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: Complete revascularization has demonstrated better outcomes in patients with acute myocardial infarction (AMI) and multivessel disease. However, in the case of left main (LM) culprit lesion AMI with multivessel disease, there is limited evidence to suggest that complete revascularization is better. Methods: We reviewed 16,831 patients in the Korea Acute Myocardial Infarction Registry who were treated from July 2016 to June 2020, and 399 patients were enrolled with LM culprit lesion AMI treated with percutaneous coronary intervention. We categorized the patients as those treated with complete revascularization (n=295) or incomplete revascularization (n=104). The study endpoint was major adverse cardiac and cerebrovascular events (MACCE), a composite of all-cause death, myocardial infarction, ischemia-driven revascularization, stent thrombosis, and stroke. We performed propensity score matching (PSM) and analyzed the incidence of MACCE at 1 year. Results: After PSM, the two groups were well balanced. There was no significant difference between the two groups in MACCE at 1 year (12.1% vs. 15.2%; hazard ratio, 1.28; 95% confidence interval, 0.60–2.74; p=0.524) after PSM. The components of MACCE and major bleeding were also not significantly different. Conclusion There was no significant difference in clinical outcomes between the groups treated with complete or incomplete revascularization for LM culprit lesion AMI with multivessel disease.

Background and Objectives: The risk profiles, procedural characteristics, and clinical outcomes for women undergoing bifurcation percutaneous coronary intervention (PCI) are not well defined compared to those in men. Methods: COronary BIfurcation Stenting III (COBIS III) is a multicenter, real-world registry of 2,648 patients with bifurcation lesions treated with second-generation drug-eluting stents.We compared the angiographic and procedural characteristics and clinical outcomes based on sex. The primary outcome was 5-year target lesion failure (TLF), a composite of cardiac death, myocardial infarction, and target lesion revascularization. Results: Women (n=635, 24%) were older, had hypertension and diabetes more often, and had smaller main vessel and side branch reference diameters than men. The pre- and post-PCI angiographic percentage diameter stenoses of the main vessel and side branch were comparable between women and men. There were no differences in procedural characteristics between the sexes. Women and men had a similar risk of TLF (6.3% vs. 7.1%, p=0.63) as well as its individual components and sex was not an independent predictor of TLF. This finding was consistent in the left main and 2 stenting subgroups. Conclusions In patients undergoing bifurcation PCI, sex was not an independent predictor of adverse outcome.

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

Background: Periprosthetic fracture (PPF) is a troublesome complication as it utilizes substantial healthcare resources. Recent studies about the epidemiology of PPF after total knee arthroplasty (TKA) are still lacking, and there is limited national-level analysis focusing on the comorbid chronic conditions as risk factors of PPF. This study used national registry data from South Korea and aimed to investigate the epidemiology of PPF following TKA between 2010 and 2020 and identify which comorbidities contributed to the risk of PPF. Methods: Using Health Insurance Review and Assessment (HIRA) service data in South Korea, the incidence of PPF after TKA between 2010 and 2020 was evaluated and stratified by age and sex. Medical comorbidities were evaluated as possible risk factors for PPF using Cox regression analysis. Results: PPF occurred in 14,429 patients, accounting for 2.37% of total TKA patients. The prevalence of PPF by sex was 2.50% in women and 1.64% in men. The PPF rate was 2.82% in under 60 years, 2.25% in 60 to 69 years, 2.42% in 70 to 79 years, 2.29% in 80 to 89 years, and 2.12% in over 90 years. Among 17 analyzed comorbidities, 11 were found to be associated with PPF after TKA. Severe liver disease (hazard ratio [HR], 1.303), hemiplegia (HR, 1.244), and dementia (HR, 1.206) were the top 3 risk factors.Although osteoporosis, pulmonary disease, peptic ulcer, and diabetes showed relatively low HRs than these top 3 factors, the incidence rates were higher. Conclusions PPF occurred in 2.37% of TKA patients in South Korea from 2010 to 2020. PPF rate was higher in women. To prevent PPF after TKA, proper patient management and education should be emphasized, particularly in patients with severe liver disease, hemiplegia, and dementia.

Background: Periprosthetic fracture (PPF) is a troublesome complication as it utilizes substantial healthcare resources. Recent studies about the epidemiology of PPF after total knee arthroplasty (TKA) are still lacking, and there is limited national-level analysis focusing on the comorbid chronic conditions as risk factors of PPF. This study used national registry data from South Korea and aimed to investigate the epidemiology of PPF following TKA between 2010 and 2020 and identify which comorbidities contributed to the risk of PPF. Methods: Using Health Insurance Review and Assessment (HIRA) service data in South Korea, the incidence of PPF after TKA between 2010 and 2020 was evaluated and stratified by age and sex. Medical comorbidities were evaluated as possible risk factors for PPF using Cox regression analysis. Results: PPF occurred in 14,429 patients, accounting for 2.37% of total TKA patients. The prevalence of PPF by sex was 2.50% in women and 1.64% in men. The PPF rate was 2.82% in under 60 years, 2.25% in 60 to 69 years, 2.42% in 70 to 79 years, 2.29% in 80 to 89 years, and 2.12% in over 90 years. Among 17 analyzed comorbidities, 11 were found to be associated with PPF after TKA. Severe liver disease (hazard ratio [HR], 1.303), hemiplegia (HR, 1.244), and dementia (HR, 1.206) were the top 3 risk factors.Although osteoporosis, pulmonary disease, peptic ulcer, and diabetes showed relatively low HRs than these top 3 factors, the incidence rates were higher. Conclusions PPF occurred in 2.37% of TKA patients in South Korea from 2010 to 2020. PPF rate was higher in women. To prevent PPF after TKA, proper patient management and education should be emphasized, particularly in patients with severe liver disease, hemiplegia, and dementia.

Background: Periprosthetic fracture (PPF) is a troublesome complication as it utilizes substantial healthcare resources. Recent studies about the epidemiology of PPF after total knee arthroplasty (TKA) are still lacking, and there is limited national-level analysis focusing on the comorbid chronic conditions as risk factors of PPF. This study used national registry data from South Korea and aimed to investigate the epidemiology of PPF following TKA between 2010 and 2020 and identify which comorbidities contributed to the risk of PPF. Methods: Using Health Insurance Review and Assessment (HIRA) service data in South Korea, the incidence of PPF after TKA between 2010 and 2020 was evaluated and stratified by age and sex. Medical comorbidities were evaluated as possible risk factors for PPF using Cox regression analysis. Results: PPF occurred in 14,429 patients, accounting for 2.37% of total TKA patients. The prevalence of PPF by sex was 2.50% in women and 1.64% in men. The PPF rate was 2.82% in under 60 years, 2.25% in 60 to 69 years, 2.42% in 70 to 79 years, 2.29% in 80 to 89 years, and 2.12% in over 90 years. Among 17 analyzed comorbidities, 11 were found to be associated with PPF after TKA. Severe liver disease (hazard ratio [HR], 1.303), hemiplegia (HR, 1.244), and dementia (HR, 1.206) were the top 3 risk factors.Although osteoporosis, pulmonary disease, peptic ulcer, and diabetes showed relatively low HRs than these top 3 factors, the incidence rates were higher. Conclusions PPF occurred in 2.37% of TKA patients in South Korea from 2010 to 2020. PPF rate was higher in women. To prevent PPF after TKA, proper patient management and education should be emphasized, particularly in patients with severe liver disease, hemiplegia, and dementia.

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.