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.

Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.

Currently, antimicrobial resistance (AMR) is a major threat to global public health. The antimicrobial stewardship program (ASP) has been proposed as an important approach to overcome this crisis. ASP supports the optimal use of antimicrobials, including appropriate dosing decisions, administration duration, and administration routes. In Korea, efforts are being made to overcome AMR using ASPs as a national policy. The current study aimed to develop core elements of ASP that could be introduced in domestic medical facilities. A Delphi survey was conducted twice to select the core elements through expert consensus.The core elements for implementing the ASP included (1) leadership commitment, (2) operating system, (3) action, (4) tracking, (5) reporting, and (6) education. To ensure these core elements are present at medical facilities, multiple departments must collaborate as teams for ASP operations. Establishing a reimbursement system and a workforce for ASPs are prerequisites for implementing ASPs. To ensure that ASP core elements are actively implemented in medical facilities, it is necessary to provide financial support for ASPs in medical facilities, nurture the healthcare workforce in performing ASPs, apply the core elements to healthcare accreditation, and provide incentives to medical facilities by quality evaluation criteria.

Objectives: . Facial nerve monitoring (FNM) can be used to identify the facial nerve, to obtain information regarding its course, and to evaluate its status during parotidectomy. However, there has been disagreement regarding the efficacy of FNM in reducing the incidence of facial nerve palsy during parotid surgery. Therefore, instead of using electromyography (EMG) to identify the location and state of the facial nerve, we applied an intraoperative neuromonitoring (IONM) system using a surface pressure sensor to detect facial muscle twitching. The objective of this study was to investigate the feasibility of using the IONM system with a surface pressure sensor to detect facial muscle twitching during parotidectomy. Methods: . We evaluated the stimulus thresholds for the detection of muscle twitching in the orbicularis oris and orbicularis oculi, as well as the amplitude and latency of EMG and the surface pressure sensor in 13 facial nerves of seven rabbits, using the same stimulus intensity. Results: . The surface pressure sensor detected muscle twitching in the orbicularis oris and orbicularis oculi in response to a stimulation of 0.1 mA in all 13 facial nerves. The stimulus threshold did not differ between the surface pressure sensor and EMG. Conclusion . The application of IONM using a surface pressure sensor during parotidectomy is noninvasive, reliable, and feasible. Therefore, the IONM system with a surface pressure sensor to measure facial muscle twitching may be an alternative to EMG for verifying the status of the facial nerve.

Background/Aims: Receptor tyrosine kinase-like orphan receptor 2 (ROR2) is a major regulator of Wnt signaling, which is involved in fibroblast dysfunction. Because its role has not been evaluated in idiopathic pulmonary fibrosis (IPF), we examined the clinical implications of ROR2 expression. Methods: ROR2 mRNA expression was measured using reverse transcription polymerase chain reaction in lung tissue-derived fibroblasts from IPF patients (n = 14) and from controls (n = 10). ROR2 protein was measured using enzyme-linked immunosorbent assay in primary fibroblasts from IPF patients (n = 14) and controls (n = 10), and in bronchoalveolar lavage (BAL) fluids obtained from normal controls (NC; n = 30). IPF patients (n = 84), and other patients with interstitial lung diseases, including nonspecific interstitial pneumonia (NSIP; n = 10), hypersensitivity pneumonitis (HP; n = 10), and sarcoidosis (n = 10). Results: ROR2 mRNA and protein levels were significantly higher in IPF fibroblasts than in controls (p = 0.003, p = 0.0017, respectively). ROR2 protein levels in BAL fluids from patients with IPF were significantly higher than in those from NC (p < 0.001), and from patients with NSIP (p = 0.006), HP (p = 0.004), or sarcoidosis (p = 0.004). Receiver operating characteristic curves showed a clear difference between IPF and NC in ROR2 protein level (area under the curve, 0.890; confidence interval, 0.829 to 0.950; p < 0.001). ROR2 protein levels were significantly higher in GAP stage III than in GAP stages I and II (p = 0.016). Conclusions ROR2 may be related to the development of IPF, and its protein level may be a useful and severity-dependent candidate marker for IPF.

Background/Aims: Receptor tyrosine kinase-like orphan receptor 2 (ROR2) is a major regulator of Wnt signaling, which is involved in fibroblast dysfunction. Because its role has not been evaluated in idiopathic pulmonary fibrosis (IPF), we examined the clinical implications of ROR2 expression. Methods: ROR2 mRNA expression was measured using reverse transcription polymerase chain reaction in lung tissue-derived fibroblasts from IPF patients (n = 14) and from controls (n = 10). ROR2 protein was measured using enzyme-linked immunosorbent assay in primary fibroblasts from IPF patients (n = 14) and controls (n = 10), and in bronchoalveolar lavage (BAL) fluids obtained from normal controls (NC; n = 30). IPF patients (n = 84), and other patients with interstitial lung diseases, including nonspecific interstitial pneumonia (NSIP; n = 10), hypersensitivity pneumonitis (HP; n = 10), and sarcoidosis (n = 10). Results: ROR2 mRNA and protein levels were significantly higher in IPF fibroblasts than in controls (p = 0.003, p = 0.0017, respectively). ROR2 protein levels in BAL fluids from patients with IPF were significantly higher than in those from NC (p < 0.001), and from patients with NSIP (p = 0.006), HP (p = 0.004), or sarcoidosis (p = 0.004). Receiver operating characteristic curves showed a clear difference between IPF and NC in ROR2 protein level (area under the curve, 0.890; confidence interval, 0.829 to 0.950; p < 0.001). ROR2 protein levels were significantly higher in GAP stage III than in GAP stages I and II (p = 0.016). Conclusions ROR2 may be related to the development of IPF, and its protein level may be a useful and severity-dependent candidate marker for IPF.