Arsanjani, Reza

Cardiac cachexia (CC) is a severe complication of advanced heart failure (HF), characterized by involuntary weight loss and muscle wasting, leading to poor outcomes and higher mortality. Despite its severity, CC remains under-recognized and undertreated, lacking targeted therapies specifically addressing its pathophysiology. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), though beneficial in reducing cardiovascular risk in patients with HF, may exacerbate muscle wasting in cachectic patients, necessitating further investigation. Non-pharmacological strategies, including tailored nutritional support and exercise programs, have shown positive effects on body composition and quality of life in patients with CC. However, there remains a gap in recommendations tailored to preventive strategies and pharmacologic therapies for patients with CC and concomitant GLP-1RA use. This review highlights the multifactorial mechanisms underlying CC and current and emerging therapeutic approaches for mitigating HF-related sarcopenia while on GLP-1RAs.

Background: Hypertrophic cardiomyopathy (HCM) is a genetic cardiomyopathy marked by increased left ventricular wall thickness, leading in some cases to left ventricular outflow tract (LVOT) obstruction, heart failure, and arrhythmias. Mavacamten, a selective allosteric inhibitor of cardiac myosin, has demonstrated benefits in improving hemodynamics and reducing LVOT obstruction. However, its impact on arrhythmic burden remains unclear, with reports of early atrial fibrillation (AF) risk contrasting with long-term reductions in arrhythmias. This study assesses the temporal patterns of Holter-detected arrhythmias in HCM patients treated with mavacamten. Methods: This retrospective study included HCM patients from three Mayo Clinic sites. Baseline demographic, clinical, and echocardiographic data were collected. Holter monitoring was performed at baseline, short-term (<6 months), and long-term (>6 months) follow-up. Arrhythmic events, including premature atrial contractions (PACs), premature ventricular contractions (PVCs), and supraventricular tachycardia (SVT), were analyzed using standardized rates per 24 h. Statistical comparisons utilized the Wilcoxon signed-rank test. Results: Twenty-seven patients (56% female, median age 66 years) were included. PACs, PVCs, and SVT duration transiently but not significantly increased at short-term follow-up but returned to baseline at long-term follow-up. No sustained or high-risk ventricular arrhythmias were observed. Conclusions: Mavacamten is associated with transient arrhythmic fluctuations early in treatment, followed by stabilization. These findings support its long-term electrophysiological safety and underscore the need for early rhythm monitoring. Further research should explore its role in arrhythmic risk stratification in HCM patients.

Cardiac computed tomography (CT) has rapidly advanced, becoming an invaluable tool for diagnosing and prognosticating various cardiovascular diseases. While echocardiography and cardiac magnetic resonance imaging (CMR) remain the gold standards for myocardial assessment, modern CT technologies offer enhanced spatial resolution, making it an essential tool in clinical practice. Cardiac CT has expanded beyond coronary artery disease evaluation, now playing a key role in assessing cardiomyopathies and structural heart diseases. Innovations like photon-counting CT enable precise estimation of myocardial extracellular volume, facilitating the detection of infiltrative disorders and myocardial fibrosis. Additionally, CT-based myocardial strain analysis allows for the classification of impaired myocardial contractility, while quantifying cardiac volumes and function remains crucial in cardiomyopathy evaluation. This review explores the emerging role of cardiac CT in cardiomyopathy phenotyping, emphasizing recent technological advancements.

