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.

OBJECTIVES: Proteinuria is widely used to predict cardiovascular risk. However, there is insufficient evidence to predict how changes in proteinuria may affect the incidence of cardiovascular disease. METHODS: The study included 265,236 Korean adults who underwent health checkups in 2003-2004 and 2007-2008. They were categorized into 4 groups based on changes in proteinuria (negative: negative → negative; resolved: proteinuria ≥1+ → negative; incident: negative → proteinuria ≥1+; persistent: proteinuria ≥1+ → proteinuria ≥1+). We conducted 6 years of follow-up to identify the risks of developing ischemic heart disease (IHD), acute myocardial infarction (AMI), and angina pectoris according to changes in proteinuria. A multivariate Cox proportional-hazards model was used to calculate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for incident IHD, AMI, and angina pectoris. RESULTS: The IHD risk (expressed as HR [95% CI]) was the highest for persistent proteinuria, followed in descending order by incident and resolved proteinuria, compared with negative proteinuria (negative: reference, resolved: 1.211 [95% CI, 1.104 to 1.329], incident: 1.288 [95% CI, 1.184 to 1.400], and persistent: 1.578 [95% CI, 1.324 to 1.881]). The same pattern was associated with AMI (negative: reference, resolved: 1.401 [95% CI, 1.048 to 1.872], incident: 1.606 [95% CI, 1.268 to 2.035], and persistent: 2.069 [95% CI, 1.281 to 3.342]) and angina pectoris (negative: reference, resolved: 1.184 [95% CI, 1.065 to 1.316], incident: 1.275 [95% CI, 1.160 to 1.401], and persistent: 1.554 [95% CI, 1.272 to 1.899]). CONCLUSIONS Experiencing proteinuria increased the risks of IHD, AMI, and angina pectoris even after proteinuria resolved.

OBJECTIVES: Our study examined the dose-response relationship between smoking amounts (pack-years) and the risk of developing pancreatic cancer in Korean men. METHODS: Of 125,743 participants who underwent medical health checkups in 2009, 121,408 were included in the final analysis and observed for the development of pancreatic cancer. We evaluated the associations between smoking amounts and incident pancreatic cancer in 4 groups classified by pack-year amounts. Cox proportional hazards models were used to estimate the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of incident pancreatic cancer by comparing groups 2 (<20 pack-year smokers), 3 (20-≤40 pack-year smokers), and 4 (>40 pack-year smokers) with group 1 (never smokers). RESULTS: During 527,974.5 person-years of follow-up, 245 incident cases of pancreatic cancer developed between 2009 and 2013. The multivariate-adjusted HRs (95% CIs) for incident pancreatic cancer in groups 2, 3, and 4 were 1.05 (0.76 to 1.45), 1.28 (0.91 to 1.80), and 1.57 (1.00 to 2.46), respectively (p for trend=0.025). The HR (95% CI) of former smokers showed a dose-response relationship in the unadjusted model, but did not show a statistically significant association in the multivariate-adjusted model. The HR (95% CI) of current smokers showed a dose-response relationship in both the unadjusted (p for trend=0.020) and multivariate-adjusted models (p for trend=0.050). CONCLUSIONS The risk of developing pancreatic cancer was higher in current smokers status than in former smokers among Korean men, indicating that smoking cessation may have a protective effect.

Purpose: There are few reports on outcomes following surgical repair of recurrent rectal prolapse. The purpose of this study was to examine surgical outcomes for recurrent rectal prolapse. Methods: We conducted a multicenter retrospective study of patients who underwent surgery for recurrent rectal prolapse. This study used data collected by the Korean Anorectal Physiology and Pelvic Floor Disorder Study Group. Results: A total of 166 patients who underwent surgery for recurrent rectal prolapse were registered retrospectively between 2011 and 2016 in 8 referral hospitals. Among them, 153 patients were finally enrolled, excluding 13 patients who were not followed up postoperatively. Median follow-up duration was 40 months (range, 0.2–129.3 months). Methods of surgical repair for recurrent rectal prolapse included perineal approach (n = 96) and abdominal approach (n = 57). Postoperative complications occurred in 16 patients (10.5%). There was no significant difference in complication rate between perineal and abdominal approach groups. While patients who underwent the perineal approach were older and more fragile, patients who underwent the abdominal approach had longer operation time and admission days (P < 0.05). Overall, 29 patients (19.0%) showed re-recurrence after surgery. Among variables, none affected the re-recurrence. Conclusion For the recurrent rectal prolapse, the perineal approach is used for the old and fragile patients. The postoperative complications and re-recurrence rate between perineal and abdominal approach were not different significantly. No factor including surgical method affected re-recurrence for recurrent rectal prolapse.

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.

BACKGROUND: This study was conducted to develop a pre-discharge group education program for liver transplant patients, and to contribute to improving the knowledge and performance of self-management after discharge. METHODS: This investigation was a methodological study consisting of analysis, design, development, operation, and evaluation stages. RESULTS: The constituent items of the pre-discharge group education program for liver transplant patients include medication management, infection management, emergency management, outpatient management, complication management, nutrition management, exercise management, wound and drainage management, disability registration, and sex life. The pre-discharge group education program for liver transplant patients was conducted once a week, 30 minutes for the coordinator, 40 minutes for the clinical nurse specialist (including 10 minutes for wound and drainage management), 30 minutes for pharmacists, 20 minutes for the nutritionist, and 20 minutes for social workers. Additionally, the contents of the lecture announced by PowerPoint were made the same as the booklet. The overall knowledge level before and 3 months after the pre-discharge group education program for liver transplantation patients increased significantly from 17.32±1.53 to 19.74±0.89. At the 3-month time point, the overall compliance was 77.39±3.04 out of 80. Overall satisfaction was 9.32±0.93 on a scale of 10. Finally, the need for medication was the highest at 3 months. CONCLUSIONS: It is expected that this program can be utilized in the clinical field as an effective nursing education intervention.
Compliance ; Drainage ; Education* ; Education, Nursing ; Emergencies ; Humans ; Liver Transplantation ; Liver* ; Methods ; Nurse Clinicians ; Nutritionists ; Outpatients ; Pamphlets ; Pharmacists ; Self Care ; Social Work ; Social Workers ; Wounds and Injuries