Objectives: This study examined the changes in the characteristics of high-risk suicide groups in South Korea before and after the COVID-19 pandemic using the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) cohort. Methods: The K-COMPASS is a longitudinal cohort study that started in 2015. The participants included suicide attempters and individuals with suicidal ideation from various hospitals and mental health centers in South Korea. This study compared the sociodemographic and psychiatric characteristics of 800 participants from the first cohort (2015–2019) with 511 participants from the second and third cohorts (2019–2024). Data were collected through structured interviews and validated scales. Results: The second and third cohort participants were younger, had a higher proportion of females, and exhibited more severe psychiatric symptoms and higher suicidal risk than the first cohort. The prevalence of physical illnesses decreased, while the use of psychiatric medications and the severity of mental health issues increased. In addition, significant sociodemographic changes were observed, such as higher educational levels and urban residency. Conclusion Significant shifts in the characteristics of high-risk suicide groups were observed during the COVID-19 pandemic, highlighting the need for targeted mental health interventions focusing on younger individuals and females to prevent suicide in high-risk groups.

Objectives: This study examined the changes in the characteristics of high-risk suicide groups in South Korea before and after the COVID-19 pandemic using the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) cohort. Methods: The K-COMPASS is a longitudinal cohort study that started in 2015. The participants included suicide attempters and individuals with suicidal ideation from various hospitals and mental health centers in South Korea. This study compared the sociodemographic and psychiatric characteristics of 800 participants from the first cohort (2015–2019) with 511 participants from the second and third cohorts (2019–2024). Data were collected through structured interviews and validated scales. Results: The second and third cohort participants were younger, had a higher proportion of females, and exhibited more severe psychiatric symptoms and higher suicidal risk than the first cohort. The prevalence of physical illnesses decreased, while the use of psychiatric medications and the severity of mental health issues increased. In addition, significant sociodemographic changes were observed, such as higher educational levels and urban residency. Conclusion Significant shifts in the characteristics of high-risk suicide groups were observed during the COVID-19 pandemic, highlighting the need for targeted mental health interventions focusing on younger individuals and females to prevent suicide in high-risk groups.

Objectives: This study examined the changes in the characteristics of high-risk suicide groups in South Korea before and after the COVID-19 pandemic using the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) cohort. Methods: The K-COMPASS is a longitudinal cohort study that started in 2015. The participants included suicide attempters and individuals with suicidal ideation from various hospitals and mental health centers in South Korea. This study compared the sociodemographic and psychiatric characteristics of 800 participants from the first cohort (2015–2019) with 511 participants from the second and third cohorts (2019–2024). Data were collected through structured interviews and validated scales. Results: The second and third cohort participants were younger, had a higher proportion of females, and exhibited more severe psychiatric symptoms and higher suicidal risk than the first cohort. The prevalence of physical illnesses decreased, while the use of psychiatric medications and the severity of mental health issues increased. In addition, significant sociodemographic changes were observed, such as higher educational levels and urban residency. Conclusion Significant shifts in the characteristics of high-risk suicide groups were observed during the COVID-19 pandemic, highlighting the need for targeted mental health interventions focusing on younger individuals and females to prevent suicide in high-risk groups.

Objectives: This study examined the changes in the characteristics of high-risk suicide groups in South Korea before and after the COVID-19 pandemic using the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) cohort. Methods: The K-COMPASS is a longitudinal cohort study that started in 2015. The participants included suicide attempters and individuals with suicidal ideation from various hospitals and mental health centers in South Korea. This study compared the sociodemographic and psychiatric characteristics of 800 participants from the first cohort (2015–2019) with 511 participants from the second and third cohorts (2019–2024). Data were collected through structured interviews and validated scales. Results: The second and third cohort participants were younger, had a higher proportion of females, and exhibited more severe psychiatric symptoms and higher suicidal risk than the first cohort. The prevalence of physical illnesses decreased, while the use of psychiatric medications and the severity of mental health issues increased. In addition, significant sociodemographic changes were observed, such as higher educational levels and urban residency. Conclusion Significant shifts in the characteristics of high-risk suicide groups were observed during the COVID-19 pandemic, highlighting the need for targeted mental health interventions focusing on younger individuals and females to prevent suicide in high-risk groups.

