Background: There are limited long-term clinical outcome data comparing first-generation (1G) versus second-generation (2G) drug-eluting stents (DES) in patients with single-vessel disease (SVD). We sought to compare the efficacy and safety of 1G- and 2G-DES in SVD patients who underwent successful percutaneous coronary intervention (PCI) over 10 years of clinical follow-up. Methods: A total of 2,312 consecutive patients with SVD who underwent PCI with 1G-DES (paclitaxel- or sirolimus-eluting stents, n = 811) or 2G-DES (zotarolimus [Endeavor, Endeavor Resolute]- or everolimus-eluting stents [Promus Element, Xience], n = 1,082) were enrolled.After propensity score matching, 2 matched groups (538 pairs) were generated. Study endpoints included individual and composite clinical outcomes through 10 years. Results: During the 10-year follow-up, the 2G-DES group had lower incidences of myocardial infarction (MI; hazard ratio [HR], 0.44; 95% confidence interval [CI], 0.22–0.92; P = 0.028) and target lesion revascularization (TLR; HR, 0.54; 95% CI, 0.32–0.91; P = 0.021) than the 1G-DES group. Rates of total death, cardiac death, non-target vessel revascularization, stroke, major adverse cardiac events, and major adverse cardiac and cerebrovascular events were similar between groups. Conclusions In our single-center, all-comer registry, 2G-DES were associated with lower incidence rates of MI and TLR compared to 1G-DES in SVD patients over 10 years of followup, indicating a safer and more durable effect than 1G-DES.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Background and Objectives: Ultimaster™, a third-generation sirolimus-eluting stent using biodegradable polymer, has been introduced to overcome long term adverse vascular events, such as restenosis or stent thrombosis. In the present study, we aimed to evaluate the 12-month clinical outcomes of Ultimaster™ stents in Korean patients with coronary artery disease. Methods: This study is a multicenter, prospective, observational registry across 12 hospitals. To reflect real-world clinical evidence, non-selective subtypes of patients and lesions were included in this study. The study end point was target lesion failure (TLF) (the composite of cardiac death, target vessel myocardial infarction [MI], and target lesion revascularization [TLR]) at 12-month clinical follow up. Results: A total of 576 patients were enrolled between November 2016 and May 2021. Most of the patients were male (76.5%), with a mean age of 66.0±11.2 years. Among the included patients, 40.1% had diabetes mellitus (DM) and 67.9% had acute coronary syndrome (ACS).At 12 months, the incidence of TLF was 4.1%. The incidence of cardiac death was 1.5%, MI was 1.0%, TLR was 2.7%, and stent thrombosis was 0.6%. In subgroup analysis based on the presence of ACS, DM, hypertension, dyslipidemia, or bifurcation, there were no major differences in the incidence of the primary endpoint. Conclusions The present registry shows that Ultimaster™ stent is safe and effective for routine real-world clinical practice in non-selective Korean patients, having a low rate of adverse events at least up to 12 months.

PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predictive model using electronic medical records (EMRs) and machine learning and to compare the performance of this model with traditional statistical methods. MATERIALS AND METHODS: In this study, a total of available 8454 patients who had no history of diabetes and were treated at the cardiovascular center of Korea University Guro Hospital were enrolled. All subjects completed 5 years of follow up. The prevalence of T2DM during follow up was 4.78% (404/8454). A total of 28 variables were extracted from the EMRs. In order to verify the cross-validation test according to the prediction model, logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbor (KNN) algorithm models were generated. The LR model was considered as the existing statistical analysis method. RESULTS: All predictive models maintained a change within the standard deviation of area under the curve (AUC) < 0.01 in the analysis after a 10-fold cross-validation test. Among all predictive models, the LR learning model showed the highest prediction performance, with an AUC of 0.78. However, compared to the LR model, the LDA, QDA, and KNN models did not show a statistically significant difference. CONCLUSION: We successfully developed and verified a T2DM prediction system using machine learning and an EMR database, and it predicted the 5-year occurrence of T2DM similarly to with a traditional prediction model. In further study, it is necessary to apply and verify the prediction model through clinical research.
Area Under Curve ; Diabetes Mellitus ; Diabetes Mellitus, Type 2 ; Electronic Health Records ; Follow-Up Studies ; Humans ; Korea ; Learning ; Logistic Models ; Machine Learning ; Methods ; Prevalence

OBJECTIVES: Intracoronary injection of acetylcholine (Ach) has been shown to induce significant coronary artery spasm (CAS) in patients with vasospastic angina. Clinical significance and angiographic characteristics of patients with ischemic electrocardiogram (ECG) changes during the Ach provocation test are not clarified yet. METHODS: A total 4,418 consecutive patients underwent coronary angiography with Ach provocation tests from 2004 to 2012 were enrolled. Ischemic ECG changes were defined as transient ST-segment depression or elevation ( > 1 mm) and T inversion with/without chest pain. Finally, a total 2,293 patients (28.5% of total subjects) proven CAS were enrolled for this study. RESULTS: A total 119 patients (5.2%) showed ECG changes during Ach provocation tests. The baseline clinical and procedural characteristics are well balanced between the two groups. Ischemic ECG change group showed more frequent chest pain, higher incidence of baseline spasm, severe vasospasm, multi-vessel involvement, and more diffuse spasm ( > 30 mm) than those without ischemic ECG changes. At 5 years, the incidences of death, major adverse cardiac events (MACE) and major adverse cardiac and cerebral events (MACCE) were higher in the ischemic ECG change group despite of optimal medical therapy. CONCLUSIONS: The patients with ischemic ECG changes during Ach provocation tests were associated with more frequent chest pain, baseline spasm, diffuse, severe and multi-vessel spasm than patients without ischemic ECG changes. At 5-years, the incidences of death, MACE and MACCE were higher in the ischemic ECG change group, suggesting more intensive medical therapy with close clinical follow up will be required.
Acetylcholine ; Chest Pain ; Coronary Angiography ; Coronary Vessels ; Depression ; Electrocardiography ; Follow-Up Studies ; Humans ; Incidence ; Spasm