Arsanjani, Reza

This cohort study evaluates the association between measured lipoprotein(a) level and incident venous thromboembolism.

Background Pericarditis may be associated with significant morbidity and mortality and can be a clinically challenging entity to diagnose. There is a clinical need for automated artificial intelligence (AI) algorithms to act as a screening aid to assist with the early and accurate detection of pericarditis. Methods We developed p-Cal, a browser-based pericarditis risk calculator that fuses the patient clinical data and 12-lead ECG image to estimate risk for pericarditis. The ECG encoder was designed based on a pretrained MedCLIP Vision Transformer and random forest classifier was used for the clinical tabular data. These two modalities were fused at the decision-level using a meta classifier. Model reasoning for both modalities was assessed using advanced explainable AI techniques—attention maps from the transformer model were used to interpret ECG images, while SHapley Additive exPlanations (SHAP) force plots were applied to analyse the tabular data. Results A total of 6508 patients (mean age 54.8±16.1 years, 50.9% female) were used for training and internal validation of the AI model, of whom 902 patients had a confirmed diagnosis of pericarditis (13.8%) and the remainder were normal controls. On 1302 hold-out test patients, the Fusion Model demonstrated the best discriminatory performance, achieving an area under the curve (AUC) of 0.89, which surpassed the ECG (AUC = 0.85) and the clinical data only models (AUC = 0.74) performance. On external validation in the Medical Information Mart for Intensive Care (MIMIC) Database, the fusion model achieved an AUC of 0.81. Conclusion A late fusion AI model integrating tabular clinical electronic medical record data and ECG signal has robust predictive capability for pericarditis as a screening tool.

Background Immune checkpoint inhibitors (ICI) improve cancer outcomes but are associated with increased atherosclerotic cardiovascular risk. Opportunistic coronary artery calcium (CAC) assessment from routine non-gated computed tomography (CT) using artificial intelligence (AI) may enable rapid cardiovascular risk stratification in this population. We evaluated the association between AI-derived CAC (AI-CAC) scores and 1-year myocardial infarction (MI) and all-cause mortality in patients receiving ICI therapy. Methods We conducted a retrospective multicenter study across three Mayo Clinic sites including patients who underwent chest CT within 3 months prior to ICI initiation between 2011 and 2022. Patients with prior coronary revascularization were excluded. AI-CAC scores derived from non-gated CT scans were categorized as 0, 1–99, 100–399, and ≥400. Cox regression analyses assessed associations with 1-year MI and all-cause mortality. Results Among 1,873 patients (mean age 66 years; 43% women), lung cancer was most common (50.3%), and 91% had at least one cardiovascular risk factor. Higher CAC burden was associated with increased 1-year MI risk (overall p=0.0032). AI-CAC was not associated with all-cause mortality (p=0.79), likely reflecting high cancer-related mortality (50.7% at 1 year). Only 15% of patients were receiving statins despite one-third having CAC >100. Conclusions Opportunistic AI-CAC assessment from routine CT scans provides modest prognostic value for MI risk among patients receiving ICI therapy and may identify opportunities to optimize preventive cardiovascular care. Elevated CAC should prompt risk factor modification rather than influence ICI treatment decisions. Prospective studies are warranted to evaluate clinical implementation and outcomes.

Objective To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large language model (LLM)-assisted workflow, against manual chart adjudication for identifying cardiovascular events. Design Retrospective diagnostic accuracy study. Setting Three sites within a single US tertiary health system. Participants Two adult cohorts with previously adjudicated cardiovascular outcomes were included. Cohort 1 included 2258 patients treated with immune checkpoint inhibitors, and Cohort 2 included 1426 patients who underwent transcatheter aortic valve replacement. Primary and secondary outcome measures The reference standard was clinician manual chart adjudication. Outcomes included ischaemic stroke or transient ischaemic attack, myocardial infarction (MI), heart failure (HF) exacerbation or hospitalisation and a composite major adverse cardiovascular events (MACE) outcome. Automated retrieval methods included International Classification of Diseases (ICD) codes, primary diagnosis, problem list and a zero-shot LLM workflow. Area under the (receiver operating characteristic) curve (AUC), sensitivity, specificity and net reclassification improvement were assessed. Results In Cohort 1, the LLM achieved the highest AUC for stroke (0.920; 95% CI 0.881 to 0.958), MI (0.938; 95% CI 0.905 to 0.971) and composite MACE (0.880; 95% CI 0.854 to 0.907), whereas ICD-based retrieval had a higher AUC for HF (0.882; 95% CI 0.845 to 0.918 vs 0.873; 95% CI 0.831 to 0.914). In Cohort 2, the LLM achieved the highest AUC for all evaluated outcomes: stroke (0.915; 95% CI 0.862 to 0.968), MI (0.928; 95% CI 0.839 to 1.000), HF (0.844; 95% CI 0.803 to 0.884) and composite MACE (0.862; 95% CI 0.829 to 0.895). In Cohort 1, differences in AUC between the LLM and ICD methods were not statistically significant across outcomes, whereas in Cohort 2 the LLM showed significantly higher AUC for stroke and composite MACE. Conclusion In this multisite retrospective validation study, the LLM-assisted workflow showed strong but context-dependent performance for identifying cardiovascular events from the EMR. Performance varied by outcome and cohort, and ICD-based retrieval remained competitive for some use cases. These findings support a complementary role for LLM-assisted extraction in retrospective cardiovascular outcomes research.

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.

