I am Dr. Bishnu Subedi. I am a cardiologist in the United States. In the era of evidence-based medicine, our practice is usually guided by a scientific study, expert society statements, or clinical guidelines. In this podcast series, I intend to highlight some of these practice-changing articles in the field of cardiology from past and present.
'Why we do What we do in Cardiology'
Claim This Podcastby Bishnu Subedi
5.0(2 reviews)
35 episodes
Updated Weekly
Accepts GuestsHas SponsorsLocation 🇺🇸
Podcast Overview
I am Dr. Bishnu Subedi. I am a cardiologist in the United States. In the era of evidence-based medicine, our practice is usually guided by a scientific study, expert society statements, or clinical guidelines. In this podcast series, I intend to highlight some of these practice-changing articles in the field of cardiology from past and present.
Language
🇺🇲
Publishing Since
6/13/2020
1 verified contact email on file for 'Why we do What we do in Cardiology'
Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.
Recent Episodes
June 24, 2024
REDUCE-AMI Trial: Diminishing Role of Beta-Blockers in ACS with preserved LVEF
<p>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.</p> <p>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.</p> <p>3. Primary and secondary outcomes showed no significant differences between the beta-blocker and usual care groups.</p> <p>4. Safety outcomes were similar between groups, and there was significant crossover and varying adherence to beta-blocker therapy over time.</p> <p>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.</p> --- Send in a voice message: https://podcasters.spotify.com/pod/show/dr-bishnu-subedi/message
June 22, 2024
Improving Left Ventricular Ejection Fraction in Heart Failure Patients: Insights from the HF-OPT Study
<p>The <strong>HF-OPT study</strong> investigated the improvement of <strong>left ventricular ejection fraction (LVEF)</strong> beyond 90 days in patients with newly diagnosed <strong>heart failure with reduced ejection fraction (HFrEF)</strong>.</p> <p>In this prospective, multicenter observational study, 1,300 patients with <strong>HFrEF (LVEF ≤35%)</strong> were initially enrolled.</p> <p>Participants wore a <strong>wearable cardioverter-defibrillator (WCD)</strong> and received <strong>guideline-directed medical therapies (GDMT)</strong>.</p> <p><strong>LVEF</strong> was measured at 0, 90, 180, and 360 days.</p> <p>By day 90, 46% had an <strong>LVEF >35%</strong>; this increased to 68% by day 180 and 77% by day 360.</p> <p>High <strong>GDMT</strong> usage was noted, with 97% on <strong>beta-blockers</strong>, 94% on <strong>ACE inhibitors/angiotensin-receptor blockers/ARNI</strong>, and 62% on <strong>mineralocorticoid antagonists</strong> by day 180.</p> <p>Achieving target doses of all three <strong>GDMT</strong> classes was associated with significant <strong>LVEF</strong> improvement.</p> <p>The study recorded low rates of <strong>ventricular arrhythmias</strong> beyond the initial 90 days.</p> <p>These results underscore the potential benefits of continuous <strong>GDMT</strong> optimization. They suggest that delayed <strong>implantable cardioverter-defibrillator (ICD)</strong> implantation may be reasonable for selected patients, allowing for further <strong>LVEF</strong> improvement.</p> <p>This emphasizes the importance of optimal dosing and continuous <strong>GDMT</strong> for effective heart failure management, highlighting the need for expedited <strong>GDMT</strong> titration and a tailored approach to heart failure care.</p> <p>Reference: <em>European Heart Journal</em>, ehae334, <a href="https://doi.org/10.1093/eurheartj/ehae334">https://doi.org/10.1093/eurheartj/ehae334</a></p> --- Send in a voice message: https://podcasters.spotify.com/pod/show/dr-bishnu-subedi/message
June 21, 2024
AI in Cardiovascular Medicine: JACC Review
<ul> <li><p><strong>Overview</strong>: 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.</p> </li> <li><p><strong>AI Glossary</strong>: 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.</p> </li> <li><p><strong>Deep Learning in Cardiology</strong>: Applied to physiologic waveform, imaging, and multiomics data with clinical applications. Studies reviewed using MeSH terms in PubMed.</p> </li> <li><p><strong>ECG and AI</strong>: Deep learning techniques like CNNs show promise in arrhythmia classification and predicting conditions like LV systolic dysfunction, hypertrophic cardiomyopathy, and cardiac amyloidosis.</p> </li> <li><p><strong>AI in Echocardiography</strong>: Improves image acquisition and interpretation, helping automate measurements and enhancing variability and disease diagnosis.</p> </li> <li><p><strong>AI in CMR Imaging</strong>: Enhances image reconstruction, segmentation, and quantification. AI applications in nuclear cardiology and CT include improved prognostication and plaque burden quantification.</p> </li> <li><p><strong>AI in Electrophysiology</strong>: Aids preprocedural planning, intraprocedural guidance, and postprocedural predictions, improving ablation target identification and therapy response prediction.</p> </li> <li><p><strong>AI in Coronary Angiography</strong>: Automates stenosis detection, plaque characterization, and fractional flow reserve computation, enhancing accuracy and procedural efficiencies.</p> </li> <li><p><strong>Machine Learning in Genomics</strong>: Improves risk prediction, variant interpretation, pathogenicity identification, and integration into clinical care.</p> </li> <li><p><strong>Future of AI in Cardiovascular Medicine</strong>: Promises enhanced disease screening, imaging data integration, and accurate diagnoses. Focuses on data quality, diversity, model generalizability, and promoting AI adoption in clinical practice.</p> </li> <li><p><strong>AI Potential</strong>: Significant potential to enhance patient care through improved diagnostics, risk stratification, and personalized treatment plans, supporting clinicians in delivering better cardiovascular care.</p> <p>Reference: <a href="https://www.jacc.org/journal/jacc">J Am Coll Cardiol</a>. 2024 Jun, 83 (24) 2472–2486</p> </li> </ul> --- Send in a voice message: https://podcasters.spotify.com/pod/show/dr-bishnu-subedi/message
35 total episodes available
Deep-dive analytics for 'Why we do What we do in Cardiology'
Frequently asked questions
Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.
- What is 'Why we do What we do in Cardiology'?
- How often does this podcast release new episodes?
This podcast updates weekly.
- Where can I listen to this podcast?
This podcast is available on 8 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Yes, this podcast regularly features guests.
Legal Disclaimer
Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.
All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.
We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.
While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.
By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.