Ex-situ machine perfusion in clinical liver transplantation: Current practices and future directions.

Ex-situ machine perfusion of the liver has surmounted traditional limitations associated with static cold storage in the context of organ preservation. This innovative technology has changed the landscape of liver transplantation by mitigating ischemia perfusion injury, offering a platform for continuous assessment of organ quality, and providing an avenue for optimizing use of traditionally marginal allografts. This review summarizes the contemporary clinical applications of machine perfusion devices, and discusses potential future strategies for real-time viability assessment, therapeutic interventions, and modulation of organ function after recovery.

AI-driven translations for kidney transplant equity in Hispanic populations.

Health equity and accessing Spanish kidney transplant information continues being a substantial challenge facing the Hispanic community. This study evaluated ChatGPT's capabilities in translating 54 English kidney transplant frequently asked questions (FAQs) into Spanish using two versions of the AI model, GPT-3.5 and GPT-4.0. The FAQs included 19 from Organ Procurement and Transplantation Network (OPTN), 15 from National Health Service (NHS), and 20 from National Kidney Foundation (NKF). Two native Spanish-speaking nephrologists, both of whom are of Mexican heritage, scored the translations for linguistic accuracy and cultural sensitivity tailored to Hispanics using a 1-5 rubric. The inter-rater reliability of the evaluators, measured by Cohen's Kappa, was 0.85. Overall linguistic accuracy was 4.89 ± 0.31 for GPT-3.5 versus 4.94 ± 0.23 for GPT-4.0 (non-significant p = 0.23). Both versions scored 4.96 ± 0.19 in cultural sensitivity (p = 1.00). By source, GPT-3.5 linguistic accuracy was 4.84 ± 0.37 (OPTN), 4.93 ± 0.26 (NHS), 4.90 ± 0.31 (NKF). GPT-4.0 scored 4.95 ± 0.23 (OPTN), 4.93 ± 0.26 (NHS), 4.95 ± 0.22 (NKF). For cultural sensitivity, GPT-3.5 scored 4.95 ± 0.23 (OPTN), 4.93 ± 0.26 (NHS), 5.00 ± 0.00 (NKF), while GPT-4.0 scored 5.00 ± 0.00 (OPTN), 5.00 ± 0.00 (NHS), 4.90 ± 0.31 (NKF). These high linguistic and cultural sensitivity scores demonstrate Chat GPT effectively translated the English FAQs into Spanish across systems. The findings suggest Chat GPT's potential to promote health equity by improving Spanish access to essential kidney transplant information. Additional research should evaluate its medical translation capabilities across diverse contexts/languages. These English-to-Spanish translations may increase access to vital transplant information for underserved Spanish-speaking Hispanic patients.

Empowering inclusivity: improving readability of living kidney donation information with ChatGPT.

Background: Addressing disparities in living kidney donation requires making information accessible across literacy levels, especially important given that the average American adult reads at an 8th-grade level. This study evaluated the effectiveness of ChatGPT, an advanced AI language model, in simplifying living kidney donation information to an 8th-grade reading level or below. Methods: We used ChatGPT versions 3.5 and 4.0 to modify 27 questions and answers from Donate Life America, a key resource on living kidney donation. We measured the readability of both original and modified texts using the Flesch-Kincaid formula. A paired t-test was conducted to assess changes in readability levels, and a statistical comparison between the two ChatGPT versions was performed. Results: Originally, the FAQs had an average reading level of 9.6 ± 1.9. Post-modification, ChatGPT 3.5 achieved an average readability level of 7.72 ± 1.85, while ChatGPT 4.0 reached 4.30 ± 1.71, both with a p-value <0.001 indicating significant reduction. ChatGPT 3.5 made 59.26% of answers readable below 8th-grade level, whereas ChatGPT 4.0 did so for 96.30% of the texts. The grade level range for modified answers was 3.4-11.3 for ChatGPT 3.5 and 1-8.1 for ChatGPT 4.0. Conclusion: Both ChatGPT 3.5 and 4.0 effectively lowered the readability grade levels of complex medical information, with ChatGPT 4.0 being more effective. This suggests ChatGPT's potential role in promoting diversity and equity in living kidney donation, indicating scope for further refinement in making medical information more accessible.

Evaluating Global and Temporal Trends in Pancreas and Islet Cell Transplantation: Public Awareness and Engagement.

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.

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation.

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.

Utilizing kidneys from a donor with bile-cast nephropathy.

Here we discuss the successful utilization of a pair of deceased donor kidneys with bile-cast nephropathy. The donor had a kidney donor profile index of 48% and an acute kidney injury requiring continuous renal replacement therapy. Peak donor bilirubin was 40.5 mg/dL, and renal wedge biopsies showed bile-cast nephropathy. Both recipients had delayed graft function lasting up to 4 weeks. The 4-month biopsies showed mild interstitial fibrosis, tubular atrophy, and a resolution of bile casts. These kidney allografts showed the reversible course of cholemic nephropathy and the potential for increasing the utilization of previously discarded kidneys.

