Citations

Background. Normothermic regional perfusion (NRP) has emerged as a vital technique in organ procurement, particularly in donation after circulatory death (DCD) cases, offering the potential to optimize organ utilization and improve posttransplant outcomes. Recognizing its significance, the American Society of Transplant Surgeons (ASTS) convened a work group to develop standardized recommendations for abdominal NRP in the United States. Methods. The workgroup, comprising experts in NRP, DCD, and transplantation, formulated recommendations through a collaborative process involving revisions and approvals by relevant committees and the ASTS council. Four key areas were identified for standardization: Preprocedure communication, NRP procedure, Terminology and documentation, and Mentorship/credentialing. Results. The recommendations encompass a range of considerations, including preprocedure communication protocols to facilitate informed decision-making by transplant centers and organ procurement organizations, procedural guidelines for NRP teams, uniform terminology to clarify the NRP process, and standards for mentorship and credentialing of NRP practitioners. Specific recommendations address logistical concerns, procedural nuances, documentation requirements, and the importance of ongoing quality assurance. Conclusions. The standardized recommendations for abdominal NRP presented in this article aim to ensure consistency, safety, and efficacy in the organ procurement process. By establishing clear protocols and guidelines, the ASTS seeks to enhance organ utilization, honor donor wishes, and uphold public trust in the donation process. Implementation of these recommendations can contribute to the advancement of NRP practices and improve outcomes for transplant recipients.

Background: Pancreas transplantation is a crucial surgical intervention for managing diabetes, but it faces challenges such as its invasive nature, stringent patient selection criteria, organ scarcity, and centralized expertise. Despite the steadily increasing number of pancreas transplants in the United States, there is a need to understand global trends in interest to increase awareness of and participation in pancreas and islet cell transplantation. Methods: We analyzed Google Search trends for “Pancreas Transplantation” and “Islet Cell Transplantation” from 2004 to 14 November 2023, assessing variations in search interest over time and across geographical locations. The Augmented Dickey–Fuller (ADF) test was used to determine the stationarity of the trends (p < 0.05). Results: Search interest for “Pancreas Transplantation” varied from its 2004 baseline, with a general decline in peak interest over time. The lowest interest was in December 2010, with a slight increase by November 2023. Ecuador, Kuwait, and Saudi Arabia showed the highest search interest. “Islet Cell Transplantation” had its lowest interest in December 2016 and a more pronounced decline over time, with Poland, China, and South Korea having the highest search volumes. In the U.S., “Pancreas Transplantation” ranked 4th in interest, while “Islet Cell Transplantation” ranked 11th. The ADF test confirmed the stationarity of the search trends for both procedures. Conclusions: “Pancreas Transplantation” and “Islet Cell Transplantation” showed initial peaks in search interest followed by a general downtrend. The stationary search trends suggest a lack of significant fluctuations or cyclical variations. These findings highlight the need for enhanced educational initiatives to increase the understanding and awareness of these critical transplant procedures among the public and professionals.

Background. Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. Methods. We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms’ outputs based on a single preoperative ECG were correlated with patient mortality data. Results. Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00–9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). Conclusions. The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.