluated. Six silica-containing blocks and five universal adhesives currently used in dental clinics were selected. After polishing the silicate block surface, the specimens were divided into two groups. The first groups were HF-treated and the second groups were sandblasted with alumina, and the surface roughness, contact angle, and microstructure were analyzed by CLSM (Confocal Laser Scanning Microscope), contact angle analyzer, and FE-SEM (Field Emission Scanning Electron Microscope).Composite resin (2 mm diameter) was bonded with universal adhesive to silicate blocks and stored in a 37 ℃ water bath for 24 hours, and the shear bond strength was measured using a universal testing machine. The measured values were statistically analyzed using the Tukey-multiple comparison test (α=0.05). For hybrid composite ceramics, there was no significant difference in bond strength between sandblasting or HF-treatment, and additional silane application may not be necessary when bonding with a universal adhesive, whereas for leucite-reinforced and lithium disilicate glass-ceramics, HF-treatment may be more favorable for adhesion than sandblasting, and additional silane application appears to be necessary even when applying a universal adhesive.

Allergen immunotherapy (AIT) has been used for over a century and has been demonstrated to be effective in treating patients with various allergic diseases. AIT allergens can be administered through various routes, including subcutaneous, sublingual, intralymphatic, oral, or epicutaneous routes. Sublingual immunotherapy (SLIT) has recently gained clinical interest, and it is considered an alternative treatment for allergic rhinitis (AR) and asthma. This review provides an overview of the current evidence-based studies that address the use of SLIT for treating AR, including (1) mechanisms of action, (2) appropriate patient selection for SLIT, (3) the current available SLIT products in Korea, and (4) updated information on its efficacy and safety. Finally, this guideline aims to provide the clinician with practical considerations for SLIT.