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.

Purpose: Risk factors predicting distant metastasis (DM) in extrahepatic bile duct cancer (EHBDC) patients treated with curative resection were investigated. Materials and Methods: Medical records of 1,418 EHBDC patients undergoing curative resection between Jan 2000 and Dec 2015 from 14 institutions were reviewed. After resection, 924 patients (67.6%) were surveilled without adjuvant therapy, 297 (21.7%) were treated with concurrent chemoradiotherapy (CCRT) and 148 (10.8%) with CCRT followed by chemotherapy. To exclude the treatment effect from innate confounders, patients not treated with adjuvant therapy were evaluated. Results: After a median follow-up of 36.7 months (range, 2.7 to 213.2 months), the 5-year distant metastasis-free survival (DMFS) rate was 57.7%. On multivariate analysis, perihilar or diffuse tumor (hazard ratio [HR], 1.391; p=0.004), poorly differentiated histology (HR, 2.014; p < 0.001), presence of perineural invasion (HR, 1.768; p < 0.001), positive nodal metastasis (HR, 2.670; p < 0.001) and preoperative carbohydrate antigen (CA) 19-9 ≥ 37 U/mL (HR, 1.353; p < 0.001) were significantly associated with inferior DMFS. The DMFS rates significantly differed according to the number of these risk factors. For validation, patients who underwent adjuvant therapy were evaluated. In patients with ≥ 3 factors, additional chemotherapy after CCRT resulted in a superior DMFS compared with CCRT alone (5-year rate, 47.6% vs. 27.7%; p=0.001), but the benefit of additional chemotherapy was not observed in patients with 0-2 risk factors. Conclusion Tumor location, histologic differentiation, perineural invasion, lymph node metastasis, and preoperative CA 19-9 level predicted DM risk in resected EHBDC. These risk factors might help identifying a subset of patients who could benefit from additional chemotherapy after resection.

Background/Aims: Daratumumab has shown an encouraging antitumor effect in patients with multiple myeloma (MM), and was known to alter the immune properties by off-targeting immunosuppressive cells. Here, we aimed to evaluate the change in absolute lymphocyte count (ALC) as a surrogate marker for predicting survival outcomes of patients treated with daratumumab. Methods: Between 2018 and 2021, the medical records of patients with relapsed/refractory MM (RRMM) treated with daratumumab monotherapy at 10 centers in South Korea were reviewed. We collected the ALC data at pre-infusion (D0), day 2 after the first infusion (D2), and prior to the third cycle of daratumumab therapy (D56). Results: Fifty patients who were administered at least two cycles of daratumumab were included. Overall response rate was 54.0% after two cycles of daratumumab treatment. On D2, almost all patients experienced a marked reduction in ALC. However, an increase in ALC on D56 (ALCD56) was observed in patients with non-progressive disease, whereas failure of ALC recovery was noted in those with progressive disease. Patients with ALCD56 > 700/μL (n = 39, 78.0%) had prolonged progression- free survival (PFS) and overall survival (OS) than those with ALCD56 ≤ 700/μL (median PFS: 5.8 months vs. 2.6 months, p = 0.025; median OS: 24.1 months vs. 6.1 months, p = 0.004). In addition, ALCD56 >700/μL was a significant favorable prognostic factor for PFS (hazard ratio [HR], 0.22; p = 0.003) and OS (HR, 0.23; p = 0.012). Conclusions Increase in ALC during daratumumab treatment was significantly associated with prolonged survival outcomes in patients with RRMM. The ALC value can predict clinical outcomes in patients treated with daratumumab.

Objective: Dysphagia is a common clinical condition characterized by difficulty in swallowing. It is sub-classified into oropharyngeal dysphagia, which refers to problems in the mouth and pharynx, and esophageal dysphagia, which refers to problems in the esophageal body and esophagogastric junction. Dysphagia can have a significant negative impact one’s physical health and quality of life as its severity increases. Therefore, proper assessment and management of dysphagia are critical for improving swallowing function and preventing complications. Thus a guideline was developed to provide evidence-based recommendations for assessment and management in patients with dysphagia. Methods: Nineteen key questions on dysphagia were developed. These questions dealt with various aspects of problems related to dysphagia, including assessment, management, and complications. A literature search for relevant articles was conducted using Pubmed, Embase, the Cochrane Library, and one domestic database of KoreaMed, until April 2021. The level of evidence and recommendation grade were established according to the Grading of Recommendation Assessment, Development and Evaluation methodology. Results: Early screening and assessment of videofluoroscopic swallowing were recommended for assessing the presence of dysphagia. Therapeutic methods, such as tongue and pharyngeal muscle strengthening exercises and neuromuscular electrical stimulation with swallowing therapy, were effective in improving swallowing function and quality of life in patients with dysphagia. Nutritional intervention and an oral care program were also recommended. Conclusion This guideline presents recommendations for the assessment and management of patients with oropharyngeal dysphagia, including rehabilitative strategies.

Background/Aims: This study assessed the efficacy and safety of adalimumab (ADA) and explored predictors of response in Korean patients with ulcerative colitis (UC). Methods: A prospective, observational, multicenter study was conducted over 56 weeks in adult patients with moderately to severely active UC who received ADA. Clinical response, remission, and mucosal healing were assessed using the Mayo score. Results: A total of 146 patients were enrolled from 17 academic hospitals. Clinical response rates were 52.1% and 37.7% and clinical remission rates were 24.0% and 22.0% at weeks 8 and 56, respectively. Mucosal healing rates were 39.0% and 30.1% at weeks 8 and 56, respectively. Prior use of anti-tumor necrosis factor-α (anti-TNF-α) did not affect clinical and endoscopic responses. The ADA drug level was significantly higher in patients with better outcomes at week 8 (P<0.05). In patients with lower endoscopic activity, higher body mass index, and higher serum albumin levels at baseline, the clinical response rate was higher at week 8. In patients with lower Mayo scores and C-reactive protein levels, clinical responses, and mucosal healing at week 8, the clinical response rate was higher at week 56. Serious adverse drug reactions were identified in 2.8% of patients. Conclusions ADA is effective and safe for induction and maintenance in Korean patients with UC, regardless of prior anti-TNF-α therapy. The ADA drug level is associated with the efficacy of induction therapy. Patients with better short-term outcomes were predictive of those with an improved long-term response.