Background and Objectives: Evidence remains limited on the real-world prescription of very low-dose oral anticoagulation among frail patients with atrial fibrillation (AF). We described the practice patterns, effectiveness, and safety of very low-dose edoxaban (15 mg once daily). Methods: Patients with AF prescribed edoxaban 15 mg once daily in 2 tertiary hospitals between 2016 and September 2022 were included. Baseline clinical characteristics and clinical outcomes of interest were thromboembolic and bleeding events. Results: A total of 674 patients were included (mean age 78.3±9.1, 49.7% aged ≥80 years, 49.3% women, median follow-up 1.0±1.2 years). Mean CHA 2 DS 2 -VASc score was 3.9±1.6, and the modified HAS-BLED score was 2.0±1.1. Between 2016 and 2022, the number of very lowdose edoxaban prescriptions increased. The main reasons for the prescription of very lowdose were low body weight (55.5% below 60 kg), anaemia (62.8%), chronic kidney disease (40.2%), active cancer (15.3%), concomitant anti-platelet use (26.7%), and prior major bleeding (19.7%). During a median follow-up duration of 8 (interquartile range 3–16) months, overall thromboembolic and bleeding events occurred in 16 (2.3%) and 88 (13.1%) patients, respectively. Compared to the expected event rates on the established risk scoring systems, patients receiving very low-dose edoxaban demonstrated a 61% reduction in ischemic stroke, a 68% reduction of ischemic stroke/transient ischemic attack/systemic embolism, whereas a 49% increase in major bleeding. Conclusions The prescription of very low-dose edoxaban was increased over time, attributable to various clinical factors. The use of very low-dose edoxaban reduced the expected risk of thromboembolic events.

Background and Objectives: Evidence remains limited on the real-world prescription of very low-dose oral anticoagulation among frail patients with atrial fibrillation (AF). We described the practice patterns, effectiveness, and safety of very low-dose edoxaban (15 mg once daily). Methods: Patients with AF prescribed edoxaban 15 mg once daily in 2 tertiary hospitals between 2016 and September 2022 were included. Baseline clinical characteristics and clinical outcomes of interest were thromboembolic and bleeding events. Results: A total of 674 patients were included (mean age 78.3±9.1, 49.7% aged ≥80 years, 49.3% women, median follow-up 1.0±1.2 years). Mean CHA 2 DS 2 -VASc score was 3.9±1.6, and the modified HAS-BLED score was 2.0±1.1. Between 2016 and 2022, the number of very lowdose edoxaban prescriptions increased. The main reasons for the prescription of very lowdose were low body weight (55.5% below 60 kg), anaemia (62.8%), chronic kidney disease (40.2%), active cancer (15.3%), concomitant anti-platelet use (26.7%), and prior major bleeding (19.7%). During a median follow-up duration of 8 (interquartile range 3–16) months, overall thromboembolic and bleeding events occurred in 16 (2.3%) and 88 (13.1%) patients, respectively. Compared to the expected event rates on the established risk scoring systems, patients receiving very low-dose edoxaban demonstrated a 61% reduction in ischemic stroke, a 68% reduction of ischemic stroke/transient ischemic attack/systemic embolism, whereas a 49% increase in major bleeding. Conclusions The prescription of very low-dose edoxaban was increased over time, attributable to various clinical factors. The use of very low-dose edoxaban reduced the expected risk of thromboembolic events.

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 evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Background and Objectives: Evidence remains limited on the real-world prescription of very low-dose oral anticoagulation among frail patients with atrial fibrillation (AF). We described the practice patterns, effectiveness, and safety of very low-dose edoxaban (15 mg once daily). Methods: Patients with AF prescribed edoxaban 15 mg once daily in 2 tertiary hospitals between 2016 and September 2022 were included. Baseline clinical characteristics and clinical outcomes of interest were thromboembolic and bleeding events. Results: A total of 674 patients were included (mean age 78.3±9.1, 49.7% aged ≥80 years, 49.3% women, median follow-up 1.0±1.2 years). Mean CHA 2 DS 2 -VASc score was 3.9±1.6, and the modified HAS-BLED score was 2.0±1.1. Between 2016 and 2022, the number of very lowdose edoxaban prescriptions increased. The main reasons for the prescription of very lowdose were low body weight (55.5% below 60 kg), anaemia (62.8%), chronic kidney disease (40.2%), active cancer (15.3%), concomitant anti-platelet use (26.7%), and prior major bleeding (19.7%). During a median follow-up duration of 8 (interquartile range 3–16) months, overall thromboembolic and bleeding events occurred in 16 (2.3%) and 88 (13.1%) patients, respectively. Compared to the expected event rates on the established risk scoring systems, patients receiving very low-dose edoxaban demonstrated a 61% reduction in ischemic stroke, a 68% reduction of ischemic stroke/transient ischemic attack/systemic embolism, whereas a 49% increase in major bleeding. Conclusions The prescription of very low-dose edoxaban was increased over time, attributable to various clinical factors. The use of very low-dose edoxaban reduced the expected risk of thromboembolic events.

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 evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.