Daily Cardiology Research Analysis

BACKGROUND: Premature atrial contractions (PACs) are independently associated with atrial fibrillation, stroke, and heart failure, yet no pharmacological therapy is approved for PAC suppression. Experimental studies have identified a functional cardiac glutamatergic system in which N-methyl-D-aspartate receptors regulate atrial electrophysiology. Preclinical studies show that pharmacological antagonism of N-methyl-D-aspartate receptors with memantine suppresses atrial arrhythmias. METHODS: We conducted an investigator-initiated, phase 2, multicenter, randomized, double-blind, placebo-controlled trial. Symptomatic adults with frequent PACs (≥1000/24 h) were randomly assigned to receive memantine or placebo for 6 weeks. The primary end point was the percentage change in mean 24-hour PAC count from baseline to the end of treatment. The primary analysis was performed in the intention-to-treat population. Prespecified secondary end points included the responder rate (≥50% PAC reduction), percentage change in nonsustained atrial tachycardia burden, and cumulative incidence of new-onset atrial fibrillation. RESULTS: Among 241 patients included in the efficacy analysis, memantine resulted in a greater reduction in PAC count than placebo (between-group difference, 47.1 percentage points; CONCLUSIONS: In patients with frequent symptomatic PACs, memantine reduced atrial ectopy and atrial tachyarrhythmia burden and demonstrated a favorable safety profile. These findings provide proof of concept for a novel, non-ion channel-based therapeutic strategy targeting the cardiac glutamatergic system. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06501638.

AIMS: Nearly, 40% of patients undergoing elective invasive coronary angiography (ICA) are diagnosed with non-obstructive coronary artery disease (CAD) or normal coronary anatomy, resulting in unnecessary risk exposure and increased costs to the healthcare system. In this study, we externally validate an artificial intelligence model for optimizing patient selection for ICA vs. coronary computed tomography angiography (CCTA) to reduce unnecessary ICAs. METHODS AND RESULTS: The model was trained on data from outpatients undergoing elective ICA at two cardiac centres in Ontario, Canada between 2008 and 2019. It uses 42 predictors including demographic characteristics, risk factors, and medical history (including ECG stress testing and/or functional imaging) to predict the probability of obstructive CAD. Geographical validation assessed the discrimination performance on patients seen at the other 20 cardiac centres in Ontario, Canada during the same period. Temporal validation evaluated the model's performance on outpatients receiving ICA at the original centres between 2020 and 2023. Reclassification analysis was employed to estimate health system impact. Subgroup analysis was used to assess model fairness. Following external validation, the model was updated on data from the entire outpatient population ( CONCLUSION: Use of the model could result in an absolute reduction of 27% in the proportion of ICAs that result in a diagnosis of normal/non-obstructive disease. This could contribute to a reduction in complications from ICA and more efficient utilization of cardiac catheterization lab capacity for higher-value cardiac interventions such as revascularization and structural procedures. Additionally, use of the model would create significant efficiencies for payors, given the much lower cost of CCTA compared with ICA. If implemented within clinical practice, the model has the potential to improve the patient experience and reduce existing health inequities.

AIMS: Timely, accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and managing patients. However, this often relies on expert interpretation, a major bottleneck in low-resource settings. We developed and validated ECG-GPT, a format-independent vision encoder-decoder model that generates expert-level interpretations from 12-lead ECG images. METHODS AND RESULTS: We developed ECG-GPT using 12-lead ECGs and their corresponding diagnosis statements performed at a large US health system between 2000 and 2022. Using structured clinical assessment, semantic similarity, and conventional metrics, we validated ECG-GPT across seven distinct health settings, including three large and diverse US health systems, ECGs from Minas Gerais, Brazil, the UK Biobank, the Germany-based PTB-XL dataset, and a community hospital in Missouri. In total, 2.9 million ECGs were used for model development, and 4.1 million ECGs for validation. The model performed well in clinical assessment across 26 extracted labels, with diagnostic accuracy ranging from 0.93 to 0.99. For rhythm abnormalities, including atrial fibrillation, sinus tachycardia, sinus bradycardia, premature atrial contractions, and premature ventricular contractions, AUROCs ranged from 0.80 to 0.95. For conduction abnormalities, including left bundle branch block, right bundle branch block, first degree atrioventricular block, left anterior fascicular block, and left posterior fascicular block, AUROCs ranged from 0.88 to 0.96. ECG-GPT identified the full context of diagnosis statements with allied conditions with a median pairwise similarity of 0.90, significantly greater than baseline ( CONCLUSION: We developed and validated a vision encoder-decoder model that generates expert-level interpretations from ECG images, a scalable strategy for accessible automated ECG analysis.