Background: Atherosclerotic cardiovascular disease (ASCVD) is a major health concern, and lipoprotein(a) (Lp(a)) is an independent risk factor. However, there is limited evidence regarding Lp(a) and the risk of ASCVD in Asian populations. This study aimed to assess the predictive value of changes in coronary artery calcification (CAC) for ASCVD risk associated with Lp(a) level. Methods: Participants (n=2,750) were grouped according to their Lp(a) levels, and the association between Lp(a) and CAC progression was examined. CAC progression was defined as the occurrence of incident CAC or a difference ≥2.5 between the square root (√) of baseline and follow-up coronary artery calcium scores (CACSs) (Δ√transformed CACS). To adjust for differences in follow-up periods, Δ√transformed CACS was divided by the follow- up period (in years). Results: Over an average follow-up of 3.07 years, 18.98% of participants experienced CAC progression. Those with disease progression had notably higher Lp(a) levels. Higher Lp(a) tertiles correlated with increased baseline and follow-up CACS, CAC progression (%), and Δ√transformed CACS. Even after adjustment, higher Lp(a) levels were associated with CAC progression. However, annualized Δ√transformed CACS analysis yielded no significant results. Conclusion This study demonstrated an association between elevated Lp(a) levels and CAC progression in a general population without ASCVD. However, longer-term follow-up studies are needed to obtain meaningful results regarding CAC progression. Further research is necessary to utilize Lp(a) level as a predictor of cardiovascular disease and to establish clinically relevant thresholds specific to the Korean population.

Background: Atherosclerotic cardiovascular disease (ASCVD) is a major health concern, and lipoprotein(a) (Lp(a)) is an independent risk factor. However, there is limited evidence regarding Lp(a) and the risk of ASCVD in Asian populations. This study aimed to assess the predictive value of changes in coronary artery calcification (CAC) for ASCVD risk associated with Lp(a) level. Methods: Participants (n=2,750) were grouped according to their Lp(a) levels, and the association between Lp(a) and CAC progression was examined. CAC progression was defined as the occurrence of incident CAC or a difference ≥2.5 between the square root (√) of baseline and follow-up coronary artery calcium scores (CACSs) (Δ√transformed CACS). To adjust for differences in follow-up periods, Δ√transformed CACS was divided by the follow- up period (in years). Results: Over an average follow-up of 3.07 years, 18.98% of participants experienced CAC progression. Those with disease progression had notably higher Lp(a) levels. Higher Lp(a) tertiles correlated with increased baseline and follow-up CACS, CAC progression (%), and Δ√transformed CACS. Even after adjustment, higher Lp(a) levels were associated with CAC progression. However, annualized Δ√transformed CACS analysis yielded no significant results. Conclusion This study demonstrated an association between elevated Lp(a) levels and CAC progression in a general population without ASCVD. However, longer-term follow-up studies are needed to obtain meaningful results regarding CAC progression. Further research is necessary to utilize Lp(a) level as a predictor of cardiovascular disease and to establish clinically relevant thresholds specific to the Korean population.

Purpose: This study aimed to examine the levels of clinical decision-making ability, role conflict, and nursing work performance ability and to identify factors influencing nursing work performance ability among emergency room nurses. Methods: This study emploved a descriptive correlational research design. A total of 137 emergency room nurses, were participated. Data were collected at three tertiary hospitals located in city B, from June 1 to August 25, 2024. The collected data were analyzed using descriptive statistics, independent t-test, one-way ANOVA, Scheffé test, Pearson’s correlation, and multiple regression analysis using IBM SPSS 29.0 program. Results: The average score of clinical decision-making ability was 3.31, while role conflict and nursing work performance ability both had an average score of 3.77. Factors influencing nursing work performance ability included clinical decision-making ability, role conflict, and annual salary, which explained 20.0% of the variance in nursing work performance ability. Conclusion Clinical decision-making ability and role conflict significantly impact nursing work performance among emergency room nurses. Strategies to enhance clinical decision-making skills and mitigate role conflict should be developed at the organizational level. Additionally, further research is needed to on appropriate compensation systems and institutional measures to enhance nursing work performance.

Purpose: This study aimed to examine the levels of clinical decision-making ability, role conflict, and nursing work performance ability and to identify factors influencing nursing work performance ability among emergency room nurses. Methods: This study emploved a descriptive correlational research design. A total of 137 emergency room nurses, were participated. Data were collected at three tertiary hospitals located in city B, from June 1 to August 25, 2024. The collected data were analyzed using descriptive statistics, independent t-test, one-way ANOVA, Scheffé test, Pearson’s correlation, and multiple regression analysis using IBM SPSS 29.0 program. Results: The average score of clinical decision-making ability was 3.31, while role conflict and nursing work performance ability both had an average score of 3.77. Factors influencing nursing work performance ability included clinical decision-making ability, role conflict, and annual salary, which explained 20.0% of the variance in nursing work performance ability. Conclusion Clinical decision-making ability and role conflict significantly impact nursing work performance among emergency room nurses. Strategies to enhance clinical decision-making skills and mitigate role conflict should be developed at the organizational level. Additionally, further research is needed to on appropriate compensation systems and institutional measures to enhance nursing work performance.