Background: It is well known that a large number of patients with diabetes also have dyslipidemia, which significantly increases the risk of cardiovascular disease (CVD). This study aimed to evaluate the efficacy and safety of combination drugs consisting of metformin and atorvastatin, widely used as therapeutic agents for diabetes and dyslipidemia. Methods: This randomized, double-blind, placebo-controlled, parallel-group and phase III multicenter study included adults with glycosylated hemoglobin (HbA1c) levels >7.0% and <10.0%, low-density lipoprotein cholesterol (LDL-C) >100 and <250 mg/dL. One hundred eighty-five eligible subjects were randomized to the combination group (metformin+atorvastatin), metformin group (metformin+atorvastatin placebo), and atorvastatin group (atorvastatin+metformin placebo). The primary efficacy endpoints were the percent changes in HbA1c and LDL-C levels from baseline at the end of the treatment. Results: After 16 weeks of treatment compared to baseline, HbA1c showed a significant difference of 0.94% compared to the atorvastatin group in the combination group (0.35% vs. −0.58%, respectively; P<0.0001), whereas the proportion of patients with increased HbA1c was also 62% and 15%, respectively, showing a significant difference (P<0.001). The combination group also showed a significant decrease in LDL-C levels compared to the metformin group (−55.20% vs. −7.69%, P<0.001) without previously unknown adverse drug events. Conclusion The addition of atorvastatin to metformin improved HbA1c and LDL-C levels to a significant extent compared to metformin or atorvastatin alone in diabetes and dyslipidemia patients. This study also suggested metformin’s preventive effect on the glucose-elevating potential of atorvastatin in patients with type 2 diabetes mellitus and dyslipidemia, insufficiently controlled with exercise and diet. Metformin and atorvastatin combination might be an effective treatment in reducing the CVD risk in patients with both diabetes and dyslipidemia because of its lowering effect on LDL-C and glucose.

Background: Despite a plethora of research on the topic, there is still no solid evidence that pharmacological treatment actually reduces the risk of suicide in patients with mental illness.In this study, we aimed to assess the effect of psychotropic medications on suicidal ideation in patients with major depressive disorder (MDD) and bipolar disorder (BPD) in two age groups: less than 25 years and 25 years and older. Methods: We analyzed 312 patients with mood disorders with current suicidal thoughts or recent suicide attempts. We followed the participants from baseline for 6 months and assessed changes in suicidal ideation with Columbia-Suicide Severity Rating Scale (C-SSRS).The effect of psychotropic drug administration on suicidal ideation over time was analyzed using a linear mixed model. Results: In patients aged 25 years and older with mood disorders, suicidal ideation was more severe when using psychotropic drugs than when not using them. However, suicidal ideation decreased rapidly over time. The time-dependent reduction in suicidal ideation was accelerated when using antidepressants and sedatives/hypnotics in adult MDD, and when using mood stabilizers in adult BPD. However, this effect was not observed in participants aged less than 25 years. Conclusion Adequate psychotropic medication may reduce suicidal ideation in patients with mood disorders aged 25 years and older. Additional research on psychotropic drugs is needed to effectively reduce the risk of suicide among children and adolescents with mood disorders.

Objective: Suicide is a significant public health issue, with South Korea having the highest suicide rate among Organisation for Economic Cooperation and Development countries. This study aimed to develop clinical guidelines for suicide prevention in psychiatric patients in Korea using the ADAPTE methodology. Methods: The development process involved a comprehensive review of literature, expert consultations, and consensus-building using the Nominal Group Technique and Delphi method. The guidelines focus on evidence-based psychiatric treatments, including both pharmacological and non-pharmacological approaches, tailored to the Korean context. Key findings underscoring the need for standardized treatment protocols for patients with major psychiatric disorders, including bipolar disorder, major depressive disorder, and schizophrenia. Results: The guidelines incorporate treatments like lithium, clozapine, atypical antipsychotics, electroconvulsive therapy, and cognitive behavioral therapy, which have shown effectiveness in suicide prevention. Applicability and acceptability within Korea’s healthcare system were addressed, ensuring feasibility given the country’s medical insurance coverage and accessibility. The guidelines were validated through expert reviews and Delphi rounds, achieving consensus on the final recommendations. Conclusion The developed guidelines provide a structured, evidence-based approach to reducing suicide rates among psychiatric patients in Korea. Future research will focus on expanding these guidelines to include screening protocols for high-risk groups.

