On, Young Keun

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.

Background— Mutations in genes including SCN5A encoding the α-subunit of the cardiac sodium channel (hNav1.5) cause Brugada syndrome via altered function of cardiac ion channels, but more than two-thirds of Brugada syndrome remains pathogenetically elusive. T-tubules and sarcoplasmic reticulum are essential in excitation of cardiomyocytes, and sarcolemmal membrane-associated protein (SLMAP) is a protein of unknown function localizing at T-tubules and sarcoplasmic reticulum. Methods and Results— We analyzed 190 unrelated Brugada syndrome patients for mutations in SLMAP . Two missense mutations, Val269Ile and Glu710Ala, were found in heterozygous state in 2 patients but were not found in healthy individuals. Membrane surface expression of hNav1.5 in the transfected cells was affected by the mutations, and silencing of mutant SLMAP by small interfering RNA rescued the surface expression of hNav1.5. Whole-cell patch-clamp recordings of hNav1.5-expressing cells transfected with mutant SLMAP confirmed the reduced hNav1.5 current. Conclusions— The mutations in SLMAP may cause Brugada syndrome via modulating the intracellular trafficking of hNav1.5 channel.