Background:The development of symptoms or left ventricular dysfunction in severe aortic stenosis (AS) is associated with high morbidity and mortality. While elective transcatheter aortic valve replacement (TAVR) is supported by robust randomized evidence, data on TAVR performed in non-elective settings (urgent, emergent, or salvage) remain limited and heterogeneous. In these settings, outcomes may be influenced more by baseline clinical severity than by procedural factors. This study aimed to systematically evaluate the safety and effectiveness of non-elective TAVR and to compare the associated outcomes with those of elective TAVR.Methods:This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and registered with PROSPERO. The PubMed, Embase, and Scopus databases were searched for studies published after 2002 evaluating urgent, emergent, or salvage TAVR in adult patients with severe AS. Eligible studies were required to include elective TAVR as a comparator. Outcomes included mortality and major procedural complications. Prespecified era-based analyses and sensitivity analyses excluding studies at critical risk of bias were performed. Risk of bias was assessed using the ROBINS-I tool.Results:A total of 17 observational studies published between 2015 and 2025 were included, comprising 215,141 patients. Compared with elective patients, those undergoing non-elective TAVR had more advanced heart failure, higher NYHA class, greater comorbidity burden, and higher surgical risk scores, whereas baseline echocardiographic severity of AS was similar. Pooled outcomes for non-elective TAVR showed in-hospital, 30-day, and 1-year mortality rates of 5.1%, 12.4%, and 26.7%, respectively. Non-elective TAVR was associated with higher mortality at all time points compared with elective procedures; meanwhile, rates of stroke, vascular complications, and permanent pacemaker implantation were similar, while major bleeding and acute kidney injury were more frequent. Era-based analyses showed a stable relative mortality risk over time.Conclusions:Although non-elective TAVR is associated with worse outcomes than elective procedures, these differences appear to be largely driven by baseline clinical severity rather than by the transcatheter intervention. In unstable patients with severe AS, non-elective TAVR remains the most effective definitive treatment option compared with balloon aortic valvuloplasty or conservative management.The PROSPERO Registration:CRD420251239620, https://www.crd.york.ac.uk/PROSPERO/view/CRD420251239620.

BACKGROUND: Anthracyclines are a widely used and effective class of chemotherapy. However, a major limitation for their use is cardiotoxicity, manifesting as systolic dysfunction, congestive heart failure, and arrhythmias. The objective of this study was to identify genes and genetic risk variants for doxorubicin-induced arrhythmia. METHODS: We performed genome-wide association studies and meta-analysis across 2 independent nonoverlapping, genetically homogenous data sets from the Molecular Epidemiology Resource (cases N=77, controls N=1184) and Tapestry (cases N=13, controls N=172). Doxorubicin-related arrhythmia was the primary cardiac outcome, followed by investigation of a broader phenotype of cardiac events. Effects of doxorubicin on top-associated genes were assessed by quantitative polymerase chain reaction on RNA extracted from human cardiomyocytes following treatment with doxorubicin and by echocardiography in a zebrafish model of doxorubicin-induced cardiomyopathy. RESULTS: In the meta-analysis, rs12151014 at the KCTD15 locus was associated with doxorubicin-related arrhythmia at the genome-wide significance (minor allele frequency in Molecular Epidemiology Resource cases 0.195 versus 0.069 in controls; Tapestry cases 0.192 versus 0.079 in controls) P =9.01×10 −9 ; odds ratio, 3.61. MMP 16 rs1094199 was associated with the broader phenotype of arrhythmia and heart failure (minor allele frequency in Molecular Epidemiology Resource cases 0.079 versus 0.019 in controls; Tapestry cases 0.038 versus 0.015) P =1.98×10 − 8 ; odds ratio=6.15. In human cardiomyocytes, MMP16 and KCTD15 gene expression was significantly downregulated by doxorubicin, and in a zebrafish MMP16 knockout was protective of doxorubicin-induced decline in left ventricular ejection fraction. CONCLUSIONS: Genetic variants at KCTD15 and MMP16 are associated with doxorubicin-related arrhythmia and cardiac events. MMP16 is a potential cardioprotective therapeutic target.

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 Aims Nutritional counselling is a core component of cardiac rehabilitation (CR), yet the evidence base for specific dietary interventions within CR remains fragmented. This scoping review aimed to map the range of nutritional interventions evaluated in adult CR programmes, characterize their reported outcomes, and identify key gaps in current evidence and practice. Methods and results Following the Arksey and O’Malley framework and reporting according to PRISMA-ScR, a systematic search of PubMed, Embase, Web of Science, and Cochrane Library was conducted from inception to June 2026. Eligible studies included randomized controlled trials, non-randomized trials, and observational studies evaluating an explicitly described nutritional intervention within a structured CR programme in adults with established cardiovascular disease. Of 2765 identified records, 35 studies met eligibility criteria. Interventions spanned Mediterranean and heart-healthy dietary patterns, calorie-restriction and weight-loss programmes, comprehensive lifestyle interventions, structured nutrition education and counselling, supplementation, functional foods, and individualized diet prescriptions. Across categories, improvements in body weight, central adiposity, and adherence to cardioprotective dietary recommendations were commonly reported, particularly when nutrition was delivered with repeated contact and individualized support. Effects on lipid profiles, inflammatory markers, blood pressure, functional capacity, and clinical outcomes were more variable. Conclusion Nutritional interventions within CR were most consistently associated with improvements in intermediate behavioural, anthropometric, and cardiometabolic outcomes, particularly when structured and reinforced over time. However, heterogeneity in study design, intervention delivery, comparator groups, outcome reporting, and follow-up duration precludes definitive conclusions about comparative efficacy, optimal intervention format, or the magnitude of benefit. Greater standardization, longer follow-up, and further evaluation of personalized nutritional strategies are needed to strengthen the nutritional component of CR.