Farina, Juan M.

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.

AbstractBackgroundThe prognosis in patients with advanced cardiac amyloidosis (CA) remains poor.ObjectivesWe sought to describe survival post heart transplantation (HT) in amyloid compared with non‐amyloid recipients, highlight waitlist times within the new allocation system across three Organ Procurement and Transplantation Network (OPTN) regions, and describe multiorgan transplantation (MOT) in hereditary amyloidosis.MethodsThis is a retrospective review of end‐stage CA patients who underwent HT at Mayo Clinic from January 2007 to December 2020. Wait time was compared in the new versus old OPTN allocation era starting December 18, 2018 by Wilcoxon rank sum test. All‐cause mortality for those with and without CA was compared using Kaplan–Meier estimates with log rank analysis, censoring December 16, 2022.ResultsFifty‐five patients with CA underwent HT between 2007 and 2020, 8 light chain amyloidosis (AL) (14.5%), 28 hereditary transthyretin (ATTRv) (50.9%), 17 wildtype transthyretin (ATTRwt) (30.9%), and 2 hereditary apolipoprotein A1 (AApoA1) amyloidosis patients (3.6%). No significant difference in overall survival post‐transplant was seen in amyloid compared with non‐amyloid (p = 0.816). Median time to HT was shorter in the new system, 45 days (IQR 24, 78) versus 174 days (IQR 76.5, 483.5), p = 0.006. There was a decline in MOT in hereditary amyloidosis over time with the concomitant rise in disease‐targeted therapies.ConclusionsHT survival in CA patients was similar to non‐amyloid patients. The new allocation system benefits this cohort with shorter wait times. There is less MOT in hereditary amyloidosis with increased utilization of disease‐targeted therapy.

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.

Bioprosthetic aortic valve degeneration (BAVD) is a significant clinical concern following both transcatheter aortic valve replacement (TAVR) and surgical aortic valve replacement (SAVR). The increasing use of bioprosthetic valves in aortic valve replacement in younger patients and the subsequent rise in cases of BAVD are acknowledged in this review which aims to provide a comprehensive overview of the incidence, diagnosis, predictors, and management of BAVD. Based on a thorough review of the existing literature, this article provides an updated overview of the biological mechanisms underlying valve degeneration, including calcification, structural deterioration, and inflammatory processes and addresses the various risk factors contributing to BAVD, such as patient demographics, comorbidities, and procedural variables. The difficulties in early detection and accurate diagnosis of BAVD are discussed with an emphasis on the need for improved imaging techniques. The incidence and progression of BAVD in patients undergoing TAVR versus SAVR are compared, providing insights into the differences and similarities between the two procedures and procedural impacts on valve longevity. The current strategies for managing BAVD, including re-intervention options of redo surgery and valve-in-valve TAVR, along with emerging treatments are discussed. The controversies in the existing literature are highlighted to offer directions for future investigations to enhance the understanding and management of BAVD.

Background Lipoprotein(a) [Lp(a)] is an independent, causal risk factor for cardiovascular disease. However, it is still unclear whether controlling low-density lipoprotein cholesterol (LDL-C) to optimal levels can attenuate cardiovascular risk mediated by elevated Lp(a), especially in the setting of secondary prevention. Methods Adult patients with a baseline Lp(a) measurement who underwent percutaneous coronary intervention (PCI) and reached their LDL-C target levels (<70 mg/dl) at Mayo Clinic sites between 2006 and 2017 were included. Primary outcomes included major adverse cardiovascular events (MACE) and all-cause mortality. Kaplan–Meier curves were created to compare the survival probabilities among patients with Lp(a) ≥ 50 mg/dl compared with Lp(a) < 50 mg/dl. Multivariable Cox regression analyses were performed to quantify the association of elevated Lp(a) with our relevant outcomes and to control for possible confounders. Results In total, 878 patients (median age: 68 years, and 74% males) who underwent PCI were included for analysis. Of them, 29.7% had elevated Lp(a) ≥ 50 mg/dl. Kaplan–Meier curves did not reveal any significant difference in survival probabilities for elevated Lp(a) for any outcome including MACE (P = 0.91), all-cause mortality (P = 0.26), or the separate MACE components. Similarly, the multivariable analysis showed no significant association for MACE (hazard ratio: 1.07, 95% confidence interval: 0.84–1.37) or all-cause mortality (hazard ratio: 0.98, 95% confidence interval: 0.74–1.30). Conclusion In patients who underwent PCI and have their LDL-C controlled below 70 mg/dl, no significant association was found between elevated Lp(a) ≥ 50 mg/dl and risk for MACE or all-cause mortality.