Navin Chander Nanda

Background/Objectives: Obesity is a multifactorial chronic condition characterized by the accumulation of excess adiposity and complex interplay between intrinsic and extrinsic factors. It is an increasingly common condition, closely implicated with the incidence and progression of cardiovascular disease and its risk factors. This narrative review synthesizes and summarizes recent evidence on obesity, with a focus on the diagnosis of obesity, an exploration of both visceral and subcutaneous adipose tissue, available interventions for obesity ranging from dietary modifications to novel anti-obesity medications, and key associations with obesity and cardiovascular diseases. This review is distinct in its integrated focus on obesity definition and diagnosis, imaging modalities, the latest non-pharmacologic and pharmacologic interventions, and also the interplay between obesity and certain cardiovascular conditions as well as their risk factors. Results: The diagnosis of obesity has been evolving with the incorporation of anthropometric measurements and imaging modalities rather than simply the body mass index. There is a wide array of contributors to obesity including genetic factors, behavior, hormonal regulators, the brain–gut axis, and psychosocial stressors. Anti-obesity medications have been evolving rapidly, with current emphasis on glucagon-like peptide 1 receptor agonists. Obesity is closely implicated in cardiovascular conditions such as atherosclerotic disease, heart failure, atrial fibrillation, and hypertension as well as related risk factors such as diabetes mellitus, chronic kidney disease and sleep apnea. Conclusions: Obesity is a widely prevalent, chronic, and complex disease. The use of a variety of anthropometric measurements can help risk-stratify individuals. Imaging techniques are also helpful in evaluating body fat. Evaluating individuals from a holistic perspective is imperative to appreciate the various contributors to obesity. There are a variety of interventions available for obesity management including lifestyle interventions, bariatric surgery, and pharmacologic therapy. Notably, obesity is closely tied with cardiovascular diseases and recent pharmacologic anti-obesity agents may mitigate cardiovascular risk.

Artificial Intelligence in Echocardiography: Transforming Cardiac Imaging

Background: Cardiovascular risk models, such as the Framingham and atherosclerotic cardiovascular disease (ASCVD) calculators, have improved risk prediction but often fail to identify individuals who experience ASCVD events despite low or intermediate predicted risk. This suggests that underrecognized, non-traditional risk factors may contribute significantly to the development of atherosclerosis. Objective: This narrative review synthesizes and summarizes recent evidence on high-yield non-traditional risk factors for atherosclerosis, with a focus on clinically significant, emerging, and applicable contributors beyond conventional frameworks. This review is distinct in that it aggregates a wide array of non-traditional risk factors while also consolidating recent data on ASCVD in more vulnerable populations. Unlike the existing literature, this manuscript integrates in a single comprehensive review various domains of non-traditional atherosclerotic risk factors, including inflammatory, metabolic, behavioral, environmental, and physical pathways. An additional unique highlight in the same manuscript is the discussion of non-traditional risk factors for atherosclerosis in more vulnerable populations, specifically South Asians. We also focus on clinically actionable factors that can guide treatment decisions for clinicians. Results: Key non-traditional risk factors identified include inflammation and biomarker-based risk factors such as C-reactive protein or interleukin-6 levels, metabolic and microbial risk factors, behavioral factors such as E-cigarette use, and environmental or infectious risk factors such as air and noise pollution. We explore certain physical exam findings associated with atherosclerotic burden, such as Frank’s sign and Achilles tendon thickness. Conclusions: Atherosclerosis is a multifactorial process influenced by diverse and often overlooked factors. Integrating non-traditional risks into clinical assessment may improve early detection, guide prevention and personalize care. Future risk prediction models should incorporate molecular, behavioral, and environmental data to reflect the complex nature of cardiovascular disease.

Artificial Intelligence (AI) in echocardiography represents a transformative leap in the realm of cardiology, refining diagnostic accuracy and mitigating human error. AI, often erroneously equated with robotics or automation, transcends mere mechanical operations. It encompasses the capacity of machines to emulate human decision-making processes and problem-solving abilities, augmenting rather than replacing human expertise. In echocardiography, AI has emerged as a pivotal tool, enhancing consistency and precision, elements crucial in cardiac diagnostics. Echocardiography is indispensable in the diagnosis of cardiovascular diseases, but it is traditionally hampered by operator-dependent variability and subjective interpretation. AI intervenes here, offering high-precision automation in echocardiographic analysis. This encompasses several key phases: from image acquisition and standard view classification to cardiac chamber segmentation, structural quantification, and functional assessment. AI algorithms have demonstrated expertise-level accuracy in these domains, notably in identifying cardiac conditions such as cardiomyopathies. The incorporation of AI into echocardiography tackles inherent challenges like variance in image capture and analysis. AI's prowess in machine learning (ML), a crucial subset of AI, specifically enhances image interpretation in echocardiography. This advancement is significant given echocardiography's reliance on the operator's subjective expertise, a reliance more pronounced than in other imaging modalities such as computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI). AI technologies, through automated and consistent interpretations, promise to reshape echocardiographic diagnostics. This review delves into ML's role in augmenting echocardiographic image analysis and diagnostic performance. It also examines the existing literature, highlighting AI's value in echocardiography and its potential to elevate patient care. In summary, AI's integration into echocardiography marks a pivotal shift, offering enhanced diagnostic capabilities and heralding a new era in cardiovascular care.

Due to the proportionally high mortality rates associated with isolated tricuspid valve surgery, the invasive treatment of such pathology, historically, has been left largely unaddressed. Recently, there has been an appreciation for the mortality and morbidity of tricuspid valve disease, giving rise to the movement towards identifying less invasive, transcatheter approaches for treatment. Due to the technical complexity of these procedures along with the uniqueness and variability of tricuspid valve anatomy, a better appreciation of the tricuspid valve anatomy and pathology is required for pre-procedural planning. While two-dimensional echocardiography serves as the initial non-invasive modality for tricuspid valve evaluation, three-dimensional echocardiography provides a complete en face view of the tricuspid valve and surrounding structures, as well contributes further information regarding disease etiology and severity. In this review, we discuss the utility of three-dimensional echocardiography as a supplement to two-dimensional imaging to better assess tricuspid valve disease and anatomy to aide in future innovative therapies.

Background. The aim of this study is to describe, for the first time to our knowledge, the utilization of both two-dimensional (2D) and three-dimensional (3D) transesophageal echocardiography (TEE) in successfully performing transcatheter mitral valve (MV) in bioprosthetic MV/MV annulopasty ring implantation using the apical approach in 12 patients (pts) with co-existing left atrial appendage (LAA) and/or LA (left atrium) body thrombus, which is considered a contraindication for this procedure. Methods and Results. All pts were severely symptomatic with severe bioprosthetic MV stenosis/regurgitation except one with a previous MV annuloplasty ring and severe native MV stenosis. Thrombus in LAA and/or LA body was noted in all by 2D and 3DTEE. All were at high/prohibitive risk for redo operation and all refused surgery. Utilizing both 2D and 3DTEE, especially 3DTEE, guidewires and the prosthesis deployment system could be manipulated under direct vision into the LA avoiding any contact with the thrombus. The procedure was successful in all with amelioration of symptoms and no embolic or other complications over a mean follow-up of 21 months. Conclusion. Our study demonstrates the feasibility of successfully performing this procedure in pts with thrombus in LAA and/or LA body without any complications.

We describe the role of three‐dimensional echocardiography in the assessment of the aortic valve and the aorta. The manuscript is heavily illustrated with figures and movie clips.