Daily Cardiology Research Analysis

In the HELIOS-B randomized clinical trial, the RNA interference therapeutic agent vutrisiran reduced the risk of all-cause mortality and recurrent cardiovascular events among patients with transthyretin amyloidosis with cardiomyopathy (ATTR-CM). In this secondary analysis of HELIOS-B, we evaluated vutrisiran's effects on echocardiographic measures of cardiac structure and function in patients with ATTR-CM receiving vutrisiran or placebo (n = 654, 93% men). At 30 months after treatment, as compared to the placebo group, vutrisiran treatment attenuated increases in mean left ventricular (LV) wall thickness (least squares mean difference: -0.4 mm; 95% confidence interval (CI): -0.8, 0.0; P = 0.03) and LV mass index (-10.6 g m

BACKGROUND: Whether frail, elderly patients with atrial fibrillation (AF) on a vitamin K antagonist (VKA) should switch to a direct-acting oral anticoagulant (DOAC) was studied in the FRAIL-AF trial and remains controversial. OBJECTIVES: The purpose of this study was to evaluate, in the COMBINE-AF data set, the impact on clinical outcomes of switching frail, elderly AF patients from VKA to DOAC. METHODS: COMBINE-AF consists of individual patient-level data from 71,683 patients with AF in 4 randomized clinical trials comparing DOAC vs warfarin. Frailty was evaluated using a frailty index derived from a modified Rockwood's Accumulation Model including 18 age-related conditions. Patients with a frailty index score above the median were considered frail. Prespecified outcomes were stroke or systemic embolic events, bleeding events, death, and a net clinical outcome combining these events. RESULTS: We identified 5,913 patients who were frail, elderly (age ≥75 years), and VKA-experienced and 52,721 patients who did not meet all 3 of these criteria. Patients were randomized to a standard-dose (SD) DOAC or warfarin. After 27 months median follow-up, there was no heterogeneity in treatment effect with SD-DOAC vs warfarin among those who met all 3 criteria vs those who did not for the endpoints of stroke or systemic embolic events (HR: 0.83 vs 0.81; P

BACKGROUND: Artificial intelligence (AI) has demonstrated significant potential in electrocardiogram (ECG) analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI, bringing benefits such as efficient disease diagnosis and cross-domain knowledge transfer. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model poses several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. In addition, there is a notable performance gap between single-lead and multilead ECG analysis. METHODS: We propose a general-purpose ECG foundation model (ECGFounder), which leverages real-world ECG annotations from cardiologists to broaden the diagnostic capabilities of ECG analysis. ECGFounder was built on 10,771,552 ECGs from 1,818,247 unique subjects with 150 label categories from the Harvard-Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis. The model is designed to be both an effective out-of-the-box solution and easily fine-tunable for downstream tasks, maximizing usability. Importantly, we extended its application to reduced-lead ECGs, particularly single-lead ECGs. ECGFounder is therefore applicable to various downstream tasks in mobile and remote monitoring scenarios. RESULTS: Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets, with area under the receiver operating characteristic curve (AUROC) exceeding 0.95 for 80 diagnoses. It also shows strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographic analysis, clinical event detection, and cross-modality cardiac rhythm diagnosis, surpassing baseline methods by 3 to 5 points in the AUROC. CONCLUSIONS: The ECG foundation model offers an effective solution, allowing it to generalize across a wide range of tasks. By enhancing existing cardiovascular diagnostics and facilitating integration with cloud-based systems, which analyze ECG data uploaded from wearable devices, it significantly contributes to the advancement of the cardiovascular AI community and enables management of cardiac conditions. (Funded by the National Science Foundation and others.).