Arsanjani, Reza

Abstract Introduction Despite its prevalence, risk stratification for tachycardia-induced cardiomyopathy remains poorly understood. A deep learning model developed at Mayo Clinic uses standard 12-lead electrocardiograms (ECGs) to predict reduced LVEF (≤35%) in the general population with high accuracy (AUC 0.93) Purpose We investigated the performance of AI-ECG in predicting systolic dysfunction among patients with atrial fibrillation and rapid ventricular response, and compared it with NT-proBNP. Methods We conducted a retrospective study (IRB approval #25-000904) of adult patients (age ≥ 18 years) evaluated across our Clinic enterprise with atrial fibrillation and rapid ventricular response (heart rate >100 bpm), including AF occurring in-hospital and AF at presentation, between January 2017 and May 2025. We excluded patients with a known history of heart failure with reduced EF (<50%). We also collected data on NT-proBNP levels when available within one week of the ECG and echocardiogram. Results A total of 11,801 patients were included, with a mean age of 73.2 +- 12.6 years, 48.6% female and 94.8% White. A combination of AI ECG low EF probability cutoff of 75% had specificity of 96% and 98% NPV in ruling out LVEF < 35%. A combination of AI ECG low EF probability cutoff of 17% and LVEF <35% provided the best balance of sensitivity and specificity, 66% and 78%, respectively with an accuracy of 78%. NT-proBNP data was available on 5,279 patients (48%). Mean NT-proBNP level was 4090 pg/mL. Based on ROC curve analysis, AI ECG was superior to NT-proBNP in predicting LVEF <35% (AUC 0.72 vs 0.59), with accuracy of 77% versus 47% at Youden’s cutpoints of AI-ECG probability of 17% and NT-proBNP level of 1767 pg/mL, respectively Conclusions Our study demonstrates the utility of AI-ECG in predicting reduced LVEF among patients with AF RVR. AI-ECG demonstrated excellent specificity and NPV in ruling out reduced LVEF, serving as a powerful clinical screening tool to assist with the pharmacologic choice of rate control and could change the timing of further echocardiographic evaluation. Our study offers novel insights paving the way for future investigations to help fine-tune and further delineate the role of AI ECG in risk stratifying patients with suspected tachycardia-induced cardiomyopathy, guiding therapeutic and diagnostic clinical decisions

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.

This cross-sectional study investigates the association between lipoprotein(a) levels and risk of ventricular arrhythmias among patients with heart failure with reduced ejection fraction receiving implantable cardioverter-defibrillator therapy.

Abstract Background Transthoracic echocardiography (TTE) is the most commonly used modality for the diagnosis and follow-up of cardiac diseases. During routine TTE, surrounding extracardiac structures are often visualized, including the lungs, pleural spaces, oesophagus, diaphragm, and sub-diaphragmatic structures such the liver, stomach, bowel, and abdominal aorta. Pathologic conditions affecting these organs may exert extrinsic compression on the cardiac chambers, leading to diagnostic uncertainty or mimicking cardiac disease by producing symptoms, such as dyspnoea, fatigue, chest pain, or palpitations. Case summary We describe five patients who underwent routine TTE and were incidentally found to have unsuspected gastrointestinal abnormalities. The findings subsequently confirmed by other imaging modalities included hiatal hernia, distended bowel loops, oesophageal carcinoma, and oesophageal varices. Reporting of these findings impacted the diagnosis and management of the cases. Conclusion Awareness of the sonographic appearance of gastrointestinal during TTE is essential, as these findings may represent clinically significant pathology that mimics or contributes to cardiac symptoms. Recognition of such findings may be enhanced by modified echocardiographic views, with confirmatory evaluation using chest radiography, computed tomography, or gastrointestinal endoscopy as appropriate

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.

Background: The timing of coronary artery bypass grafting (CABG) in patients with non-ST-elevation myocardial infarction (NSTEMI) may critically impact clinical outcomes, yet the ideal surgical window remains unclear. We aimed to evaluate the association between time-to-CABG and in-hospital outcomes among NSTEMI admissions undergoing CABG. Methods: This retrospective cohort study utilized the US National Inpatient Sample database from 2017 to 2021 to evaluate the association between time-to-CABG and key in-hospital outcomes, including mortality, cardiac arrest, cardiogenic shock, ischemic stroke, acute kidney injury, and blood transfusion rates, while assessing length of stay (LOS) and hospital charges exclusively among survivors. Adult admissions with principal diagnosis of NSTEMI who underwent CABG without prior interhospital transfer were grouped by the calendar day of CABG from admission (0, 1, 2, 3, 4, 5, 6, and ≥7). Multivariate regression models were used to assess the outcomes across these eight groups. Results: Of 142,200 included admissions, same-day CABG (hospital day 0) was associated with the highest rates of in-hospital mortality, cardiac arrest, cardiogenic shock, and ischemic stroke. The adjusted odds of mortality, cardiac arrest, and cardiogenic shock were lower among admissions undergoing CABG between days 2 and 5. Likewise, the adjusted odds of ischemic stroke were lowest when CABG occurred on days 2 to 4. The odds of AKI increased when CABG was delayed beyond day 6, whereas blood transfusion rates showed no statistically significant association with time-to-CABG. Among survivors, LOS and hospital charges progressively increased with delayed CABGs. Conclusions: Among NSTEMI admissions undergoing CABG, CABG performed on hospital days 2 to 4 was associated with lower adjusted odds of several adverse in-hospital outcomes compared with same-day CABG. Further prospective studies are needed to validate these results and guide clinical decision-making.

