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/Aims: Combination of midazolam and opioids is used widely for endoscopic sedation. Compared with meperidine, fentanyl is reportedly associated with rapid recovery, turnover rate of endoscopy room, and quality of endoscopy. We compared fentanyl with meperidine when combined with midazolam for sedative colonoscopy. Methods: A retrospective, cross-sectional, 1:2 matching study was conducted. Induction and recovery time were compared as the primary outcomes. Moreover, cecal intubation time, withdrawal time, total procedure time of colonoscopy, paradoxical reaction, adenoma detection rate, and adverse effect of midazolam or opioids were assessed as the secondary outcomes. Results: A total of 129 subjects (43 fentanyl vs. 86 meperidine) were included in the analysis. The fentanyl group showed significantly more rapid induction time (4.5±2.7 min vs. 7.5±4.7 min, p<0.001), but longer recovery time (59.5±25.6 min vs. 50.3±10.9 min, p=0.030) than the meperidine group. In multivariate analysis, the induction time of the fentanyl group was 3.40 min faster (p<0.001), but the recovery time was 6.38 min longer (p=0.046) than that of the meperidine group. There was no difference in withdrawal time and adenoma detection rate between the two groups. Conclusions The fentanyl group had more rapid sedation induction time but longer recovery time than the meperidine group.

Background/Aims: Dietary fiber intake is considered a protective factor for diverticular disease such as diverticulitis. However, evidence for an inverse connection between dietary fiber consumption and asymptomatic colonic diverticulosis is lacking. Specifically, few studies have investigated this subject in Asians with different presentations of diverticulosis. Therefore, we assessed the protective effects of a vegetarian diet for asymptomatic colonic diverticulosis in Buddhist monks who are obligatory vegetarians for spiritual reasons compared with the general population. Methods: A retrospective, cross-sectional, case-control study was conducted in age- and sex-matched Buddhist monks and the general population who underwent colonoscopy for screening at a Korean health promotion center from August 2005 to June 2018. We compared the prevalence of asymptomatic diverticulosis between the 2 groups using a self-administered questionnaire. Results: In this study, a total of 1,316 individuals were included (Buddhist monks of 658 and general population of 658) with a mean age of 52.6±9.5 years. The prevalence of asymptomatic diverticulosis in Buddhist monks was lower compared with the general population (6.7% [44/658] vs. 10.8% [71/658], P=0.008). Buddhist monks had a higher rate of high body mass index (BMI) and metabolic syndrome. By a multivariate regression analysis model, a nonvegetarian diet (odds ratio [OR], 1.82; 95% confidence interval [CI], 1.21–2.72, P=0.004), old age (OR, 4.53; 95% CI, 1.36–15.12; P=0.014), male sex (OR, 1.91; 95% CI, 1.28–2.85; P=0.002), and a high BMI (OR, 1.50; 95% CI, 1.01–2.23; P=0.047) were independent predictors of asymptomatic diverticulosis. Moreover, a nonvegetarian diet was associated with both right-sided and left-sided diverticulosis. Conclusions A nonvegetarian diet may increase a risk of asymptomatic colonic diverticulosis in Asians.

BACKGROUND/AIMS: Guidelines recommend surveillance for hepatocellular carcinoma (HCC) recurrence at 3-month intervals during the first year after curative treatment and 6-month intervals thereafter in all patients. This strategy does not reflect individual risk of recurrence. We aimed to stratify risk of recurrence to optimize surveillance intervals 1 year after treatment. METHODS: We retrospectively analyzed 1,316 HCC patients treated with resection/radiofrequency ablation at Barcelona Clinic Liver Cancer stage 0/ A. In patients without 1-year recurrence under 3-monthly surveillance, a new model for recurrence was developed using backward elimination methods: training (n=582)/ validation cohorts (n=291). Overall survival (OS) according to risk stratified by the new model was compared according to surveillance intervals: 3-monthly versus 6-monthly (n=401) after lead time bias correction and propensity-score matching analyses. RESULTS: Among patients without 1-year recurrence, age and international normalized ratio values were significant factors for recurrence (hazard ratio [HR], 1.03; 95% confidence interval [CI], 1.00 to 1.03; p=0.009 and HR, 5.63; 95% CI, 2.24 to 14.18; p < 0.001; respectively). High-risk patients stratified by the new model showed significantly higher recurrence rates than low-risk patients in the validation cohort (HR, 1.73; 95% CI, 1.18 to 2.53; p=0.005). After propensity-score matching between the 3-monthly and 6-monthly surveillance groups, OS in high-risk patients under 3-monthly surveillance was significantly higher than that under 6-monthly surveillance (p=0.04); however, OS in low-risk patients under 3-monthly surveillance was not significantly different from that under 6-monthly surveillance (p=0.17). CONCLUSIONS: In high-risk patients, 3-monthly surveillance can prolong survival compared to 6-monthly surveillance. However, in low-risk patients, 3-monthly surveillance might not be beneficial for survival compared to 6-monthly surveillance.
Bias (Epidemiology) ; Carcinoma, Hepatocellular* ; Cohort Studies ; Humans ; International Normalized Ratio ; Liver Neoplasms ; Recurrence ; Retrospective Studies

BACKGROUND: The role of sorafenib in patients with hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) has been rarely studied. The aim of this study was to evaluate the efficacy of sorafenib in post-LT era. METHODS: Consecutive patients with post-transplant HCC recurrence not eligible to resection or locoregional therapy were included. Patients receiving best supportive care (BSC) until 2007 were compared with those treated by sorafenib thereafter. RESULTS: Of a total of 65 patients, 20 patients received BSC and 45 received sorafenib. Clinical characteristics were similar between two groups except that sorafenib group received tacrolimus and mammalian target-of-rapamycin inhibitors more frequently than BSC group. Treatment with sorafenib conferred a survival advantage as compared with BSC for survival after recurrence (median, 14.2 vs. 6.8 months; P = 0.01). In multivariate analyses, high serum α-fetoprotein level, synchronous intrahepatic recurrence and distant metastasis at the time of recurrence, and BSC were independently associated with poorer survival after recurrence. Sorafenib treatment was associated with better survival after recurrence as compared with BSC (hazard ratio, 0.25; 95% confidence interval, 0.10–0.62; P = 0.002). In addition, sorafenib group showed tolerable toxicity in the post-transplant setting. CONCLUSION: Sorafenib may be beneficial in patients with post-transplant HCC recurrence.
Carcinoma, Hepatocellular* ; Humans ; Liver Transplantation ; Multivariate Analysis ; Neoplasm Metastasis ; Recurrence* ; Tacrolimus