Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Prostate cancer (PCa) typically occurs in older adults and generally has a relatively long survival period, making it challenging to demonstrate the effectiveness of treatment. In 2012, medical guidelines recommending against PCa screening using prostate-specific antigen (PSA) became widespread, as randomized controlled trials had not consistently shown survival benefits.Current concepts: In South Korea, awareness of PCa has historically been very low, and the rate of PSA test screening has consequently been lower than that in other major developed countries. Additionally, PSA screening has tended to occur within a limited framework accessible only to individuals already aware of the test. Given that PCa is a high-risk cancer predominantly affecting older men in South Korea, unconditionally following medical guidelines from the 2010s that took a negative stance toward PSA screening may exacerbate health inequalities, increase metastatic cancer incidence, and elevate cancer-specific mortality, particularly in light of Korea’s rapidly aging population. Thus, recent shifts in clinical guidelines in the early 2020s, advocating the proactive implementation of PSA-based screening tests as announced by various countries, are significant. These shifts provide additional support for policy responses aligned with Korea’s epidemiological context. Discussion and conclusion: We advocate for increased public discourse on national PCa screening policies. Such policies should be rationally designed to reduce the incidence of high-risk PCa while minimizing the potential harms associated with overdiagnosis and overtreatment.

Background: Barriers to treatment with intravenous thrombolysis (IVT) for patients with acute ischemic stroke (AIS) in South Korea remain incompletely characterized. We analyze a nationwide prospective cohort to determine patient-level features associated with delayed presentation and non-treatment of potential IVT-eligible patients. Methods: We identified consecutive patients with AIS from 01/2011 to 08/2023 from a multicenter and prospective acute stroke registry in Korea. Patients were defined as IVT candidates if they presented within 4.5 hours from the last known well, had no lab evidence of coagulopathy, and had National Institute of Health Stroke Scale (NIHSS) ≥ 4. Multivariable generalized linear mixed regression models were used to investigate the associations between their characteristics and the IVT candidates or the use of IVT among the candidates. Results: Among 84,103 AIS patients, 41.0% were female, with a mean age of 69 ± 13 years and presentation NIHSS of 4 [interquartile range, 1–8]. Out of these patients, 13,757 (16.4%) were eligible for IVT, of whom 8,179 (59.5%) received IVT. Female sex (adjusted risk ratio [RR], 0.90; 95% confidence interval [CI], 0.86–0.94) and lower years of education (adjusted RR, 0.90; 95% CI, 0.84–0.97 for 0–3 years, compared to ≥ 13 years) were associated with a decreased likelihood of presenting as eligible for IVT after AIS; meanwhile, young age (adjusted RR, 1.12; 95% CI, 1.01–1.24 for ≤ 44 years, compared to 75–84 years) was associated with an increased likelihood of being an IVT candidate. Among those who were eligible for IVT, only age was significantly associated with the use of IVT (adjusted RR, 1.09; 95% CI, 1.03–1.16 for age 65–74 and adjusted RR, 0.83; 95% CI, 0.76–0.90 for ≥ 85 years, respectively). Conclusion Most patients with AIS present outside IVT eligibility in South Korea, and only 60% of eligible patients were ultimately treated. We identified increased age, female sex and lower education as key features on which to focus interventions for improving IVT utilization.

Background: Immunocompromised (IC) pediatric patients are at increased risk of severe acute respiratory syndrome coronavirus 2 infection, but the viral kinetics and seroimmunologic response in pediatric IC patients are not fully understood. Methods: From April to June 2022, a prospective cohort study was conducted. IC pediatric patients hospitalized for coronavirus disease 2019 (COVID-19) were enrolled. Serial saliva swab and serum specimens were subjected to reverse transcription polymerase chain reaction assays with mutation sequencing, viral culture, anti-spike-protein, anti-nucleocapsid antibody assays, plaque reduction neutralization test (PRNT) and multiplex cytokine assays. Results: Eleven IC children were evaluated. Their COVID-19 symptoms resolved promptly (median, 2.5 days; interquartile range, 2.0–4.3). Saliva swab specimens contained lower viral loads than nasopharyngeal swabs (P = 0.008). All cases were BA.2 infection, and 45.5% tested negative within 14 days by saliva swab from symptom onset. Eight (72.7%) showed a time-dependent increase in BA.2 PRNT titers, followed by rapid waning. Multiplex cytokine assays revealed that monocyte/macrophage activation and Th 1 responses were comparable to those of non-IC adults. Activation of interleukin (IL)-1Ra and IL-6 was brief, and IL-17A was suppressed. Activated interferon (IFN)-γ and IL-18/IL-1F4 signals were observed. Conclusion IC pediatric patients rapidly recovered from COVID-19 with low viral loads.Antibody response was limited, but cytokine analysis suggested an enhanced IFN-γ- and IL-18-mediated immune response without excessive activation of inflammatory cascades. To validate our observation, immune cell-based functional studies need to be conducted among IC and non-IC children.

