Ahluwalia, Vibhor

We report the rare coexistence of extensive coronary-cameral fistulas (CCFs) with aneurysmal dilatation of coronary arteries and an incidental left ventricular papillary fibroelastoma (PFE) in a female in her early 70s who presented with chest pain and lower extremity oedema. Multimodality imaging, including transoesophageal echocardiography, cardiac CT and coronary angiography, confirmed the diagnosis. The PFE was surgically resected to prevent embolic complications, while the CCFs were managed conservatively due to their haemodynamic insignificance. This case demonstrates that even extensive CCFs may be haemodynamically insignificant, while a small PFE can pose a significant embolic threat, justifying disparate management strategies guided by multimodality imaging and haemodynamic assessment.

ABSTRACT Background Patients with hypertrophic obstructive cardiomyopathy (HOCM) were excluded from all major trials for transcatheter aortic valve replacement (TAVR). “Suicide left ventricle” occurring after TAVR is postulated to occur as a result of chronic pressure overload from fixed obstruction that is acutely relieved after the valve deployment resulting in worsening left ventricular outflow tract obstruction. Aims The aim of this study was to determine the clinical and readmission outcomes in HOCM patients undergoing TAVR. Methods Data was extracted from the National Inpatient and National Readmission 2016−2020 Databases. We performed multivariable Logistic regression analysis to determine the odds of in‐hospital mortality and other relevant clinical outcomes in patients undergoing TAVR with and without comorbid HOCM. Multivariable Cox regression analysis was used to assess 30‐day hospital readmission in HOCM patients undergoing TAVR. Results We identified 296,670 patients who underwent TAVR, of whom 534 (0.2%) had comorbid HOCM. The patients undergoing TAVR with comorbid HOCM had more than threefold higher risk of in‐hospital mortality (adjusted odds ratio: 3.47, 95% CI 1.37–8.81, p = 0.009) when compared to patients without HOCM. Additionally, patients undergoing TAVR with comorbid HOCM had a higher risk of atrioventricular blocks, cardiogenic shock, acute kidney injury, and the need for intubation. There was no difference in readmission outcomes post‐TAVR among patients with and without HOCM. Conclusion Our analysis of a large, real‐world cohort of patients undergoing TAVR showed that patients with HOCM have a significantly higher risk of in‐hospital mortality and procedural complications when compared to patients without HOCM.

Heart failure, a significant global health burden, is divided into heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF), characterized by systolic dysfunction and diastolic stiffness, respectively. While HFrEF benefits from pharmacological and device-based therapies, HFpEF lacks effective treatments, with both conditions leading to high rehospitalization rates and reduced quality of life, especially in older adults with comorbidities. This review explores the role of artificial intelligence (AI) in advancing autonomic neuromodulation for heart failure management. AI enhances patient selection, optimizes stimulation strategies, and enables adaptive, closed-loop systems. In HFrEF, vagus nerve stimulation and baroreflex activation therapy improve functional status and biomarkers, while AI-driven models adjust stimulation dynamically based on physiological feedback. In HFpEF, AI aids in deep phenotyping to identify responsive subgroups for neuromodulatory interventions. Clinical tools support remote monitoring, risk assessment, and symptom detection. However, challenges like data integration, ethical oversight, and clinical adoption limit real-world application. Algorithm transparency, bias minimization, and equitable access are critical for success. Interdisciplinary collaboration and ethical innovation are essential to develop personalized, data-driven, patient-centered heart failure treatment strategies through AI-guided neuromodulation.

Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency and decision-making, particularly in managing VVs through improved risk factor identification, diagnosis, and treatment planning. Objective: This abstract explores the role of AI in VV management, focusing on its applications in risk detection, image analysis, treatment planning, and surgical interventions, while addressing challenges to its widespread adoption. Methods: AI leverages advanced techniques such as computer vision and deep learning to analyze patient data, including medical history, symptoms, physical examinations, and imaging (e.g., ultrasounds, venography). It identifies patterns in large datasets to support personalized treatment plans, early risk detection, and disease severity assessment. Results: AI demonstrates promise in automating VV detection and classification, assessing disease severity, and aiding treatment planning. It enhances surgical interventions through preoperative planning, intraoperative navigation, and recurrence risk prediction. However, its adoption is limited by a lack of large-scale studies, concerns over accuracy, and the need for regulatory and ethical oversight. Conclusion: AI has the potential to revolutionize VV management by improving diagnosis, treatment precision, and patient outcomes. Further research, validation, and integration are critical to overcoming current limitations and fully realizing AI’s capabilities in clinical practice.