Abdelnabi, Mahmoud

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.

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.

Abstract Background Chronic kidney disease (CKD) increases cardiac arrest (CA) risk because of renal and cardiovascular interactions. Methods Using Centers for Disease Control and Prevention (CDC) data from 1999 to 2020, we analyzed CKD‐related CA mortality and the impact of social vulnerability index (SVI). Results We identified 336 494 CKD‐related CA deaths, with stable age‐adjusted mortality rates over time. Disparities were observed across gender, racial/ethnic, and geographic subpopulations, with higher mortality among males, Hispanic and non‐Hispanic Black populations, and those in urban and Western regions. Higher SVI correlated with increased mortality. Conclusions CKD‐related CA mortality rates are stable, with disparities across demographics; higher SVI correlates with increased mortality, highlighting needed interventions.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, impacting approximately 6.1 million adults in the United States, with projections to increase two-fold by 2030. AF significantly increases the risk of stroke and other adverse cardiovascular events, leading to increased morbidity and mortality. The 2023 ACC/AHA/ACCP/HRS guidelines present a paradigm shift in AF management, moving from a duration-based classification to a more comprehensive, patient-centered approach. This includes a novel AF classification system that emphasizes early detection and intervention, including risk factors and lifestyle modification tailored to each patient’s risk profile. Moreover, the recommendations advocate for a multidisciplinary care model, ensuring coordinated management involving primary care providers and specialists. Primary care providers play a crucial role in initiating risk factor management and lifestyle interventions, even before the development of AF. This review aims to thoroughly examine the guidelines for the diagnosis and management of AF and equip general internists with the necessary insights to navigate the evolving landscape of AF care effectively.

Infective endocarditis (IE) poses a diagnostic challenge due to its diverse clinical presentations, especially among high-risk groups. Diagnosis relies on integrating clinical presentation, blood cultures and imaging findings. Advanced imaging techniques enhance diagnostic accuracy, particularly in complex cases. Treatment involves antimicrobial therapy and surgery in complicated cases, with early intervention crucial for optimal outcomes. Coordinated care by an Endocarditis Team ensures tailored treatment plans, prompt complication management and long-term monitoring after discharge. The authors present a case of subacute IE presenting initially with back pain in a patient with a complex medical history, highlighting diagnostic and management approaches.

Key Clinical MessagePre‐bariatric surgery dietary recommendations should take into account daily protein intake and other risk factors for kidney injury. This is important because a high protein intake can potentially lead to kidney injury.AbstractBariatric surgery has been shown to be a highly effective intervention for achieving weight loss and reducing obesity related‐comorbidities. Acute kidney injury (AKI) is considered one of the common complications in perioperative and post‐bariatric surgery. However, pre‐bariatric surgery AKI has never been reported. Several studies demonstrated that pre‐bariatric surgery weight loss improved surgical outcomes and decrease postoperative complications. Some diet regimens have been introduced including low‐caloric diet (LCD), very‐low caloric diet (VLCD), and very‐low caloric ketogenic diet (VLCKD). We present a patient who develops AKI after 10 days of having a high‐protein diet from a pre‐bariatric weight loss strategy.