Background: Barriers to treatment with intravenous thrombolysis (IVT) for patients with acute ischemic stroke (AIS) in South Korea remain incompletely characterized. We analyze a nationwide prospective cohort to determine patient-level features associated with delayed presentation and non-treatment of potential IVT-eligible patients. Methods: We identified consecutive patients with AIS from 01/2011 to 08/2023 from a multicenter and prospective acute stroke registry in Korea. Patients were defined as IVT candidates if they presented within 4.5 hours from the last known well, had no lab evidence of coagulopathy, and had National Institute of Health Stroke Scale (NIHSS) ≥ 4. Multivariable generalized linear mixed regression models were used to investigate the associations between their characteristics and the IVT candidates or the use of IVT among the candidates. Results: Among 84,103 AIS patients, 41.0% were female, with a mean age of 69 ± 13 years and presentation NIHSS of 4 [interquartile range, 1–8]. Out of these patients, 13,757 (16.4%) were eligible for IVT, of whom 8,179 (59.5%) received IVT. Female sex (adjusted risk ratio [RR], 0.90; 95% confidence interval [CI], 0.86–0.94) and lower years of education (adjusted RR, 0.90; 95% CI, 0.84–0.97 for 0–3 years, compared to ≥ 13 years) were associated with a decreased likelihood of presenting as eligible for IVT after AIS; meanwhile, young age (adjusted RR, 1.12; 95% CI, 1.01–1.24 for ≤ 44 years, compared to 75–84 years) was associated with an increased likelihood of being an IVT candidate. Among those who were eligible for IVT, only age was significantly associated with the use of IVT (adjusted RR, 1.09; 95% CI, 1.03–1.16 for age 65–74 and adjusted RR, 0.83; 95% CI, 0.76–0.90 for ≥ 85 years, respectively). Conclusion Most patients with AIS present outside IVT eligibility in South Korea, and only 60% of eligible patients were ultimately treated. We identified increased age, female sex and lower education as key features on which to focus interventions for improving IVT utilization.

Background: Immunocompromised (IC) pediatric patients are at increased risk of severe acute respiratory syndrome coronavirus 2 infection, but the viral kinetics and seroimmunologic response in pediatric IC patients are not fully understood. Methods: From April to June 2022, a prospective cohort study was conducted. IC pediatric patients hospitalized for coronavirus disease 2019 (COVID-19) were enrolled. Serial saliva swab and serum specimens were subjected to reverse transcription polymerase chain reaction assays with mutation sequencing, viral culture, anti-spike-protein, anti-nucleocapsid antibody assays, plaque reduction neutralization test (PRNT) and multiplex cytokine assays. Results: Eleven IC children were evaluated. Their COVID-19 symptoms resolved promptly (median, 2.5 days; interquartile range, 2.0–4.3). Saliva swab specimens contained lower viral loads than nasopharyngeal swabs (P = 0.008). All cases were BA.2 infection, and 45.5% tested negative within 14 days by saliva swab from symptom onset. Eight (72.7%) showed a time-dependent increase in BA.2 PRNT titers, followed by rapid waning. Multiplex cytokine assays revealed that monocyte/macrophage activation and Th 1 responses were comparable to those of non-IC adults. Activation of interleukin (IL)-1Ra and IL-6 was brief, and IL-17A was suppressed. Activated interferon (IFN)-γ and IL-18/IL-1F4 signals were observed. Conclusion IC pediatric patients rapidly recovered from COVID-19 with low viral loads.Antibody response was limited, but cytokine analysis suggested an enhanced IFN-γ- and IL-18-mediated immune response without excessive activation of inflammatory cascades. To validate our observation, immune cell-based functional studies need to be conducted among IC and non-IC children.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Prostate cancer (PCa) typically occurs in older adults and generally has a relatively long survival period, making it challenging to demonstrate the effectiveness of treatment. In 2012, medical guidelines recommending against PCa screening using prostate-specific antigen (PSA) became widespread, as randomized controlled trials had not consistently shown survival benefits.Current concepts: In South Korea, awareness of PCa has historically been very low, and the rate of PSA test screening has consequently been lower than that in other major developed countries. Additionally, PSA screening has tended to occur within a limited framework accessible only to individuals already aware of the test. Given that PCa is a high-risk cancer predominantly affecting older men in South Korea, unconditionally following medical guidelines from the 2010s that took a negative stance toward PSA screening may exacerbate health inequalities, increase metastatic cancer incidence, and elevate cancer-specific mortality, particularly in light of Korea’s rapidly aging population. Thus, recent shifts in clinical guidelines in the early 2020s, advocating the proactive implementation of PSA-based screening tests as announced by various countries, are significant. These shifts provide additional support for policy responses aligned with Korea’s epidemiological context. Discussion and conclusion: We advocate for increased public discourse on national PCa screening policies. Such policies should be rationally designed to reduce the incidence of high-risk PCa while minimizing the potential harms associated with overdiagnosis and overtreatment.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.