1. The REDUCE-AMI trial showed no significant benefit of beta-blockers in reducing all-cause mortality or future myocardial infarction in patients with acute myocardial infarction and preserved left ventricular ejection fraction.

2. The trial included 5,020 patients who were randomized to either beta-blockade with metoprolol or bisoprolol or usual care, with follow-up over a median of 3.5 years.

3. Primary and secondary outcomes showed no significant differences between the beta-blocker and usual care groups.

4. Safety outcomes were similar between groups, and there was significant crossover and varying adherence to beta-blocker therapy over time.

5. The findings suggest a need to re-evaluate the routine use of beta-blockers in this patient population, emphasizing personalized treatment approaches and further research.

The HF-OPT study investigated the improvement of left ventricular ejection fraction (LVEF) beyond 90 days in patients with newly diagnosed heart failure with reduced ejection fraction (HFrEF).

In this prospective, multicenter observational study, 1,300 patients with HFrEF (LVEF ≤35%) were initially enrolled.

Participants wore a wearable cardioverter-defibrillator (WCD) and received guideline-directed medical therapies (GDMT).

LVEF was measured at 0, 90, 180, and 360 days.

By day 90, 46% had an LVEF >35%; this increased to 68% by day 180 and 77% by day 360.

High GDMT usage was noted, with 97% on beta-blockers, 94% on ACE inhibitors/angiotensin-receptor blockers/ARNI, and 62% on mineralocorticoid antagonists by day 180.

Achieving target doses of all three GDMT classes was associated with significant LVEF improvement.

The study recorded low rates of ventricular arrhythmias beyond the initial 90 days.

These results underscore the potential benefits of continuous GDMT optimization. They suggest that delayed implantable cardioverter-defibrillator (ICD) implantation may be reasonable for selected patients, allowing for further LVEF improvement.

This emphasizes the importance of optimal dosing and continuous GDMT for effective heart failure management, highlighting the need for expedited GDMT titration and a tailored approach to heart failure care.

Reference: European Heart Journal, ehae334, https://doi.org/10.1093/eurheartj/ehae334

• Overview: This review discusses the use and future directions of AI in cardiology, focusing on areas like electrocardiography, telemetry and wearables, echocardiography, CMR, nuclear cardiology, CT, electrophysiology studies, coronary angiography, and genetics or multiomics.

• AI Glossary: Includes key terms such as algorithms, AUC, artificial intelligence, neural networks, classification, CNNs, deep learning, features, foundation models, joint embedding, labels, large language models, machine learning, preprocessing, reinforcement learning, segmentation, semi-supervised learning, structured data, supervised learning, unstructured data, unsupervised learning, and wearables.

• Deep Learning in Cardiology: Applied to physiologic waveform, imaging, and multiomics data with clinical applications. Studies reviewed using MeSH terms in PubMed.

• ECG and AI: Deep learning techniques like CNNs show promise in arrhythmia classification and predicting conditions like LV systolic dysfunction, hypertrophic cardiomyopathy, and cardiac amyloidosis.

• AI in Echocardiography: Improves image acquisition and interpretation, helping automate measurements and enhancing variability and disease diagnosis.

• AI in CMR Imaging: Enhances image reconstruction, segmentation, and quantification. AI applications in nuclear cardiology and CT include improved prognostication and plaque burden quantification.

• AI in Electrophysiology: Aids preprocedural planning, intraprocedural guidance, and postprocedural predictions, improving ablation target identification and therapy response prediction.

• AI in Coronary Angiography: Automates stenosis detection, plaque characterization, and fractional flow reserve computation, enhancing accuracy and procedural efficiencies.

• Machine Learning in Genomics: Improves risk prediction, variant interpretation, pathogenicity identification, and integration into clinical care.

• Future of AI in Cardiovascular Medicine: Promises enhanced disease screening, imaging data integration, and accurate diagnoses. Focuses on data quality, diversity, model generalizability, and promoting AI adoption in clinical practice.

• AI Potential: Significant potential to enhance patient care through improved diagnostics, risk stratification, and personalized treatment plans, supporting clinicians in delivering better cardiovascular care.

  Reference: J Am Coll Cardiol. 2024 Jun, 83 (24) 2472–2486