Citations

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes.

Coronary microvascular dysfunction (CMD) has emerged as a crucial contributor to cardiovascular morbidity and mortality, particularly in patients with ischemia and non-obstructive coronary arteries (INOCA). The condition arises from a complex interplay of structural and functional abnormalities within the small coronary vessels, driven by underlying molecular mechanisms including endothelial nitric oxide synthase (eNOS) uncoupling, oxidative stress, and chronic inflammation. Lipid metabolism plays a central role in this pathology, especially in the setting of elevated low-density lipoprotein cholesterol (LDL-C). Furthermore, the protective capacity of high-density lipoprotein (HDL) is increasingly understood to depend on its functionality rather than absolute levels, as it can become dysfunctional and pro-inflammatory in pathological states. Emerging evidence has identified lipoprotein(a) [Lp(a)] and triglyceride-rich lipoproteins as significant, independent contributors to microvascular injury. Comprehensive clinical assessment of microvascular dysfunction therefore requires integration of advanced lipid profiling, including apolipoprotein B (ApoB), [Lp(a)], and the triglyceride-glucose (TyG) index with invasive and non-invasive measures of coronary flow reserve to more precisely stratify risk. In this narrative review, we synthesize current observational, mechanistic, and early interventional data linking diverse lipid phenotypes to coronary microvascular dysfunction. We propose a concept of lipid-driven CMD endotypes, such as ApoB-/particle overload, dysfunctional HDL, Lp(a)-mediated risk, and metabolic/TyG-high states, and map these to a practical, mechanism-informed management framework. While intensive LDL-C lowering with high-intensity statins and combination therapy remains guideline-directed care for high-risk patients, evidence for dedicated microvascular benefit from newer lipid and cardiometabolic agents is still largely hypothesis-generating. A personalized approach that aligns lipid phenotyping, CMD endotyping, and existing guideline-based therapies may help refine risk assessment and inform future trials.

This cohort study examines the association of bruton tyrosine kinase inhibitor therapy in B-cell malignant disease with increased risk of pericardial effusion and cardiovascular outcomes.

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.

Abstract Aims Pre-capillary pulmonary hypertension (PHTN), typically due to intrinsic pulmonary pathology, requires right heart catheterization (RHC) for diagnosis. ePLAR, the echocardiographic pulmonary to left atrial ratio [calculated: tricuspid regurgitation maximum velocity (TRVmax) divided by mitral valve E to e′ wave ratio (E/e′)], has been demonstrated as a promising non-invasive surrogate for characterization of pre-capillary PHTN. As such, this study aimed to further evaluate the diagnostic utility of ePLAR in a large cohort of patients with PHTN. Methods and results All RHCs between 2018 and 2023 across three tertiary centres were retrospectively reviewed. Patients with PHTN were characterized as pre-capillary or post-capillary (isolated or combined). Concurrent ePLAR was calculated. ROC analysis evaluated the accuracy of ePLAR in detecting pre-capillary PHTN. Overall, 2235 patients had PHTN by RHC, mean age 63.5 ± 14.7 years, 53.5% male. Of these, 43.3% had pre-capillary PHTN. ROC analysis demonstrated ePLAR to have good accuracy in detection of pre-capillary PHTN (AUC 0.760), with a cutoff of 0.20 m/s demonstrating a sensitivity and specificity of 77% and 60% respectively. This persisted on sensitivity analysis for contemporaneous investigations and for right ventricular function. Conclusion In this large cohort, ePLAR was validated as an accurate tool for identifying patients with pre-capillary PHTN. This widely available, non-invasive, and reproducible parameter can have an important role in the work up of PHTN and may guide the need for invasive testing.

