Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Black Kidney Transplant Recipients and Associated Outcomes

Importance: Among kidney transplant recipients, Black patients continue to have worse graft function and reduced patient and graft survival. Better understanding of different phenotypes and subgroups of Black kidney transplant recipients may help the transplant community to identify individualized strategies to improve outcomes among these vulnerable groups. Objective: To cluster Black kidney transplant recipients in the US using an unsupervised machine learning approach. Design, Setting, and Participants: This cohort study performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in Black kidney transplant recipients in the US from January 1, 2015, to December 31, 2019, in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database. Each cluster\u27s key characteristics were identified using the standardized mean difference, and subsequently the posttransplant outcomes were compared among the clusters. Data were analyzed from June 9 to July 17, 2021. Exposure: Machine learning consensus clustering approach. Main Outcomes and Measures: Death-censored graft failure, patient death within 3 years after kidney transplant, and allograft rejection within 1 year after kidney transplant. Results: Consensus cluster analysis was performed for 22 687 Black kidney transplant recipients (mean [SD] age, 51.4 [12.6] years; 13 635 men [60%]), and 4 distinct clusters that best represented their clinical characteristics were identified. Cluster 1 was characterized by highly sensitized recipients of deceased donor kidney retransplants; cluster 2, by recipients of living donor kidney transplants with no or short prior dialysis; cluster 3, by young recipients with hypertension and without diabetes who received young deceased donor transplants with low kidney donor profile index scores; and cluster 4, by older recipients with diabetes who received kidneys from older donors with high kidney donor profile index scores and extended criteria donors. Cluster 2 had the most favorable outcomes in terms of death-censored graft failure, patient death, and allograft rejection. Compared with cluster 2, all other clusters had a higher risk of death-censored graft failure and death. Higher risk for rejection was found in clusters 1 and 3, but not cluster 4. Conclusions and Relevance: In this cohort study using an unsupervised machine learning approach, the identification of clinically distinct clusters among Black kidney transplant recipients underscores the need for individualized care strategies to improve outcomes among vulnerable patient groups

Pericardial Patch Angioplasty Heals via an Ephrin-B2 and CD34 Positive Cell Mediated Mechanism

Pericardial patches are commonly used in vascular surgery to close arteriotomies. The mechanism of early healing after patch implantation is still not well defined. We used a rat aortic patch model to assess pericardial patch healing and examined Ephrin-B2, a marker of arterial identity, expression within the post-implantation patch. We also determined whether endothelial progenitor cells (EPC) are associated with early patch healing in the arterial environment.Wistar rats (200-250 grams) underwent infrarenal aortic arteriotomy and then closure via bovine or porcine pericardial patch angioplasty. Control groups included subcutaneously implanted patches. Patches were harvested at 0-30 days and analyzed by histology, immunohistochemistry, immunofluorescence and Western blot as well as quantitative PCR.Prior to implantation, pericardial patches are largely composed of collagen and are acellular. Following arterial implantation, increasing numbers of CD68-positive cells as well as Ephrin-B2 and CD34 dual-positive cells are found within both bovine and porcine pericardial patches, whereas the infiltrating cells are negative for vWF and α-actin. Porcine patches have a luminal monolayer of cells at day 7, compared to bovine patches that have fewer luminal cells. Subcutaneously implanted patches do not attract Ephrin-B2/CD34-positive cells. By day 30, both bovine and porcine pericardial patches develop a neointima that contains Ephrin-B2, CD34, and VEGFR2-positive cells.Both CD68-positive and Ephrin-B2 and CD34 dual-positive cells infiltrate the pericardial patch early after implantation. Arteriotomy closure via pericardial patch angioplasty shows patch adaptation to the arterial environment that may involve a foreign body response as well as localization of EPC. Arterial remodeling of pericardial patches support endothelialization and may represent a paradigm of healing of scaffolds used for tissue engineering

Early patient and liver allograft outcomes from donation after circulatory death donors using thoracoabdominal normothermic regional: a multi-center observational experience

