Abdelnabi, Mahmoud

Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image interpretation is limited by inter-observer variability and time-intensive workflows. Artificial intelligence (AI) models have emerged as a transformative tool for automated coronary plaque analysis across multiple imaging modalities. AI-driven models demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment. Integration of AI-derived imaging biomarkers with clinical risk scores can further enhance prediction of major adverse cardiovascular events and supports personalized management. These advances position AI-enhanced imaging as a powerful adjunct for both invasive and non-invasive evaluation of CAD. Despite its promise, important barriers to widespread clinical adoption remain, including data heterogeneity, algorithmic bias, limited model transparency, insufficient prospective validation, regulatory challenges, and incomplete integration into clinical workflows. Addressing these challenges will be essential to ensure safe, generalizable, and cost-effective implementation of AI in routine cardiovascular care.

Heart failure (HF) remains a major cause of morbidity and mortality worldwide, with its prevalence continuing to rise due to an aging population and the increasing burden of cardiometabolic diseases. Advances in understanding HF pathophysiology—neurohormonal, inflammatory, and metabolic mechanisms—have led to significant improvements in diagnosis and management, emphasizing earlier detection and patient-centered approaches. Novel biomarkers have the potential to enhance risk assessment beyond traditional natriuretic peptides. Imaging advances can enhance structural and functional assessment, enabling more accurate phenotyping, disease characterization, and risk stratification. Recent advances and real-world data have been used to implement and optimize guideline-directed medical therapy (GDMT) for HF to reduce HF hospitalizations and cardiovascular mortality across the spectrum of HF etiologies. Adjunctive therapies are reserved for select patients with persistent symptoms or high-risk features despite optimal GDMT. Device- and transcatheter-based interventions include established and emerging technologies that address persistent symptoms, structural abnormalities, and hemodynamic abnormalities despite optimal GDMT, thereby expanding treatment options for high-risk patients. Collectively, these advancements highlight a paradigm shift toward precise, personalized approaches to HF management, thereby improving long-term outcomes across the spectrum of HF etiologies.

Abstract Introduction Cardiovascular disease (CVD) is the leading cause of death among patients with kidney failure on dialysis. Social determinants of health may worsen this risk but are seldom incorporated into prognostic models. ICD-10 Z-codes provide standardized documentation of social determinants of health, yet their prognostic value in dialysis populations remains uncertain. This study examined whether Z-coded social determinants of health are associated with adverse cardiovascular outcomes and mortality among patients receiving dialysis for kidney failure. Methods A retrospective cohort study was conducted using the TriNetX Research Network (2010–2024), encompassing de-identified data from 103 institutions. Adults with kidney failure on chronic dialysis were stratified by the presence of ICD-10 Z-codes (Z55–Z65). Propensity score matching (1:1) balanced demographics, comorbidities, medications, and laboratory values. Primary outcomes were all-cause mortality and hospitalization; secondary outcomes included acute heart failure, myocardial infarction, stroke, cardiac arrest, bradyarrhythmias, and ventricular tachycardia. Results After matching, mean age was 62 years, and 42.4% were female in the Z-code cohort versus 41.2% in controls. Over one year, mortality occurred in 12.5% versus 10.9% (hazard ratio [HR], 1.22; 95% confidence interval [CI], 1.15–1.29) and all-cause hospitalizations in 57.3% versus 53.1% (HR, 1.22; 95% CI, 1.19–1.26). Cardiovascular complications were consistently higher in Z-coded patients, including heart failure (24.3% vs 20.5%; HR, 1.29), myocardial infarction (8.0% vs 7.1%; HR, 1.19), stroke (6.6% vs 6.2%; HR, 1.13), cardiac arrest (3.8% vs 3.1%; HR, 1.29), and bradyarrhythmias (6.9% vs 5.5%; HR, 1.33). Discussion Among patients with kidney failure, documented Z-coded social risk factors were associated with greater mortality and cardiovascular morbidity. Incorporating social determinants of health documentation into nephrology care may improve risk stratification and inform targeted interventions.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, contributing to significant morbidity and mortality worldwide. The relationship between physical exercise and AF is complex, with studies showing both beneficial and potentially adverse effects. Moreover, evidence suggests a U-shaped association between exercise intensity and AF risk. Moderate exercise has been shown to reduce AF burden by improving cardiovascular risk factors, enhancing autonomic regulation, and mitigating atrial fibrosis. In contrast, excessively high-intensity endurance exercise may increase AF risk, particularly in young athletes, due to atrial stretching, dilation, fibrosis, autonomic imbalances, and heightened inflammation. The current guidelines emphasize exercise as a core lifestyle intervention for AF management, recommending moderate-intensity aerobic activity for optimal outcomes. This review examines the current evidence on the effects of exercise on AF, identifies knowledge gaps, and proposes potential future research directions.

Abstract Background/Introduction Access to timely healthcare in disadvantaged areas remains essential to achieving equitable health outcomes, especially in regions facing structural and social vulnerabilities. Purpose To assess wait times for cardiovascular disease consultations in US-Mexico border regions compared to non-border regions among US Veterans. Methods This cross-sectional analysis included all veteran cardiovascular consultations referred through the Veterans Choice Program to community healthcare centers from 2014 to 2020. We obtained the average wait times in days for consultation approval, scheduling, completion, and total wait time from approval to consultation completion. All consultations were classified by county of residence as either within a US-Mexico border region or a non-border region, as per the La Paz Agreement. Multivariable linear regression analyses were performed to evaluate the impact of border status on average wait times. ARIMA modeling was completed to estimate forecasted wait times during the COVID-19 pandemic. Results A total of 485,158 consultations related to outpatient cardiovascular disease were retrieved from the VA Healthcare System. Amongst these, 461,696 of the consultations were placed in non-border regions whereas 23,462 consultations were placed in US-Mexico border regions. The most common consultation requests were made for general cardiology clinic (n=365,227), echocardiography (n=32,017), cardio-pulmonary rehabilitation (n=40,699), and cardiac stress testing (n=16,258). Wait times for cardiovascular disease consultations were not higher in US-Mexico border counties than in non-border regions. For electrophysiology consultations specifically, border counties exhibited significantly shorter total wait times (coeff = -10.81, p=0.008). Additionally, while the COVID-19 pandemic led to increased total wait times in non-border areas (Figure 1), total wait times in border regions remained stable during this period (Figure 2). Conclusions Our findings suggest that US-Mexico border region status does not result in longer wait times for cardiovascular disease consultations, with the exception of shorter wait times for electrophysiology services. This study provides important insights into the unique dynamics of healthcare access in structurally disadvantaged regions, highlighting that wait times are not a significant barrier to cardiovascular care in border areas, and emphasizes the need to consider regional differences in healthcare delivery policies.

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.