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)

Purpose: Optimal treatment for stage IIIA/N2 non–small cell lung cancer (NSCLC) is controversial. We aimed to assess the efficacy and safety of adjuvant pembrolizumab for stage IIIA/N2 NSCLC completely resected after neoadjuvant concurrent chemoradiation therapy (CCRT). Materials and Methods: In this open-label, single-center, single-arm phase 2 trial, patients with stage IIIA/N2 NSCLC received adjuvant pembrolizumab for up to 2 years after complete resection following neoadjuvant CCRT. The primary endpoint was disease-free survival (DFS). Secondary endpoints included overall survival (OS) and safety. As an exploratory biomarker analysis, we evaluated the proliferative response of blood CD39+PD-1+CD8+ T cells using fold changes in the percentage of proliferating Ki-67+ cells from days 1 to 7 of cycle 1 (Ki-67D7/D1). Results: Between October 2017 and October 2018, 37 patients were enrolled. Twelve (32%) and three (8%) patients harbored EGFR and ALK alterations, respectively. Of 34 patients with programmed cell death ligand 1 assessment, 21 (62%), nine (26%), and four (12%) had a tumor proportion score of < 1%, 1%-50%, and ≥ 50%, respectively. The median follow-up was 71 months. The median DFS was 22.4 months in the overall population, with a 5-year DFS rate of 29%. The OS rate was 86% at 2 years and 76% at 5 years. Patients with tumor recurrence within 6 months had a significantly lower Ki-67D7/D1 among CD39+PD-1+CD8+ T cells than those without (p=0.036). No new safety signals were identified. Conclusion Adjuvant pembrolizumab may offer durable disease control in a subset of stage IIIA/N2 NSCLC patients after neoadjuvant CCRT and surgery.

Background: Coronary artery disease patients undergoing percutaneous coronary intervention (PCI) often exhibit reduced left ventricular ejection fraction (LVEF). However, the impact of LV dysfunction status in conjunction with platelet reactivity on clinical outcomes has not been previously investigated. Methods: From the multicenter PTRG-DES (Platelet function and genoType-Related long-term prognosis in DES-treated patients) consortium, the patients were classified as preserved-EF (PEF: LVEF ≥ 50%) and reduced-EF (REF: LVEF< 5 0%) group by echocardiography. Platelet reactivity was measured using VerifyNow P2Y 12 assay and high platelet reactivity (HPR) was defined as PRU ≥ 252. The major adverse cardiac and cerebrovascular events (MACCEs) were a composite of death, myocardial infarction, stent thrombosis and stroke at 5 years after PCI. Major bleeding was defined as Bleeding Academic Research Consortium bleeding types 3–5. Results: A total of 13,160 patients from PTRG-DES, 9,319 (79.6%) patients with the results of both PRU and LVEF were analyzed. The incidence of MACCE and major bleeding was higher in REF group as compared with PEF group (MACCEs: hazard ratio [HR] 2.17, P < 0.001, 95% confidence interval [CI] 1.85–2.55; major bleeding: HR 1.78, P < 0.001, 95% CI 1.39–2.78).The highest rate of MACCEs was found in patients with REF and HPR, and the difference between the groups was statistically significant (HR 3.14 in REF(+)/HPR(+) vs. PEF(+)/HPR(-) group,P <0.01, 95% CI 2.51–3.91). The frequency of major bleeding was not associated with the HPR in either group. Conclusion LV dysfunction was associated with an increased incidence of MACCEs and major bleeding in patients who underwent PCI. The HPR status further exhibited significant increase of MACCEs in patients with LV dysfunction in a large, real-world registry.Trial Registration: ClinicalTrials.gov Identifier: NCT04734028

In May 2023, the Committee of Clinical Practice Guidelines of the Korean Diabetes Association published the revised clinical practice guidelines for Korean adults with diabetes and prediabetes. We incorporated the latest clinical research findings through a comprehensive systematic literature review and applied them in a manner suitable for the Korean population. These guidelines are designed for all healthcare providers nationwide, including physicians, diabetes experts, and certified diabetes educators who manage patients with diabetes or individuals at risk of developing diabetes. Based on recent changes in international guidelines and the results of a Korean epidemiological study, the recommended age for diabetes screening has been lowered. In collaboration with the relevant Korean medical societies, recently revised guidelines for managing hypertension and dyslipidemia in patients with diabetes have been incorporated into this guideline. An abridgment containing practical information on patient education and systematic management in the clinic was published separately.

Background/Aims: Chronic hepatitis C (CHC) is the second leading cause of liver-related mortality and is more prevalent in the elderly population in Korea. Decisions to initiate treatment and selection of proper antiviral agents may be challenging among elderly patients due to relevant comorbidities, comedications, and drug-drug interaction (DDI). It may be helpful to understand the current demographic status and comorbidities of CHC patients in the country. Methods: Patients aged ≥ 18 years and diagnosed with CHC (KCD-7 code B18.2) were extracted from the Korean Health Insurance Review & Assessment Service database in 2018. Data on comorbidities and comedications were assessed and potential DDIs were analyzed. Results: A total of 50,476 patients with CHC, with a mean age of 60.3 years and 46.7% male patients were identified. The proportion of patients with cirrhosis, hepatocellular carcinoma, and liver transplantation was 6.0%, 4.1%, and 0.3%, respectively and 37.2% of patients were more than 65 years of age. The three most common comorbidities were diseases of the digestive system (83.7%), respiratory system (58.2%), and musculoskeletal system and connective tissue (57.6%). The three most common comedications were analgesics (91.6%), gastrointestinal agents (85%), and antibacterials (80.3%). Lipid-lowering agents and anticonvulsants were prescribed in 28.5% and 14.8% of patients. Rate of potential DDI for contraindication was 2.2%, 13.1%, and 15.6% with sofosbuvir/velpatasvir, ledipasvir/sofosbuvir, and glecaprevir/pibrentasvir. Conclusions With the increasing age of patients with CHC, comorbidity, comedication, and potential DDI should be considered when choosing antivirals in Korea. Sofosbuvir-based regimens showed favorable DDI profiles among Korean patients.