Purpose: This study aimed to characterize the clinical patterns and severity of brain injury in neonates who survived extracorporeal membrane oxygenation (ECMO) therapy for acute respiratory failure during the neonatal period, to evaluate their short-term neurodevelopmental outcomes, and to identify the factors associated with these outcomes. Methods: We retrospectively reviewed the medical records of neonates who survived ECMO between 2018 and 2024. Based on brain magnetic resonance imaging (MRI) findings, the patients were classified into two groups: no/mild and moderate/severe brain injury. Neurodevelopmental outcomes were assessed at 12–40 months of age using the Bayley Scale of Infant Development II/III and/or the Korean Developmental Screening Test. Results: Among the 19 neonates included in the study, 18 (94.7%) showed varying degrees of brain injury on MRI (mild: 12, moderate: 1, severe: 5). Neonates with moderate/severe brain injury had significantly longer durations of ECMO support and extended durations of mechanical ventilation and were more likely to receive continuous renal replacement therapy than those with no or mild injury. Developmental delay was identified in 36.8% of survivors and was significantly associated with prolonged mechanical ventilation, longer neonatal intensive care unit stays, and a higher incidence of seizures. Conclusion Brain injury is frequently observed on MRI in neonates treated with ECMO. However, its direct association with adverse neurodevelopmental outcomes is not definitive. Since MRI findings alone cannot predict developmental outcomes, clinical and environmental factors should be integrated into prognostic assessments.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study aimed to characterize the clinical patterns and severity of brain injury in neonates who survived extracorporeal membrane oxygenation (ECMO) therapy for acute respiratory failure during the neonatal period, to evaluate their short-term neurodevelopmental outcomes, and to identify the factors associated with these outcomes. Methods: We retrospectively reviewed the medical records of neonates who survived ECMO between 2018 and 2024. Based on brain magnetic resonance imaging (MRI) findings, the patients were classified into two groups: no/mild and moderate/severe brain injury. Neurodevelopmental outcomes were assessed at 12–40 months of age using the Bayley Scale of Infant Development II/III and/or the Korean Developmental Screening Test. Results: Among the 19 neonates included in the study, 18 (94.7%) showed varying degrees of brain injury on MRI (mild: 12, moderate: 1, severe: 5). Neonates with moderate/severe brain injury had significantly longer durations of ECMO support and extended durations of mechanical ventilation and were more likely to receive continuous renal replacement therapy than those with no or mild injury. Developmental delay was identified in 36.8% of survivors and was significantly associated with prolonged mechanical ventilation, longer neonatal intensive care unit stays, and a higher incidence of seizures. Conclusion Brain injury is frequently observed on MRI in neonates treated with ECMO. However, its direct association with adverse neurodevelopmental outcomes is not definitive. Since MRI findings alone cannot predict developmental outcomes, clinical and environmental factors should be integrated into prognostic assessments.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study aimed to characterize the clinical patterns and severity of brain injury in neonates who survived extracorporeal membrane oxygenation (ECMO) therapy for acute respiratory failure during the neonatal period, to evaluate their short-term neurodevelopmental outcomes, and to identify the factors associated with these outcomes. Methods: We retrospectively reviewed the medical records of neonates who survived ECMO between 2018 and 2024. Based on brain magnetic resonance imaging (MRI) findings, the patients were classified into two groups: no/mild and moderate/severe brain injury. Neurodevelopmental outcomes were assessed at 12–40 months of age using the Bayley Scale of Infant Development II/III and/or the Korean Developmental Screening Test. Results: Among the 19 neonates included in the study, 18 (94.7%) showed varying degrees of brain injury on MRI (mild: 12, moderate: 1, severe: 5). Neonates with moderate/severe brain injury had significantly longer durations of ECMO support and extended durations of mechanical ventilation and were more likely to receive continuous renal replacement therapy than those with no or mild injury. Developmental delay was identified in 36.8% of survivors and was significantly associated with prolonged mechanical ventilation, longer neonatal intensive care unit stays, and a higher incidence of seizures. Conclusion Brain injury is frequently observed on MRI in neonates treated with ECMO. However, its direct association with adverse neurodevelopmental outcomes is not definitive. Since MRI findings alone cannot predict developmental outcomes, clinical and environmental factors should be integrated into prognostic assessments.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.