Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

Purpose: Potassium voltage-gated channel subfamily Q member 2 (KCNQ2)-related epilepsy, caused by mutations in the KCNQ2 gene, encompasses a spectrum of epileptic phenotypes, ranging from self-limited epilepsy to severe developmental and epileptic encephalopathy (DEE). Although the mutational background of these disorders has been characterized, predicting outcomes based solely on genetic variants remains challenging. Methods: This multicenter observational study investigated the clinical features, genotype-phenotype correlations, and comorbidities in pediatric patients with KCNQ2-related epilepsy in Korea. Conducted across three tertiary hospitals, the study enrolled 20 pediatric patients with genetically confirmed KCNQ2-related epilepsy. Data were collected from medical records, including demographic information, age at seizure onset, types of seizures, comorbidities, and treatment history. Results: Of the 20 patients enrolled, nine had self-limited epilepsy, while 11 had DEE. Missense mutations were more prevalent in the DEE group, whereas truncation mutations were associated with milder forms of epilepsy. Although 75% of cases achieved effective seizure control, 55% of patients exhibited comorbidities such as intellectual disability and neuropsychiatric disorders. Genotype-phenotype correlations revealed variability in clinical outcomes, with specific mutations in similar regions resulting in different phenotypes. Conclusion This study highlights the complexity of KCNQ2-related epilepsy, demonstrating that genotype-phenotype correlations are not straightforward and may be influenced by genetic modifiers, environmental factors, or dominant negative effects. While seizure control often improves, neurodevelopmental challenges may persist, underscoring the need for therapeutic approaches that address both seizure management and developmental support. Further research into the relevant non-genetic factors is essential to enhance the understanding and treatment of KCNQ2-related epilepsy.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Background: Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea. Methods: Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold. Results: Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001). Conclusions The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

Purpose: Potassium voltage-gated channel subfamily Q member 2 (KCNQ2)-related epilepsy, caused by mutations in the KCNQ2 gene, encompasses a spectrum of epileptic phenotypes, ranging from self-limited epilepsy to severe developmental and epileptic encephalopathy (DEE). Although the mutational background of these disorders has been characterized, predicting outcomes based solely on genetic variants remains challenging. Methods: This multicenter observational study investigated the clinical features, genotype-phenotype correlations, and comorbidities in pediatric patients with KCNQ2-related epilepsy in Korea. Conducted across three tertiary hospitals, the study enrolled 20 pediatric patients with genetically confirmed KCNQ2-related epilepsy. Data were collected from medical records, including demographic information, age at seizure onset, types of seizures, comorbidities, and treatment history. Results: Of the 20 patients enrolled, nine had self-limited epilepsy, while 11 had DEE. Missense mutations were more prevalent in the DEE group, whereas truncation mutations were associated with milder forms of epilepsy. Although 75% of cases achieved effective seizure control, 55% of patients exhibited comorbidities such as intellectual disability and neuropsychiatric disorders. Genotype-phenotype correlations revealed variability in clinical outcomes, with specific mutations in similar regions resulting in different phenotypes. Conclusion This study highlights the complexity of KCNQ2-related epilepsy, demonstrating that genotype-phenotype correlations are not straightforward and may be influenced by genetic modifiers, environmental factors, or dominant negative effects. While seizure control often improves, neurodevelopmental challenges may persist, underscoring the need for therapeutic approaches that address both seizure management and developmental support. Further research into the relevant non-genetic factors is essential to enhance the understanding and treatment of KCNQ2-related epilepsy.

Purpose: Potassium voltage-gated channel subfamily Q member 2 (KCNQ2)-related epilepsy, caused by mutations in the KCNQ2 gene, encompasses a spectrum of epileptic phenotypes, ranging from self-limited epilepsy to severe developmental and epileptic encephalopathy (DEE). Although the mutational background of these disorders has been characterized, predicting outcomes based solely on genetic variants remains challenging. Methods: This multicenter observational study investigated the clinical features, genotype-phenotype correlations, and comorbidities in pediatric patients with KCNQ2-related epilepsy in Korea. Conducted across three tertiary hospitals, the study enrolled 20 pediatric patients with genetically confirmed KCNQ2-related epilepsy. Data were collected from medical records, including demographic information, age at seizure onset, types of seizures, comorbidities, and treatment history. Results: Of the 20 patients enrolled, nine had self-limited epilepsy, while 11 had DEE. Missense mutations were more prevalent in the DEE group, whereas truncation mutations were associated with milder forms of epilepsy. Although 75% of cases achieved effective seizure control, 55% of patients exhibited comorbidities such as intellectual disability and neuropsychiatric disorders. Genotype-phenotype correlations revealed variability in clinical outcomes, with specific mutations in similar regions resulting in different phenotypes. Conclusion This study highlights the complexity of KCNQ2-related epilepsy, demonstrating that genotype-phenotype correlations are not straightforward and may be influenced by genetic modifiers, environmental factors, or dominant negative effects. While seizure control often improves, neurodevelopmental challenges may persist, underscoring the need for therapeutic approaches that address both seizure management and developmental support. Further research into the relevant non-genetic factors is essential to enhance the understanding and treatment of KCNQ2-related epilepsy.

Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.