Kim, Darae

Abstract Aims To assess the performance of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm in identifying patients with moderate to severe aortic stenosis (AS) in an Asian cohort from a tertiary care centre. Methods and results We identified a randomly selected patients ≥60 years old who underwent echocardiography and ECG within in 31 days between 2012 and 2021 at the Samsung Medical Center in Korea. Patients with previous cardiac surgery, prosthetic valves, or pacemakers were excluded. The AI-ECG model, originally developed and validated by Mayo Clinic in the USA, was applied without fine-tuning. Performance metrics, including the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, were calculated to compare AI-ECG predictions with TTE-confirmed AS status. Among 5425 patients, 1095 had moderate to severe AS, and 4330 age- and sex-matched patients without AS were included as controls. The AI-ECG model achieved an AUC of 0.85 (95% CI: 0.84–0.87) in detecting moderate to severe AS. Sensitivity, specificity, PPV, NPV, and accuracy were 0.83, 0.65, 0.37, 0.94, and 68.29%, respectively. The model's performance was consistent across various age and sex subgroups, with sensitivity increasing in older patients. Conclusion The AI-ECG model developed in the USA demonstrated comparable performance in detecting moderate to severe AS in an Asian cohort compared with its original validation population. These findings highlight the potential utility of AI-ECG as a non-invasive screening tool for AS across diverse patient populations.

Abstract Aims To investigate whether left arterial reservoir strain (LASr) could predict new-onset atrial fibrillation (NOAF) in patients with light-chain-type cardiac amyloidosis (ALCA). Methods and results This study enrolled 427 patients with CA from two tertiary centres between 2005 and 2019. LASr was measured using a vendor-independent analysis programme. The primary outcome was NOAF. A total of 287 patients with ALCA were included [median age 63.0 (56.0–70.0) years, 53.3% male]. The median LASr was 13.9% (10.5–20.8%). During the median follow-up of 0.85 years, AF occurred in 34 patients (11.8%). In the receiver operating characteristics curve analysis, the optimal cut-off of LASr for predicting NOAF was 14.4%. Patients with LASr ≤14.4% had a higher risk of NOAF than those with LASr >14.4% (18.1% vs. 5.1%, P < 0.010). In the multivariate analysis adjusting for confounding factors, including left arterial volume index and left ventricular global longitudinal strain (LV-GLS), higher LASr (%) was independently associated with lower risk for NOAF [adjusted hazard ratio (aHR): 0.936, 95% confidence interval (95% CI): 0.879–0.997, P = 0.039]. Furthermore, LASr ≤14.4% was an independent predictor for NOAF (aHR: 3.370, 95% CI: 1.337–8.492, P = 0.010). This remained true after accounting for all-cause death as a competing risk. Compared with Model 1 (LV-GLS) and Model 2 (LV-GLS plus LAVI), Model 3, including LASr showed a better reclassification ability for predicting NOAF (net reclassification index = 0.735, P < 0.001 compared with Model 1; net reclassification index = 0.514, P = 0.003 compared with Model 2). Conclusion LASr was an independent predictor of NOAF in patients with ALCA.