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: To investigate the effects of honey-based oral care on the oral health of patients with stroke undergoing rehabilitation. Methods: In this randomized controlled trial, 44 stroke patients from a tertiary hospital’s rehabilitation ward were assigned to receive either honey-based oral care or normal saline, with treatments administered twice daily for 2 weeks. The study, conducted from November 2021 to August 2022, employed a double-blind method, blinding both participants and evaluators to treatment allocations. The key outcomes measured included oral status, dental plaque index (DPI), and xerostomia. The final analysis included 13 patients in the experimental group and 16 in the control group. Results: The intervention significantly changed the oral status, DPI, and xerostomia between the groups. The experimental group showed significantly improved oral status (Z = −4.63, p < .001), DPI (Z = −4.58, p < .001), and xerostomia (t = −6.33, p < .001) compared with the control group. The experimental group showed significant improvements in oral status (Z = −3.27, p = .001), DPI (Z = −3.19, p = .001), and xerostomia (t = 7.37, p < .001) after the intervention, confirming the efficacy of honey-based oral care. Conclusions Honey-based oral care effectively improves oral status and xerostomia, and reduces DPI in patients with stroke.

Purpose: To investigate the effects of honey-based oral care on the oral health of patients with stroke undergoing rehabilitation. Methods: In this randomized controlled trial, 44 stroke patients from a tertiary hospital’s rehabilitation ward were assigned to receive either honey-based oral care or normal saline, with treatments administered twice daily for 2 weeks. The study, conducted from November 2021 to August 2022, employed a double-blind method, blinding both participants and evaluators to treatment allocations. The key outcomes measured included oral status, dental plaque index (DPI), and xerostomia. The final analysis included 13 patients in the experimental group and 16 in the control group. Results: The intervention significantly changed the oral status, DPI, and xerostomia between the groups. The experimental group showed significantly improved oral status (Z = −4.63, p < .001), DPI (Z = −4.58, p < .001), and xerostomia (t = −6.33, p < .001) compared with the control group. The experimental group showed significant improvements in oral status (Z = −3.27, p = .001), DPI (Z = −3.19, p = .001), and xerostomia (t = 7.37, p < .001) after the intervention, confirming the efficacy of honey-based oral care. Conclusions Honey-based oral care effectively improves oral status and xerostomia, and reduces DPI in patients with stroke.

Purpose: To investigate the effects of honey-based oral care on the oral health of patients with stroke undergoing rehabilitation. Methods: In this randomized controlled trial, 44 stroke patients from a tertiary hospital’s rehabilitation ward were assigned to receive either honey-based oral care or normal saline, with treatments administered twice daily for 2 weeks. The study, conducted from November 2021 to August 2022, employed a double-blind method, blinding both participants and evaluators to treatment allocations. The key outcomes measured included oral status, dental plaque index (DPI), and xerostomia. The final analysis included 13 patients in the experimental group and 16 in the control group. Results: The intervention significantly changed the oral status, DPI, and xerostomia between the groups. The experimental group showed significantly improved oral status (Z = −4.63, p < .001), DPI (Z = −4.58, p < .001), and xerostomia (t = −6.33, p < .001) compared with the control group. The experimental group showed significant improvements in oral status (Z = −3.27, p = .001), DPI (Z = −3.19, p = .001), and xerostomia (t = 7.37, p < .001) after the intervention, confirming the efficacy of honey-based oral care. Conclusions Honey-based oral care effectively improves oral status and xerostomia, and reduces DPI in patients with stroke.

Purpose: Despite decreased prevalence of tuberculosis, the incidence of the diseases associated with nontuberculous mycobacteria (NTM) has been increasing in South Korea and around the world. The present retrospective study was conducted to determine longitudinal changes in the epidemiology and distribution of NTM over 13 years at a tertiary care hospital in Korea. Materials and Methods: We retrospectively analyzed data on Mycobacterium species over 13 years (January 2007 to December 2019) by utilizing the laboratory information system. Mycobacterium species were identified using biochemical tests and PCR-restriction fragment length polymorphism and Mycobacteria GenoBlot assays. Results: After excluding duplicates from the initial pool of 17996 mycobacterial isolates, 7674 strains were analyzed and 2984 (38.9%) NTM were isolated. The proportion of NTM continuously increased over the 13-year period, from 17.0% in 2007 to 57.5% in 2019. Among the NTM isolates, the most common species were Mycobacterium intracellulare (50.6%), M. avium (18.3%), M. fortuitum complex (4.9%), M. abscessus (4.5%), M. gordonae (3.3%), M. kansasii (1.1%), M. chelonae (1.0%), and M. massiliense (0.9%).In patients over the age of 70 years, the proportion of NTM among the isolates increased from 26.6% in 2007 to 62.0% in 2019, and that of M. intracellulare isolates among the NTM increased from 13.9% (11/79) in 2007 to 37.4% (175/468) in 2019. Conclusion The number of NTM isolates continuously increased over the study period, and the increase in the proportion of M. intracellulare in patients aged over 70 years was notable.

Purpose: : This study aimed to present the incidence of pressure ulcers and identify different associated factors according to the time of occurrence of pressure ulcers in intensive care unit (ICU) patients. Methods: : The participants were 313 patients who reported pressure ulcers among 2,908 patients in ICUs at a large tertiary hospital in Gyeonggi-do. Among them, 220 patients (70.3%) had a pressure ulcer before admission, and 93 patients (29.7%) reported newly developed pressure ulcers after admission to the ICU. Data were collected between August 2018 and April 2019. Along with the time of occurrence and characteristics of pressure ulcers, diverse associated factors were gathered through electronic medical records. Data were analyzed using descriptive statistics, independent t-tests, and χ2-tests. Results: : Different risk factors associated with pressure ulcers in ICU patients according to the time of occurrence were main diagnosis, score of acute physiology and chronic health evaluation, score of Richmond agitation sedation scale, level of consciousness, administered sedatives, use of a ventilator, insertion of a feeding tube, and the duration of fasting period. Conclusion : Based on the results of this study, healthcare providers, especially ICU nurses, should try to detect early signs and symptoms of pressure ulcers, taking into account the derived factors associated with pressure ulcers in ICU patients. Practical intervention programs and strategies considering the factors associated with pressure ulcers must be developed to prevent and alleviate such ulcers in ICU patients in the future.