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Direct oral anticoagulants or warfarin in patients with left ventricular thrombus after ST-elevation myocardial infarction: a pilot trial and a prespecified meta-analysis of randomised trials.
EuroIntervention
January 2025
Cardiovascular Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Background: The role of direct oral anticoagulants (DOACs) in the treatment of left ventricular thrombus (LVT) after ST-elevation myocardial infarction (STEMI) remains uncertain.
Aims: We aimed to compare the effect of rivaroxaban versus warfarin in patients with STEMI complicated by LVT.
Methods: Adult patients with STEMI and two-dimensional transthoracic echocardiography showing LVT were assigned to rivaroxaban (15 mg once daily) or warfarin (international normalised ratio goal of 2.
A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers.
Am J Cardiol
February 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut. Electronic address:
Article Synopsis
- AI-ECG can effectively detect hypertrophic cardiomyopathy (HCM) and track treatment responses using 12-lead ECGs.
- The study analyzed data from patients undergoing surgical reduction and those receiving mavacamten at multiple healthcare centers, finding no improvement in HCM scores after surgery, but a significant decrease in scores among patients taking mavacamten.
- This highlights AI-ECG's potential for ongoing monitoring of heart condition improvements following medication rather than surgical interventions.
Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing.
JACC Heart Fail
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut, USA. Electronic address:
Background: The lack of automated tools for measuring care quality limits the implementation of a national program to assess guideline-directed care in heart failure with reduced ejection fraction (HFrEF).
Objectives: The authors aimed to automate the identification of patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care.
Methods: The authors developed a novel deep-learning language model for identifying patients with HFrEF from discharge summaries of hospitalizations with heart failure at Yale New Haven Hospital during 2015 to 2019.
Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.
Circ Cardiovasc Qual Outcomes
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.
Article Synopsis
- Researchers evaluated the potential of artificial intelligence (AI) applied to electrocardiograms (ECG) to predict cardiac dysfunction related to cancer treatments, aiming to develop a more scalable risk stratification method.
- In a study involving 1,550 patients treated with anthracyclines and/or trastuzumab, the AI model classified patients into low, intermediate, and high-risk groups based on their baseline ECG images.
- The findings revealed that patients in the high-risk group had significantly higher incidents of cardiac dysfunction within a year post-treatment, highlighting the effectiveness of AI-ECG in identifying those at greater risk for complications.
Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study.
Eur Heart J Digit Health
May 2024
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA.
Article Synopsis
- - The study investigates the effectiveness of the ASSIST algorithm for guiding anatomical vs. functional testing in patients with suspected coronary artery disease (CAD), finding that following this algorithm can lead to better health outcomes compared to random testing choices.
- - Data from two cohorts (Yale health system and UK Biobank) showed that certain demographics (younger individuals, women, Black patients, and those with diabetes) were less likely to receive ASSIST-aligned testing, which resulted in a lower risk of heart attacks or death when they were referred to the algorithm.
- - The research concludes that utilizing a data-driven approach like ASSIST in places that traditionally favor functional testing can significantly improve detection of CAD and reduce negative health events, emphasizing the need for such
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