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: 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)

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.