Duration of Dialysis in Acute Kidney Injury Donors and Transplant Outcomes.

BACKGROUND: Acute kidney injury (AKI) kidneys, including those from donors on dialysis, are often underutilized, although there is increasing data available demonstrating good transplant outcomes. To date, data on the duration of donor dialysis and transplant outcomes are limited. STUDY DESIGN: This was a single-center study of deceased donor kidney transplants from 2010 to 2022. The study cohort consisted of recipients of deceased donor kidney transplants from donors with AKI and on dialysis. Three groups were identified based on the predetermined interquartile range of donor dialysis duration: 1 to 2 dialysis days, 3 to 4 dialysis days, and 5 or more dialysis days. RESULTS: During this period, 765 AKI deceased donor transplants were performed, of which 230 were from donors on dialysis. The median dialysis duration was 2 days with a maximum of 13 days. Across the 3 groups, there were no differences in recipient age (p = 0.23) or dialysis vintage (p = 0.70). Donor age (p = 0.86) and kidney donor profile index (p = 0.57) were comparable between the groups. Recipients of deceased donor kidney transplants from donors on dialysis 5 or more days had lower terminal creatinine levels (p = 0.003) and longer cold ischemia times (p = 0.04). Posttransplant, the median length of hospital stay was 3 days for all groups (p = 0.75). There were no differences in delayed graft function occurrence (94.4% vs 86.8% vs 92.1%, p = 0.19), duration of delayed graft function (p = 0.56), or readmissions (p = 0.99). At 1 year posttransplant, the estimated glomerular filtration rate (p = 0.76), patient survival (p = 0.82), or death-censored graft survival (p = 0.28) were comparable. CONCLUSIONS: Excellent outcomes have been observed in AKI deceased donor kidney transplants, including those coming from donors on dialysis. In this small cohort, the duration of donor dialysis did not adversely affect outcomes. Cautious expansion of the donor pool, including donors on dialysis, should be considered given the ongoing organ shortage.

Re-assessing prolonged cold ischemia time in kidney transplantation through machine learning consensus clustering.

BACKGROUND: We aimed to cluster deceased donor kidney transplant recipients with prolonged cold ischemia time (CIT) using an unsupervised machine learning approach. METHODS: We performed consensus cluster analysis on 11 615 deceased donor kidney transplant patients with CIT exceeding 24 h using OPTN/UNOS data from 2015 to 2019. Cluster characteristics of clinical significance were identified, and post-transplant outcomes were compared. RESULTS: Consensus cluster analysis identified two clinically distinct clusters. Cluster 1 was characterized by young, non-diabetic patients who received kidney transplants from young, non-hypertensive, non-ECD deceased donors with lower KDPI scores. In contrast, the patients in cluster 2 were older and more likely to have diabetes. Cluster 2 recipients were more likely to receive transplants from older donors with a higher KDPI. There was lower use of machine perfusion in Cluster 1 and incrementally longer CIT in Cluster 2. Cluster 2 had a higher incidence of delayed graft function (42% vs. 29%), and lower 1-year patient (95% vs. 98%) and death-censored (95% vs. 97%) graft survival compared to Cluster 1. CONCLUSIONS: Unsupervised machine learning characterized deceased donor kidney transplant recipients with prolonged CIT into two clusters with differing outcomes. Although Cluster 1 had more favorable recipient and donor characteristics and better survival, the outcomes observed in Cluster 2 were also satisfactory. Overall, both clusters demonstrated good survival suggesting opportunities for transplant centers to incrementally increase CIT.

Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering.

BACKGROUND: Educational attainment significantly influences post-transplant outcomes in kidney transplant patients. However, research on specific attributes of lower-educated subgroups remains underexplored. This study utilized unsupervised machine learning to segment kidney transplant recipients based on education, further analyzing the relationship between these segments and post-transplant results. METHODS: Using the OPTN/UNOS 2017-2019 data, consensus clustering was applied to 20,474 kidney transplant recipients, all below a college/university educational threshold. The analysis concentrated on recipient, donor, and transplant features, aiming to discern pivotal attributes for each cluster and compare post-transplant results. RESULTS: Four distinct clusters emerged. Cluster 1 comprised younger, non-diabetic, first-time recipients from non-hypertensive younger donors. Cluster 2 predominantly included white patients receiving their first-time kidney transplant either preemptively or within three years, mainly from living donors. Cluster 3 included younger re-transplant recipients, marked by elevated PRA, fewer HLA mismatches. In contrast, Cluster 4 captured older, diabetic patients transplanted after prolonged dialysis duration, primarily from lower-grade donors. Interestingly, Cluster 2 showcased the most favorable post-transplant outcomes. Conversely, Clusters 1, 3, and 4 revealed heightened risks for graft failure and mortality in comparison. CONCLUSIONS: Through unsupervised machine learning, this study proficiently categorized kidney recipients with lesser education into four distinct clusters. Notably, the standout performance of Cluster 2 provides invaluable insights, underscoring the necessity for adept risk assessment and tailored transplant strategies, potentially elevating care standards for this patient cohort.