Background and Objectives: There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV).This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients. Methods: We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features. Results: Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435(60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001).Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model’s performance. Conclusions Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.

Background: As an indicator of electro-mechanical coupling, electromechanical window (EMW) can be used to predict fatal ventricular arrhythmias. We investigated the additive effect of EMW on the prediction of fatal ventricular arrhythmias in high-risk patients. Methods: We included patients who had implantable cardioverter-defibrillator (ICD) implanted for primary or secondary prevention. The event group was defined as those who received an appropriate ICD therapy. We acquired echocardiograms at ICD implantation and follow-up. The EMW was calculated as the difference between the interval from QRS onset to aortic valve closure and QT interval from the electrocardiogram embedded in the continuous wave doppler image. We evaluated the predictive value of EMW for predicting fatal ventricular arrhythmia. Results: Of 245 patients (67.2 ± 12.8 years, 63.7% men), the event group was 20.0%. EMW at baseline (EMW-Baseline) and follow-up (EMW-FU) was significantly different between event and control groups. After adjustment, both EMW-Baseline (odds ratio [OR]adjust 1.02 [1.01– 1.03], P = 0.004) and EMW-FU (ORadjust 1.06 [1.04–1.07], P < 0.001) remained as significant predictors for fatal arrhythmic events. Adding EMW-Baseline significantly improved the discriminating ability of the multivariable model including clinical variables (area under the curve [AUC] 0.77 [0.70–0.84] vs. AUC 0.72 [0.64–0.80], P = 0.004), while a univariable model using EMW-FU alone showed the best performance among models (AUC 0.87 [0.81– 0.94], P = 0.060 against model with clinical variables; P = 0.030 against model with clinical variables and EMW-Baseline). Conclusion The EMW could effectively predict severe ventricular arrhythmia in ICD implanted patients. This finding supports the importance of incorporating the electro-mechanical coupling index into the clinical practice for predicting future fatal arrhythmia events.

Background: Although respiratory tract infection is one of the most important factors triggering acute exacerbation of chronic obstructive pulmonary disease (AE-COPD), limited data are available to suggest an epidemiologic pattern of microbiology in South Korea. Methods: A multicenter observational study was conducted between January 2015 and December 2018 across 28 hospitals in South Korea. Adult patients with moderate-to-severe acute exacerbations of COPD were eligible to participate in the present study. The participants underwent all conventional tests to identify etiology of microbial pathogenesis. The primary outcome was the percentage of different microbiological pathogens causing AE-COPD. A comparative microbiological analysis of the patients with overlapping asthma–COPD (ACO) and pure COPD was performed. Results: We included 1,186 patients with AE-COPD. Patients with pure COPD constituted 87.9% and those with ACO accounted for 12.1%. Nearly half of the patients used an inhaled corticosteroid-containing regimen and one-fifth used systemic corticosteroids. Respiratory pathogens were found in 55.3% of all such patients. Bacteria and viruses were detected in 33% and 33.2%, respectively. Bacterial and viral coinfections were found in 10.9%. The most frequently detected bacteria were Pseudomonas aeruginosa (9.8%), and the most frequently detected virus was influenza A (10.4%). Multiple bacterial infections were more likely to appear in ACO than in pure COPD (8.3% vs. 3.6%, p=0.016). Conclusion Distinct microbiological patterns were identified in patients with moderate-to-severe AE-COPD in South Korea. These findings may improve evidence-based management of patients with AE-COPD and represent the basis for further studies investigating infectious pathogens in patients with COPD.