Background Immune‐checkpoint inhibitors (ICI) are associated with adverse cardiac events. Although troponin elevation is a diagnostic criterion for ICI‐related myocarditis (ICIrM) and myocardial infarction, data on other causes of troponin elevation and their outcomes in ICI‐treated patients are limited. Methods All patients treated with ICI who had hs‐TnT (high‐sensitivity troponin T) measured at a multicenter institution from 2011 to 2022 were included. Clinical data, outcomes (cardiac death, heart failure (HF), major adverse cardiovascular events [myocardial infarction, stroke, heart failure]), and cause of hs‐TnT elevation were assessed. Risks of cardiac events were compared across hs‐TnT elevation causes. Results Of 5423 patients treated with ICI, 1669 had post‐ICI hs‐TnT measurement (mean age 68.7±11.3 years, 58.3% male), with 1‐year follow‐up. Hs‐TnT elevation in patients with ICIrM (n=59) was associated with the highest risk for cardiac death (hazard ratio [HR], 52.7 [95% CI, 11.7–238.0], P <0.001), followed by hs‐TnT elevation due to heart failure (HR, 15.9), myocardial infarction/type 2 ischemia (HR, 11.6), and infection/sepsis (HR, 5.7), compared with those with no hs‐TnT elevation. ICIrM also carried highest risk for major adverse cardiovascular events (HR, 8.2, [95% CI, 4.4–15.3], P <0.001), followed by myocardial infarction/type 2 ischemia (HR, 8.1), heart failure (HR, 7.6), pulmonary embolus (HR, 5.1), infection/sepsis (HR, 4.1), and indeterminate cause (HR, 2.4). Among ICIrM, HsTnT value >576 ng/L best predicted risk for cardiac death and >319 ng/L for major adverse cardiovascular events. Conclusions Hs‐TnT elevation after ICI therapy is associated with increased risk of cardiac events, particularly in ICIrM, and a graded prognostic association depending on the cause of hs‐TnT elevation. Identifying the underlying cause and troponin thresholds may guide risk stratification and management.

Abstract Introduction Cancer therapy-related cardiac dysfunction (CTRCD) after anthracycline-based therapy is a dreaded complication due to its significant morbidity and mortality. Sodium-glucose cotransporters-2 inhibitors (SGLT2i) have been shown to improve cardiovascular outcomes in multiple scenarios. However, its potential benefits in patients who develop CTRCD due to anthracycline-based chemotherapy are not well established. Purpose To investigate the benefits of SGLT2i in cancer patients who develop CTRCD secondary to anthracycline-based chemotherapy. Methods All patients who underwent anthracycline therapy between 2002 and 2022 across three tertiary centers and had a diagnosis of moderate or severe CTRCD according to 2022 European Society of Cardiology (ESC) guidelines were evaluated. Baseline characteristics, echocardiograms pre and post chemotherapy, and mortality were reviewed. Kaplan Meier analysis was conducted to compare all-cause and cardiovascular mortality between patients treated with SGLT2i (both patients on chronic SGLT2i who continued their therapy following CTRCD and those who commenced the treatment after CTRCD diagnosis) and those not receiving this therapy. Results In total, 398 patients diagnosed with moderate or severe CTRCD were included; median age was 66.82 (58.07, 74.46) years and 59.29% were males. Among them, 39 (9.80%) received SGLT2i. Mortality rates at 1- and 5-years were significantly lower among patients who were on SGLT2i treatment compared to patients who were not (8.40% vs 46.30%, p= 0.0001 and 17.30% vs 65%, p= 0.0001, respectively) (Figure1). Furthermore, cardiovascular mortality rate at 5 years was significantly lower in patients receiving SGLT2i compared to patients not receiving this therapy (0% vs 11%, p= 0.046). Conclusions SGLT2i may have survival benefit in cancer patients who develop CTRCD secondary to anthracycline-based chemotherapy and might be considered as an adjunctive treatment to improve survival outcomes in this population.Figure 1.Kaplan-Meier Curves

Advances in plaque imaging have transformed cardiovascular diagnostics through detailed characterization of atherosclerotic plaques beyond traditional stenosis assessment. This review outlines the clinical applications of varying modalities, including dual-layer spectral CT, photon-counting CT, dual-energy CT, and CT-derived fractional flow reserve (CT-FFR). These technologies offer improved spatial resolution, tissue differentiation, and functional assessment of coronary lesions. Additionally, artificial intelligence has emerged as a powerful tool to automate plaque detection, quantify burden, and refine risk prediction. Collectively, these innovations provide a more comprehensive approach to coronary artery disease evaluation and support personalized management strategies.