Dadwal, Surbhi

Background: Heart transplantation is the definitive treatment of end-stage heart failure and is a complex process involving coordination in procurement, preservation, and transport of a donor organ to a recipient. There are different heart transport methods, including the traditional static methods of Cooler and Ryobi and advanced dynamic methods, including SherpaPak and the Organ Care System (OCS). The aim of the study is to compare the post-transplant outcome of these in patients bridged with Impella 5.5. Method: A retrospective study was conducted between 2020 and 2025 after IRB approval. Patient demographics and clinical data was extracted from the medical record. Cox proportional hazards regression model was applied to identify independent predictors of mortality across 30-day, 90-day, and 1-year intervals. Results: 123 patients were included in the analysis. Age, EF and Race were similar between all groups. The transport method was a significant predictor of 1-year mortality. Use of OCS (HR=0.022, p value <0.05) and SherpaPak (HR=0.066, p value <0.05) was associated with a significant decrease in mortality compared to traditional cooler use. Across 30-day, 90-day, and 1-year intervals, OCS showed the strongest survival benefit, while SherpaPak also demonstrated reduced mortality, but less significant compared to OCS. Conclusion: Transport method is a strong predictor of 1-year post-transplant mortality, with OCS showing the most pronounced effect in reducing the risk of 1-year mortality as well as mortality across 30-day and 90-day intervals. These findings support broader adoption of advanced transport systems in clinical practice to improve post-transplant outcomes.

Background: Mechanical support strategies used for patients in shock prior to heart transplantation (OHT) correlates with post-transplant outcomes. OHT timing in patients supported by Impella 5.5 remains an evolving decision point, influenced by patient stability and donor logistics. With the emergence of organ transport technologies, we sought to determine whether the choice of system was associated with variations in pre-transplant support duration. Method: A retrospective review between Jan 2020 and May 2025 was completed. Patients were categorized by donor transport technology: traditional cooler, Organ Care System (OCS), or SherpaPak. The primary outcome was duration of Impella support (days) prior to transplant. Group-wise comparisons were performed using Kruskal-Wallis testing. Result: A total of 129 patients were assessed across three transport methods: traditional cooler:12, OCS: 35, SherpaPak: 84. Median age at transplant was 58 (51-65) years with EF of 17% (15-22). Cold ischemic time differed significantly across transport technologies (Kruskal-Wallis H = 77.4, p < 0.001) and was longest in OCS cases (417 min), compared to SherpaPak (236 min) and traditional cooler (234 min). The median duration of Impella support prior to OHT was 27 days for organs transported via traditional cooler, 25 days for those using OCS, and 28.5 days for SherpaPak with near-significant difference by transport method (H=5.77, p=0.056). There was no significant difference of rejection within the first year after OHT. Conclusion: Donor transport technology choice may serve as a surrogate for broader transplant logistics and selection strategies as evidenced by significant differences in ischemic time and near-significant trends in Impella support duration.

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.