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.

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.

Hypovolemic shock and septic shock present similar symptoms, such as increased heart rate and decreased blood pressure. However, the two conditions have different causes, mechanisms, and treatment approaches. Early differentiation between the two conditions can have a positive impact on patient prognosis. In this case, the patient underwent a right ovarian cystectomy due to a teratoma torsion during a previous pregnancy, followed by treatment for a postoperative infection. While recovering, the patient underwent an emergency cesarean section due to sudden severe abdominal pain. After the surgery, unstable vital signs were suggestive of hypovolemia due to massive bleeding from the cesarean section. Therefore, fluid infusion and blood transfusion were initiated. The vital signs did not improve. So, the patient was reassessed. Body temperature and the previously elevated C-reactive protein levels were remeasured. The results of the reassessment indicated a septic condition due to previous infection. The patient was prescribed additional vasopressors and antibiotics for the following week. Subsequently, the patient’s vital signs stabilized, and the treatment was discontinued.

Hypovolemic shock and septic shock present similar symptoms, such as increased heart rate and decreased blood pressure. However, the two conditions have different causes, mechanisms, and treatment approaches. Early differentiation between the two conditions can have a positive impact on patient prognosis. In this case, the patient underwent a right ovarian cystectomy due to a teratoma torsion during a previous pregnancy, followed by treatment for a postoperative infection. While recovering, the patient underwent an emergency cesarean section due to sudden severe abdominal pain. After the surgery, unstable vital signs were suggestive of hypovolemia due to massive bleeding from the cesarean section. Therefore, fluid infusion and blood transfusion were initiated. The vital signs did not improve. So, the patient was reassessed. Body temperature and the previously elevated C-reactive protein levels were remeasured. The results of the reassessment indicated a septic condition due to previous infection. The patient was prescribed additional vasopressors and antibiotics for the following week. Subsequently, the patient’s vital signs stabilized, and the treatment was discontinued.