Abdalla, Hesham

Multimodality imaging plays a pivotal role in ventricular tachycardia (VT) ablation by providing critical insights into arrhythmogenic substrates and guiding procedural strategies. This updated review explores the integration of various imaging techniques, including echocardiography, cardiac magnetic resonance (CMR), multidetector computed tomography (MDCT), electroanatomical mapping (EAM), and nuclear imaging, to optimize VT ablation outcomes. Echocardiography, particularly transthoracic echocardiography (TTE), is an essential first-line tool for evaluating structural heart disease and left ventricular function. Moreover, echocardiography aids in risk stratification and procedural planning by detecting regional wall motion abnormalities and thrombus formation. Meanwhile, intracardiac echocardiography (ICE) enhances ablation precision by providing real-time catheter visualization, improving procedural success, and reducing complications such as cardiac tamponade. Nonetheless, CMR is the gold standard for myocardial tissue characterization, enabling the identification of scar burden and conduction channels critical for VT pathogenesis. Late gadolinium enhancement (LGE) facilitates preprocedural planning by localizing arrhythmogenic substrates, predicting VT recurrence risk, and informing ablation strategies. Additionally, T1- and T2-mapping techniques refine the assessment of myocardial fibrosis and inflammation, further improving patient selection and outcomes. MDCT complements CMR by offering high-resolution anatomical visualization and aids in delineating scar distribution, epicardial fat, and vascular structures, informing safe and effective ablation approaches. The integration of these imaging modalities significantly enhances VT ablation precision, reduces recurrence rates, and improves patient outcomes.

Abstract: Cardiovascular disease (CVD) remains the leading cause of death worldwide. While advances in treatment have improved outcomes, the greatest gains in public health will come from primary prevention. Risk stratification is the foundation of CVD prevention. Traditional tools, such as pooled cohort equations (PCEs) and SCORE2, do not capture non-traditional risk factors and individual variability, driving efforts to refine existing approaches. We critically discuss emerging risk determinants for primary CVD prevention. Coronary artery calcium scoring can reclassify CVD risk but has limitations related to cost, access, and unvalidated improvements in clinical endpoints. Biomarkers such as apolipoprotein B and lipoprotein(a) may identify residual risk beyond LDL-C, particularly in patients with lipid discordance, but remain underused due to uncertain thresholds and the lack of prospective triallevel outcome data. Trials of colchicine in primary prevention have yielded mixed results, making the role of targeting inflammation unclear in CVD prevention. Polygenic risk scores can stratify genetic risk but face challenges in sensitivity, specificity, and generalizability. Multiomics approaches, while offering the promise of deeper phenotyping, lack established clinical applications and consensus on when their use is appropriate. Artificial intelligence may allow better prediction across cohorts but requires broader validation. Clonal hematopoiesis of indeterminate potential, a marker of inflammation and age-related CVD risk, lacks evidence supporting routine screening or intervention in younger, asymptomatic individuals. The proliferation of new risk stratification tools poses a key challenge: determining when additional data meaningfully alter clinical decisions. Until robust outcome-based evidence emerges, risk enhancers should guide, but not dictate, therapy in selected patients.