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The burden of cardiovascular diseases is sharply rising in low- and middle-income countries (LMICs). Along with the increasing rates of cardiovascular risk factors in these regions, there is a growing recognition of the contribution of neglected tropical diseases and other infections. Several cardiac implications of these infections have been reported but have not yet been validated by robust population data. This is in part due to limited access to health care and insufficient data collection infrastructure in many LMICs. Therefore, the true impact of these infections on the cardiovascular system may be underestimated, because of both underdiagnosis and underreporting bias. There is an urgent need to thoroughly delineate the cardiac impact of these conditions with elevated prevalence in LMICs and to propose strategies to reduce the negative consequences of these diseases in health systems with limited resources., Competing Interests: Funding Support and Author Disclosures The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation. [ABSTRACT FROM AUTHOR]

Lipoprotein(a) [Lp(a)] is a lipid molecule with atherogenic, inflammatory, thrombotic, and antifibrinolytic effects, whose concentrations are predominantly genetically determined. The association between Lp(a) and cardiovascular diseases (CVDs) has been well-established in numerous studies, and the ability to measure Lp(a) levels is widely available in the community. As such, there has been increasing interest in Lp(a) as a therapeutic target for the prevention of CVD. The impact of the currently available lipid-modifying agents on Lp(a) is modest and heterogeneous, except for the monoclonal antibody proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i), which demonstrated a significant reduction in Lp(a) levels. However, the absolute reduction in Lp(a) to significantly decrease CVD outcomes has not been definitely established, and the magnitude of the effect of PCSK9i seems insufficient to directly reduce the Lp(a)-related CVD risk. Therefore, emerging therapies are being developed that specifically aim to lower Lp(a) levels and the risk of CVD, including RNA interference (RNAi) agents, which have the capacity for temporary and reversible downregulation of gene expression. This review article aims to summarize the effects of Lp(a) on CVD and to evaluate the available evidence on established and emerging therapies targeting Lp(a) levels, focusing on the potential reduction of CVD risk attributable to Lp(a) concentrations. [ABSTRACT FROM AUTHOR]

Interatrial blocks (IABs) are a variety of abnormalities in the interatrial conduction. Bayes' syndrome is a clinical entity based on the association between advanced IABs and supraventricular tachyarrhythmias, being atrial fibrillation (AF) the most frequent. Due to its negative effects on left atrial electromechanical function, both IABs and Bayes' syndrome are associated with thromboembolic phenomena, causing cardiovascular and neurological complications. In regard to neurological involvement, patients with these conditions have an increased incidence of ischemic events, cognitive impairment, and dementia. These observations triggered the question whether the use of early anticoagulation therapy (before the documentation of AF) could prevent thromboembolic events in patients with IABs diagnosis. This review aims to summarize the most recent evidence describing the association of IABs and Bayes' syndrome with neurological events. Potential early therapeutic options to prevent these undesirable clinical consequences will be also discussed. [ABSTRACT FROM AUTHOR]