Abstract Background Accurate assessment of left ventricular outflow tract (LVOT) gradients is critical for hypertrophic cardiomyopathy (HCM) management, yet Doppler-based measurements are technically demanding and require expertise. Objective To develop a multi-view deep learning model capable of classifying LVOT obstruction (> 20mmHg) using routine 2D 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 three 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 internal held-out test set (N = 275) and a Korean external validation cohort (N = 46). Single-view baselines showed limited discrimination (external AUROCs 0.47–0.70). Conversely, domain-specific foundational model (EchoPrime) achieved superior single-view performance (AUROCs 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 AUROC of 0.84 on the external cohort with significant population-shift. Conclusions These results suggest LVOT physiology is encoded in routine 2D imaging and can be leveraged for clinically relevant gradient classification without Doppler input- proposed AI-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 HCM management.

Background In low-gradient severe aortic stenosis (AS), reduced left ventricular ejection fraction (LVEF <50%) is practically used to define low flow and prompt dobutamine stress echocardiography to discern pseudo-severe AS. In patients with preserved LVEF, stroke volume index (SVI) <35 mL/m² is typically used. However, both are volume-based surrogates. Transaortic flow rate (TAFR), calculated as stroke volume divided by left ventricular ejection time, may better reflect true flow. Nonetheless, comparative data between TAFR and established metrics remain limited. We aimed to evaluate the prognostic value of TAFR in symptomatic low-gradient severe AS. Methods We retrospectively identified patients with low-gradient severe AS (AVA ≤1 cm 2 and peak velocity (Vmax) <4 m/s or mean gradient (MG) <40 mm Hg) who underwent transcatheter aortic valve replacement at Mayo Clinic sites (2017–2023). The primary outcome was 1-year all-cause mortality. Survival was assessed using Kaplan-Meier and Cox proportional hazards models. Results Among 475 patients included (mean age 85±8 years; 49% women), 242 (51%) had TAFR <220 mL/s, 165 (35%) had EF <50%, and 221 (47%) had SVI <35 mL/m 2 . Low TAFR was significantly associated with higher 1-year mortality even after stratifying by EF or SVI. In multivariate analysis, TAFR was an independent predictor of mortality (HR 2.38; 95% CI 1.19 to 4.76, p=0.014) after adjusting for reduced LVEF, low SVI, gender, chronic kidney disease and mitral and tricuspid regurgitation. Conclusions In patients with symptomatic low-gradient severe AS, low TAFR, not SVI or LVEF, is independently associated with mortality and may offer more accurate measure of flow state for clinical staging.

Background: Disparities exist in the representation of genders in cardiovascular clinical trials. Atrial fibrillation (AF) is associated with significant morbidity and mortality; however, understanding regarding the representation of women in AF-related clinical trials remains limited. Therefore, this systematic review sought to evaluate the representation of women in AF-related clinical trials. Methods: We conducted a systematic review of clinical trials using the PubMed, Scopus, and EMBASE databases from 1996 to January 1st, 2024, focusing on AF-related lifestyle interventions, pharmacological treatments, catheter ablation, and device therapies for AF. Data extraction and analysis encompassed trial characteristics, participant demographics, and funding sources. The primary outcome was the prevalence of female enrollees, quantified through participation-to-prevalence ratios (PPRs). This was estimated overall and stratified by funding source, intervention type, and enrollment region. Results: Of the 103 clinical trials involving 218,322 participants (39.5% female), the PPR ranged from 0.00 to 1.73, with an average PPR of 1.03. Meanwhile, 43% of the trials exhibited female under-representation (PPR, <0.8). University-funded trials showed higher female enrollment (mean PPR, 0.951) compared to industry/government-funded trials (mean PPR, 0.800). No differences were observed in the representation of women when comparing enrollment regions or intervention types. Conclusions: Despite advancements in AF management, gender disparities persist in AF-related clinical trial representation, particularly in industry/government-funded studies compared to university-funded trials. Thus, addressing implicit biases and enforcing sex equality guidelines are critical steps toward more inclusive cardiovascular research.