BackgroundDonation after circulatory death (DCD) liver allografts are associated with higher rates of primary non-function (PNF) and ischemic cholangiopathy (IC). Advanced recovery techniques, including thoracoabdominal normothermic regional perfusion (TA-NRP), may improve organ utilization and patient and allograft outcomes. Given the increasing US experience with TA-NRP DCD recovery, we evaluated outcomes of DCD liver allografts transplanted after TA-NRP.MethodsLiver allografts transplanted from DCD donors after TA-NRP were identified from 5/1/2021 to 1/31/2022 across 8 centers. Donor data included demographics, functional warm ischemic time (fWIT), total warm ischemia time (tWIT) and total time on TA-NRP. Recipient data included demographics, model of end stage liver disease (MELD) score, etiology of liver disease, PNF, cold ischemic time (CIT), liver function tests, intensive care unit (ICU) and hospital length of stay (LOS), post-operative transplant related complications.ResultsThe donors' median age was 32 years old and median BMI was 27.4. Median fWIT was 20.5 min; fWIT exceeded 30 min in two donors. Median time to initiation of TA-NRP was 4 min and median time on bypass was 66 min. The median recipient listed MELD and MELD at transplant were 22 and 21, respectively. Median allograft CIT was 292 min. The median length of follow up was 257 days. Median ICU and hospital LOS were 2 and 7 days, respectively. Three recipients required management of anastomotic biliary strictures. No patients demonstrated IC, PNF or required re-transplantation.ConclusionLiver allografts from TA-NRP DCD donors demonstrated good early allograft and recipient outcomes

Delayed Presentation of Traumatic Right-Sided Diaphragmatic Hernia after Abdominoplasty

Traumatic diaphragmatic hernias are rare and challenging to diagnose. Following trauma, diagnosis may occur immediately or in a delayed fashion. It is believed that left traumatic diaphragmatic hernias are more common as a result of the protective right-sided anatomic lie of the liver. If unrecognized, traumatic diaphragmatic injuries are subject to enlarge over time as a result of the normal pressure changes observed between the thoracic and abdominal cavities. Additionally, abrupt changes to the pressure gradients, such as those which occur with positive pressure ventilation or surgical manipulation of the abdominal wall, can act as a nidus for making an asymptomatic hernia symptomatic. We report our experience with a delayed traumatic right-sided diaphragmatic hernia presenting with large bowel incarceration two months after abdominoplasty. In our review of the literature, we were unable to find any reports of delayed presentation of a traumatic right-sided diaphragmatic hernia occurring acutely following abdominoplasty

Enhancing Kidney Transplant Care through the Integration of Chatbot

Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. This article explores the potential integration of a sophisticated chatbot, an AI-powered conversational agent, to enhance kidney transplant practice and potentially improve patient outcomes. Chatbots and generative AI have shown promising applications in various domains, including healthcare, by simulating human-like interactions and generating contextually appropriate responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, and Bard AI by Google exhibit significant potential in supporting evidence-based research and healthcare decision making. The integration of chatbots in kidney transplant care may offer transformative possibilities. As a clinical decision support tool, it could provide healthcare professionals with real-time access to medical literature and guidelines, potentially enabling informed decision making and improved knowledge dissemination. Additionally, the chatbot has the potential to facilitate patient education by offering personalized and understandable information, addressing queries, and providing guidance on post-transplant care. Furthermore, under clinician or transplant pharmacist supervision, it has the potential to support post-transplant care and medication management by analyzing patient data, which may lead to tailored recommendations on dosages, monitoring schedules, and potential drug interactions. However, to fully ascertain its effectiveness and safety in these roles, further studies and validation are required. Its integration with existing clinical decision support systems may enhance risk stratification and treatment planning, contributing to more informed and efficient decision making in kidney transplant care. Given the importance of ethical considerations and bias mitigation in AI integration, future studies may evaluate long-term patient outcomes, cost-effectiveness, user experience, and the generalizability of chatbot recommendations. By addressing these factors and potentially leveraging AI capabilities, the integration of chatbots in kidney transplant care holds promise for potentially improving patient outcomes, enhancing decision making, and fostering the equitable and responsible use of AI in healthcare