Farina, Juan M

BACKGROUND: Accurate assessment of left ventricular outflow tract (LVOT) gradients is critical for hypertrophic cardiomyopathy management, yet Doppler-based measurements are technically demanding and require expertise. The objective of this work was to develop a multi-view deep learning model capable of classifying LVOT obstruction (>20 mm Hg) using routine 2-dimensional echocardiographic windows without reliance on Doppler imaging. METHODS: We trained and externally validated a cross-attention-based video-to-video fusion framework that integrated EchoPrime-derived video representations from 3 standard transthoracic echocardiographic views to classify LVOT gradients. RESULTS: Training was performed on a derivation cohort (N=1833) from a tertiary care system in the United States, with model performance evaluated on an internally held-out test set (N=275) and a Korean external validation cohort (N=46). Single-view baselines showed limited discrimination (external area under the receiver operating curves, 0.47–0.70). Conversely, the domain-specific foundational model (EchoPrime) achieved superior single-view performance (area under the receiver operating curves, 0.75–0.80 internal; 0.79–0.83 external), highlighting the importance of echo-specific pretraining and temporal modeling. The proposed multi-view fusion further enhanced predictive performance, with the late fusion model reaching an area under the receiver operating curve of 0.84 on the external cohort with significant population-shift. CONCLUSIONS: These results suggest LVOT physiology is encoded in routine 2-dimensional imaging and can be leveraged for clinically relevant gradient classification without Doppler input. The proposed artificial intelligence-guided strategy demonstrates substantial cost savings compared with the screen-all approach. By integrating complementary spatial-temporal information across multiple views, our approach generalizes robustly across populations and may enable real-time decision support, extend LVOT assessment to portable or resource-limited settings, and complement Doppler-based evaluation for longitudinal hypertrophic cardiomyopathy management.

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.

ABSTRACT Electrocardiography (ECG) is a critical diagnostic tool for identifying cardiac conditions. While standard lead positions are widely used to ensure diagnostic accuracy, alternative lead placements have been developed to address specific clinical scenarios. These alternative configurations can overcome physical or technical challenges, enhance rhythm assessment, improve signal quality, and provide greater specificity for certain conditions, ultimately enabling more personalized diagnostic strategies. This paper examines the clinical significance of alternative ECG lead positions, highlighting their advantages, limitations, and potential applications in various clinical settings.

Background: Indications for TAVR explant have been established, although limited data exist regarding pre-TAVR baseline characteristics that predict eventual explantation. Methods: The TriNetX network, a database comprising medical records from over 105 institutions, was used. Two cohorts were created: those who underwent TAVR without explant and those requiring subsequent TAVR explant and SAVR. Predictors of explantation were analyzed by multivariate models. Results: Among the 63,377 patients undergoing TAVR, 273 (0.4%) required explantation. Patients in the explant group were younger (69.1 ± 11.3 vs. 78.1 ± 8.8 years; p < 0.001), more likely to have a thoracic aortic aneurysm (TAA) (10.6% vs. 4.7%; p < 0.001) and had higher LDL levels (88.2 ± 41.3 vs. 80.7 ± 34.6 mg/dL; p = 0.011). They also had increased post-TAVR rates of acute kidney injury (9.2% vs. 5.2%; p = 0.004) and paravalvular leak (5.9% vs. 0.9%; p < 0.001). Age at TAVR (HR 1.04; CI 1.03–1.06), baseline TAA (HR 1.69; CI 1.09–2.63), history of infective endocarditis (HR 1.92; CI 1.10–3.35), and higher LDL (HR 1.02; 95% CI 1.00–1.03) were independent predictors for explantation. Conclusions: Younger age at TAVR, TAA, history of endocarditis, and elevated baseline LDL were notable predictors of explantation. These findings highlight the necessity of pre-procedural assessment and follow-up in high-risk patients to optimize TAVR durability.