Background: Aortic stenosis (AS) imposes a significant mortality burden. Understanding demographic influences on the location of AS-related death is crucial for advancing equitable end-of-life care. Therefore, we investigated how demographic factors influence the location of death among AS patients in the United States. Methods: We completed a cross-sectional study utilizing US mortality data from the CDC’s WONDER database for 2019. All files related to decedents with AS identified as the primary cause of death were obtained, including demographic information and death locations (i.e., inpatient facilities, outpatient/ER facilities, home, or hospice/nursing facilities). Associations between demographic factors (age, sex, race/ethnicity, marital status, and education) and place of death were assessed using multivariable logistic regression models, yielding odds ratios (ORs). Results: In 2019, most AS-related deaths occurred in inpatient facilities (38.3%, n = 5062), home (29.2%, n = 3859), or hospice/nursing facilities (28.6%, n = 3775). Higher odds of inpatient death were observed among Black (OR 1.67, p < 0.001) and Hispanic individuals (OR 1.91, p < 0.001) compared to White decedents. Those aged >85 years were more likely to die at home (OR 1.76, p < 0.001) or in hospice/nursing facilities (OR 7.80, p < 0.001). Males had increased odds of inpatient death (OR 1.09, p = 0.044) but decreased odds of hospice/nursing facility death (OR 0.87, p = 0.003). Higher education levels were associated with increased odds of home death (OR 1.33, p = 0.023) and decreased odds of hospice/nursing facility death (OR 0.71, p = 0.015). Conclusions: Demographic factors significantly influence the location of death among AS patients, emphasizing the need for culturally and socioeconomically tailored interventions to promote equitable end-of-life care.

Aortic stenosis is a progressive condition with substantial implications for morbidity and mortality. In recent years, attention has shifted toward risk stratification and the development of individualized management plans to optimize treatment outcomes. The management of asymptomatic patients has become a topic of significant controversy, as emerging studies challenge traditional watchful waiting guidelines and propose the potential benefits of early intervention. While early intervention may reduce overall morbidity and mortality in this patient population, the associated procedural risks remain a critical consideration. This review seeks to analyze the existing literature, offering an updated perspective on patient risk stratification and evidence evaluating both management approaches.

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.

Abstract Aims Atrial fibrillation (AF) is the most common cardiac arrhythmia. Although lipoprotein(a) [Lp(a)] is known to be a well-established risk factor for atherosclerotic cardiovascular disease, its role in the development of AF, independent of this association, remains unclear. Methods and results Adult patients from the three Mayo Clinic sites with a baseline Lp(a) and without AF history were included. Patients were categorized into two groups based on their Lp(a) levels: high Lp(a) (≥50 mg/dL) and low Lp(a) (&lt;50 mg/dL). Survival probabilities free from incident AF were compared between Lp(a) groups, during a follow-up period up to 15 years, using the Kaplan–Meier curve and the log-rank test. Multivariable Cox regression analysis was also conducted. A total of 75 376 patients were included (median age: 55 years, 59% males), with a median follow-up duration of 8.8 (inter-quartile range: 3.4, 14.8) years. Incident AF was detected in 5738 (7.6%) patients. Survival probability free from incident AF was significantly lower in patients with elevated Lp(a) (86%) compared with those with low Lp(a) (88%, log-rank P &lt; 0.001). Multivariable analysis adjusted for potential risk factors of AF showed a statistically significant association of elevated Lp(a) with an 11% increase in AF risk (adjusted hazard ratio: 1.11, 95% confidence interval: 1.05–1.18). Conclusion Our study suggests that elevated Lp(a) (≥50 mg/dL) is an independent risk factor for incident AF. Future prospective studies are warranted to validate our results and to test if reducing Lp(a) could mitigate the burden of AF.

Lipoprotein(a) [Lp(a)] is a well-established causal risk factor for cardiovascular diseases (CVDs), as reported by multiple Mendelian randomization studies and large epidemiological studies. When elevated Lp(a) is combined with other risk factors, most notably elevated low-density lipoprotein cholesterol (LDL-C), a synergistic atherogenic effect has been reported. However, the current literature is conflicting regarding how Lp(a) interacts in the context of controlled LDL-C levels (e.g., <70 mg/dL) and whether reducing LDL-C can modify the atherogenic effect of Lp(a). In some studies, elevated Lp(a) was still significantly associated with a higher risk of cardiovascular events, despite controlled levels of LDL-C. In contrast, multiple studies have reported attenuation of the cardiovascular risk mediated by elevated Lp(a) with lower LDL-C levels. Moreover, the relationship between Lp(a) and triglycerides, high-density lipoprotein, and very low-density lipoprotein remains unclear. In this literature review, we summarize and discuss the current evidence regarding the interactions between Lp(a) and other lipid molecules, how they contribute to the pathogenesis of CVD, and future perspectives, particularly in the current era where promising targeted Lp(a)-lowering therapies are under development.

Background: Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. Conclusions: While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes.