This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

Background and Objectives: Hypertrophic cardiomyopathy (HCM) increases the risk of sudden cardiac death. Despite international guidelines for sudden death prevention and implantable cardiac defibrillator (ICD) use, clinical practices vary. This study assessed the practices and opinions of Korean cardiologists on HCM management, focusing on ICD use and reimbursement policies. Methods: A structured electronic survey was distributed to members of the Korean Society of Cardiology, covering HCM management, ICD preferences, and reimbursement policies. Results: A total of 119 cardiologists participated, with most being arrhythmia specialists (56.3%) and university professors (81.5%). Cardiac magnetic resonance imaging and genetic testing were underutilized (30.3% and 11.8%, respectively). Beta-blocker-refractory premature ventricular complexes were primarily managed with continued medical therapy (77.3%). ICD preferences varied by physician specialty and patient age, with subcutaneous ICDs preferred in younger patients and dual-chamber ICDs in older patients. Nearly half of respondents believed ICD reimbursement criteria required revision. Conclusions Significant variability in HCM management exists among Korean cardiologists, influenced by restrictive reimbursement criteria rather than clinical guidelines. Updated reimbursement policies and improved access to advanced diagnostics are needed to align with international standards and optimize patient care.

Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development. Methods: At baseline and after an 8-week intervention, we performed bioimpedance analysis measurements, the Timed Up and Go, Tinetti, and grip strength tests, and gait analysis in 217 healthy individuals and 276 patients with CKD. Demographic and clinical information was collected, including underlying diseases and medications, laboratory tests, and quality of life satisfaction surveys. Gait analysis was performed using skeleton data, which involved acquiring three-dimensional skeleton data of a walker using a single Kinect sensor. The performance of an artificial intelligence-based classification model in distinguishing between healthy individuals and those with CKD was then investigated. Simultaneously, inertia measurement unit analysis was conducted using measurements taken from the wrist and waist. Results: Most subjects received a health intervention via an app, and their gait was assessed for improvements after an 8-week period. Incidents such as falls, fractures, hospitalizations, and deaths will be investigated in years 1 and 3. Conclusion: This study confirmed that the gaits of healthy individuals and CKD patients were different, and the effect of the 8-week app-based health intervention will be analyzed. The study will yield important baseline data for creating digital therapeutics for CKD patients’ diet/exercise in the future.

Purpose: : This study aimed to identify factors influencing the quality of life of family caregivers of intensive care unit (ICU) patients. Methods: : We conducted a study using a cross-sectional design. The study involved 109 family caregivers of ICU patients at a university-affiliated hospital in Gyeonggi-do, South Korea. Data were collected through self-report questionnaires between July 2020 and April 2021 and analyzed using descriptive statistics, independent t-tests, one-way ANOVA, Pearson’s correlation coefficients, and multiple regression analysis. Results: : The study revealed significant differences in quality of life based on economic status (F=11.63, p ＜ .001), cohabitation with patients (t=-2.04, p=.044), sleep duration after patient’s admission to the ICU (t=-2.48, p =.025), and subjective health status (F=30.06, p ＜.001). There were significant negative correlations observed between quality of life and post-traumatic stress symptoms (r=-.38, p ＜.001) as well as caregiver burden (r=-.46, p ＜.001). Factors affecting quality of life were subjective health status, economic status, and caregiver burden (adj. R2 =0.52, F=15.64, p ＜.001). Conclusion : These findings underscore the need to develop and implement intervention programs tailored to the health conditions and economic status of family caregivers, with a focus on alleviating caregiver burden. Such initiatives are essential to ultimately improve the quality of life for family caregivers of ICU patients.

The purpose of this study was to investigate the effects of activities of daily living, depression, and social support on the meaning in life in older adults using services under the long-term care insurance system. Methods: This was a cross-sectional study. A total of 121 older adults were recruited from seven senior daycare centers in two cities in Korea. Data collection was performed between September and October 2023. The data were analyzed using descriptive statistics, t-tests, ANOVA, Pearson correlation coefficients, and hierarchical multiple regression analysis. Results: The results of this study showed that social support (β=.45, p<.001) and depression (β=-.16, p=.048) were significantly affecting factors to the meaning in life in older adults using services under the long-term care insurance system. Conclusion: Efforts to enhance social support and reduce depressive symptoms are critical to improve the meaning in life of older adults utilizing services under the long-term care insurance system. Specifically, it is necessary to closely identify older adults’ depressive symptoms and further strengthen social support, including policy support and individualized support from healthcare professionals in the facilities to improve older adults’ meaning in life.

Purpose: The expression of major histocompatibility complex class I (MHC I) has previously been reported to be negatively associated with estrogen receptor (ER) expression. Furthermore, MHC I expression, level of tumor-infiltrating lymphocytes (TILs), and expression of interferon (IFN) mediator MxA are positively associated with one another in human breast cancers. This study aimed to investigate the mechanisms of association of MHC I with ER and IFN signaling. Materials and Methods: The human leukocyte antigen (HLA)-ABC protein expression was analyzed in breast cancer cell lines. The expressions of HLA-A and MxA mRNAs were analyzed in MCF-7 cells in Gene Expression Omnibus (GEO) data. ER and HLA-ABC expressions, Ki-67 labeling index and TIL levels in tumor tissue were also analyzed in ER+/ human epidermal growth factor receptor 2 (HER2)- breast cancer patients who randomly received either neoadjuvant chemotherapy or estrogen modulator treatment followed by resection. Results: HLA-ABC protein expression was decreased after β-estradiol treatment or hESR-GFP transfection and increased after fulvestrant or IFN-γ treatment in cell lines. In GEO data, HLA-A and MxA expression was increased after ESR1 shRNA transfection. In patients, ER Allred score was significantly lower and the HLA-ABC expression, TIL levels, and Ki-67 were significantly higher in the estrogen modulator treated group than the chemotherapy treated group. Conclusion MHC I expression and TIL levels might be affected by ER pathway modulation and IFN treatment. Further studies elucidating the mechanism of MHC I regulation could suggest a way to boost TIL influx in cancer in a clinical setting.

Purpose: : The study aimed to identify relationship among intensive care unit (ICU) nurses’ critical thinking disposition, medication error risk level of high-alert medication, and medication safety competency, as well as the factors affecting medication safety competency. Methods: : The participants were 266 ICU nurses of one higher-tier general hospital and one general hospital in Province. The data were collected using structured self-administered questionnaire from August 10 to August 31, 2021. Measurements included the critical thinking disposition questionnaire, nurses’s knowledge of high-alert medication questionnaire, the medication safety competency scale. Data were analyzed using hierarchical multiple regressions using SPSS/WIN 28.0. Results: : In the multiple regression analysis, the medication safety competence has a statistically significant correlation with the working department, the critical thinking disposition, and medication error risk level of high-alert medication. Conclusion : Based on the results of this study, it is suggested to develop and apply an educational strategy that can strengthen the knowledge and skills of critical thinking disposition and medication error risk level of high-alert medication to improve the ICU nurse’s medication safety competency.