Pathangey, Girish

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.

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.

Despite advances in lipid-lowering and antithrombotic therapy, patients with acute coronary syndromes remain at elevated risk for recurrent events due to persistent atherosclerotic inflammation. This review evaluates inflammation as a therapeutic target in secondary prevention and discusses established, investigational, and emerging strategies. Colchicine, now FDA-approved for cardiovascular risk reduction, lowered major adverse cardiovascular events in COLCOT and LoDoCo2. Canakinumab (IL-1β inhibition) reduced recurrent events in proportion to IL-6 and hsCRP suppression, while ziltivekimab (IL-6 inhibition) achieved profound biomarker reductions but remains investigational. Early-phase studies of anakinra (IL-1 receptor antagonist) and dapansutrile (oral NLRP3 inhibitor) showed anti-inflammatory effects in early trials, whereas varespladib and darapladib illustrated the challenges of targeting lipid-associated pathways. Beyond direct immunomodulators, GLP-1 receptor agonists and SGLT2 inhibitors provide both cardioprotective and anti-inflammatory benefits, reinforcing their expanding role post-ACS. Additional emerging avenues include triptolidiol, dasatinib, and BTK or JAK/STAT inhibitors, while novel approaches, such as nanozyme delivery systems and CRISPR-based editing, extend the therapeutic horizon. This review highlights the potential of inflammation-targeted therapies to advance secondary prevention in ACS by integrating current evidence and perspectives on future therapeutic developments.

Abstract Background Inadvertent temporary transvenous pacing (TTVP) of left ventricle (LV) is rare and likely underreported, posing significant embolic risk. We present a case of TTVP with atypical trajectory identified on computed tomography (CT) imaging. Summary A 62-year-old male with metastatic renal cell carcinoma on chemotherapy and bifascicular block presented with symptomatic bradycardia and non–ST-elevation myocardial infarction (NSTEMI), raising concerns for complete heart block on ECG. Intermittent asystole occurred during TTVP placement with appropriate capture. Post-procedure, ECG showed ventricular-paced rhythm with pseudo-right bundle branch block, and chest X-ray suggested lead placement in right ventricle; however, suboptimal echocardiography limited lead visualization. Coronary angiography revealed non-obstructive coronary artery disease. Positron emission tomography–computed tomography (PET-CT), performed to evaluate immune checkpoint inhibitor myocarditis, incidentally noted TTVP wire entering right subclavian artery and traversing to LV. Patient underwent TTVP removal, endovascular repair, and pacemaker implantation; however, course was unfortunately complicated by embolic stroke and haemorrhagic conversion. Discussion Early detection and management of lead malposition remain critical to minimizing complications. Management strategies for inadvertent lead malposition (ILM) depend on duration of implantation, clinical presentation, and associated risks. This case highlights importance of recognizing this high-risk complication, preventive strategies, and evidence-based management. While existing data primarily focus on ILM in permanent devices, further research is needed to elucidate incidence, predictors, and outcomes of ILM in TTVP, particularly in resource-limited settings.

Myocardial deformation imaging has emerged as a valuable clinical tool for assessing right ventricular (RV) systolic function, providing additional diagnostic and prognostic insights compared to traditional indices of RV function. Two-dimensional speckle-tracking echocardiography is currently the standardized method of choice for measuring RV longitudinal strain (RVLS) in clinical practice. RVLS provides a more sensitive indicator of subtle myocardial dysfunction than conventional parameters for RV function assessment (i.e., tricuspid annular plane systolic excursion, tissue Doppler systolic velocity, fractional area change, or RV ejection fraction), with utility for the risk stratification and surveillance of conditions affecting the right heart. However, accurate interpretation of RVLS requires a comprehensive understanding of RV mechanics, pathology, and loading conditions across various cardiovascular conditions, as well as the effects of image quality and technical aspects of image acquisition and tracking in RV strain measurements. This review provides an updated overview of current practical guidelines for RV strain analysis, current clinical applications, and future directions for its potential use in clinical practice.

Cardiac computed tomography (CT) has rapidly advanced, becoming an invaluable tool for diagnosing and prognosticating various cardiovascular diseases. While echocardiography and cardiac magnetic resonance imaging (CMR) remain the gold standards for myocardial assessment, modern CT technologies offer enhanced spatial resolution, making it an essential tool in clinical practice. Cardiac CT has expanded beyond coronary artery disease evaluation, now playing a key role in assessing cardiomyopathies and structural heart diseases. Innovations like photon-counting CT enable precise estimation of myocardial extracellular volume, facilitating the detection of infiltrative disorders and myocardial fibrosis. Additionally, CT-based myocardial strain analysis allows for the classification of impaired myocardial contractility, while quantifying cardiac volumes and function remains crucial in cardiomyopathy evaluation. This review explores the emerging role of cardiac CT in cardiomyopathy phenotyping, emphasizing recent technological advancements.

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.

On March 11, 2020, the World Health Organization declared coronavirus disease 2019 (COVID-19) a pandemic, and the reality of the situation has finally caught up to the widespread reach of the disease. The presentation of the disease is highly variable, ranging from asymptomatic carriers to critical COVID-19. The availability of angiotensin-converting enzyme 2 (ACE2) receptors may reportedly increase the susceptibility and/or disease progression of COVID-19. Comorbidities and risk factors have also been noted to increase COVID-19 susceptibility. In this paper, we hereby review the evidence pertaining to ACE2’s relationship to common comorbidities, risk factors, and therapies associated with the susceptibility and severity of COVID-19. We also highlight gaps of knowledge that require further investigation. The primary comorbidities of respiratory disease, cardiovascular disease, renal disease, diabetes, obesity, and hypertension had strong evidence. The secondary risk factors of age, sex, and race/genetics had limited-to-moderate evidence. The tertiary factors of ACE inhibitors and angiotensin II receptor blockers had limited-to-moderate evidence. Ibuprofen and thiazolidinediones had limited evidence.

AbstractThis study aimed to evaluate the influence of physical activity on incidence of acute mountain sickness (AMS) by continuous activity monitoring in a free-living sample of South Pole workers over the initial 72 h at altitude exposure of 2,840 m (9,318 ft). Body Media activity monitors were worn by 47 healthy participants. AMS was defined by the Lake Louise symptom questionnaire. Venous blood samples were taken at sea level and approximately 48 h after high altitude exposure. AMS incidence was 34% (n=16/47) over the first 48 h and 40% (n=19/47) over 72 h. On day 2 at high altitude, individuals with AMS demonstrated a significantly greater increase in the percent change in physical activity metrics from baseline: total energy expenditure 19±13 vs. 5±7%, total steps 65±51 vs. 10±18%, metabolic equivalent of tasks 21±13 vs. 7±13%, and time spent performing moderate to vigorous physical activity 114±79 vs. 26±27% for individuals with AMS vs. no AMS, respectively, p<0.05. In addition, erythropoietin and vascular endothelial growth factor were 1.69 and 1.75 times higher, respectively, in those with AMS. In conclusion, workers who engaged in increased physical activity and activity intensity during initial exposure to the South Pole were more susceptible to developing AMS.