Key Points Atrial fibrillation after kidney transplantation is common and is associated with poor prognosis. Artificial intelligence applied to 12-lead electrocardiograms pretransplant accurately predicts the risk of new-onset atrial fibrillation in our cohort. High-risk artificial intelligence electrocardiography score was independently associated with increased risk of mortality and allograft failure. Background Incident atrial fibrillation (AF) is common after kidney transplantation (KTx) and is associated with worse clinical outcomes. Artificial intelligence electrocardiography (AI-ECG) algorithms have demonstrated efficacy in predicting risk of new-onset AF in the general population; however, their prognostic value in KTx recipients is relatively unknown. Methods Retrospective analysis was conducted on KTx recipients without AF, with at least one pretransplant ECG between 2011 and 2021 across three tertiary centers in the United States (Mayo Clinic sites in MN, AZ, and FL). A previously validated AI-ECG algorithm estimated the probability of incident AF for each patient. Based on AI-ECG probabilities, patients were categorized into high-risk and low-risk groups, with the optimal AI-ECG score cutoff determined. The incidence of new-onset AF, allograft failure, and mortality was compared between groups. Results Overall, 6246 patients (age 53.5±13.8 years; 58.9% male) were included. Pretransplant AI-ECG probability of AF ≥5% was the optimal cutoff for high risk of incident AF (sensitivity 72%, specificity 62%). High-risk scores were associated with true new-onset AF at 30 days (adjusted hazard ratio [aHR], 2.89; 95% confidence intervals [CI], 2.05 to 4.09; P < 0.001), 3 years (aHR, 2.54; 95% CI, 1.99 to 3.26, P < 0.001), and 5 years post-transplant (aHR, 2.48; 95% CI, 1.99 to 3.09, P < 0.001). High-risk AI-ECG scores were also associated with increased mortality (aHR, 1.56; 95% CI, 1.30 to 1.88, P < 0.001) and overall allograft failure (aHR, 1.50; 95% CI, 1.30 to 1.75, P < 0.001) through the 5-year follow-up. Conclusions This pretransplant AI-ECG parameter identified patients at increased risk of new-onset AF post-KTx and provided prognostic utility. Overall, this easy to obtain tool allows for risk stratification of patients who may benefit from closer monitoring, targeted risk factor modification, and early intervention.

Acute exposure to hypoxia will induce right ventricular (RV) hemodynamic changes and may increase the degree of right-to-left shunting, which can contribute to dyspnea at altitude. In this retrospective study, 125 patients (median age 66 years; 50.4% women) with unexplained dyspnea at altitude underwent hypoxic simulation testing (HST) with transthoracic echocardiography (TTE). During simulated hypoxia (mode (Min-Max) altitude: 8000 (6000–18,000) ft, were observed a significant decrease in oxygen saturation (97% (95–98) vs. 88% (82–92), p < 0.001) and RV free wall longitudinal strain (−19.6 ± 3.99% vs. −17.3 ± 4.17%, p < 0.01), an increase in RV systolic pressure (RVSP: 26 (23–30.5) vs. 29 (25–36.5) mmHg, p < 0.001). No significant changes were observed in TAPSE (20 (18–23) vs. 20 (19–24) mm) or S wave (0.12 (0.11–0.14) vs. 0.13 (0.12–0.14) m/s). Right-to-left shunting was present in 47.2% of patients and 11.9% exhibited inducible shunting only under hypoxia. However, under hypoxia, there were no significant differences in RV hemodynamic parameters or saturation between those with and without shunting. TTE with HST is useful to characterize both cardiopulmonary response and the dynamic changes in right-to-left shunt behavior under hypoxic stress.

Introduction: Mitral valve transcatheter edge-to-edge repair (M-TEER) has emerged as a minimally invasive option for high-risk surgical candidates with severe and symptomatic mitral regurgitation (MR), but post-procedure residual mitral valve (MV) dysfunction remains a significant concern. This study evaluates the clinical utility of combining artificial intelligence applied to electrocardiograms (ECG-AI) for diastolic dysfunction (DD) grading and the echocardiography-derived velocity time integral of the MV and left ventricular outflow tract ratio (VTIMV/LVOT) in predicting prognosis in patients post-M-TEER. Methods: A retrospective analysis of patients who underwent M-TEER between 2014 and 2021 was conducted. Patients were categorized based on VTIMV/LVOT and ECG-AI scores into three groups: both normal parameters, either abnormal parameter, or both abnormal parameters to compare outcomes (mortality, major adverse cardiovascular events [MACE], and the need for subsequent MV reintervention) using Kaplan–Meier analysis, multivariable Cox regression models, and net reclassification improvement. Results: Overall, 250 patients were included; the median age was 79.5 (IQR: 73.1, 84.6) and 66.4% were male. The combined abnormal VTIMV/LVOT (≥2.5) and ECG-AI score for DD (>1) was associated with higher risk of one-year mortality (adjusted HR: 4.56 [1.04–19.89], p = 0.044) and MACE (adjusted HR: 3.72 [1.09–12.72], p = 0.037) compared to patients with both normal parameters. Conclusions: This study highlights the potential additive value of integrating VTIMV/LVOT and ECG-AI scores as a prognostic tool for a personalized approach to the post-operative evaluation and risk stratification in M-TEER patients.

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.