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: 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/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

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/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.