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.

Past, Current, and Future Perspectives on Transplanting Acute Kidney Injury Kidneys.

(1) Background: Acute kidney injury (AKI) kidneys have high non-utilization rates due to concerns regarding unfavorable outcomes. In this paper, we aimed to review the past, present, and future opinions on AKI kidneys. (2) Methods: A PubMed search was conducted for topics relevant to AKI kidney transplantation. (3) Results: Current short- and long-term data on AKI kidneys have demonstrated good outcomes including favorable graft function and survival. The role of procurement biopsies is controversial, but they have been shown to be beneficial in AKI kidneys by allowing clinicians to differentiate between reversible tubular injury and irreversible cortical necrosis. Machine perfusion has also been applied to AKI kidneys and has been shown to reduce delayed graft function (DGF). The incidence of DGF increases with AKI severity and its management can be challenging. Strategies employed to counteract this have included early initiation of dialysis after kidney transplantation, early targeting of adequate immunosuppression levels to minimize rejection risk, and establishment of outpatient dialysis. (4) Conclusions: Despite good outcomes, there continue to be barriers that impact AKI kidney utilization. Successful strategies have included use of procurement biopsies or machine perfusion and expectant management of DGF. With increasing experience, better use of AKI kidneys can result in additional opportunities to expand the donor pool.

Characteristics of Kidney Transplant Recipients with Prolonged Pre-Transplant Dialysis Duration as Identified by Machine Learning Consensus Clustering: Pathway to Personalized Care.

Longer pre-transplant dialysis duration is known to be associated with worse post-transplant outcomes. Our study aimed to cluster kidney transplant recipients with prolonged dialysis duration before transplant using an unsupervised machine learning approach to better assess heterogeneity within this cohort. We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 5092 kidney transplant recipients who had been on dialysis ≥ 10 years prior to transplant in the OPTN/UNOS database from 2010 to 2019. We characterized each assigned cluster and compared the posttransplant outcomes. Overall, the majority of patients with ≥10 years of dialysis duration were black (52%) or Hispanic (25%), with only a small number (17.6%) being moderately sensitized. Within this cohort, three clinically distinct clusters were identified. Cluster 1 patients were younger, non-diabetic and non-sensitized, had a lower body mass index (BMI) and received a kidney transplant from younger donors. Cluster 2 recipients were older, unsensitized and had a higher BMI; they received kidney transplant from older donors. Cluster 3 recipients were more likely to be female with a higher PRA. Compared to cluster 1, cluster 2 had lower 5-year death-censored graft (HR 1.40; 95% CI 1.16-1.71) and patient survival (HR 2.98; 95% CI 2.43-3.68). Clusters 1 and 3 had comparable death-censored graft and patient survival. Unsupervised machine learning was used to characterize kidney transplant recipients with prolonged pre-transplant dialysis into three clinically distinct clusters with variable but good post-transplant outcomes. Despite a dialysis duration ≥ 10 years, excellent outcomes were observed in most recipients, including those with moderate sensitization. A disproportionate number of minority recipients were observed within this cohort, suggesting multifactorial delays in accessing kidney transplantation.

Outcomes with transplanting kidneys offered through expedited allocation.

INTRODUCTION: Expedited out-of-sequence deceased donor kidney allocation is a strategy to avoid discards after early placement attempts have been unsuccessful. Our study aimed to assess outcomes and characteristics of these transplanted kidneys. METHODS: KDPI matching was performed between expedited allocation (EA) and standard allocation (SA) deceased donor kidney transplants performed at our center. RESULTS: Between 2018 and 2021, there were 225 EA offers, and 189 (84%) were transplanted. EA recipients were older (p = .007) and had shorter dialysis vintage (p < .0001). EA kidneys were likely to be nationally allocated (p < .001), have AKI (p < .0001) and longer CIT (p < .0001). There were no differences in EA and SA time-zero kidney biopsies (ci, p = .07; ct, p = .89; cv, p = .95; ah, p = .79). EA kidneys had more DGF (p = .0006), but there were no differences in DGF duration (p = .83), hospital length of stay (p = .43), 1- and 2-year eGFR (p = .16, p = .99), patient (p = .34), or death-censored graft (p = .66) survival. CONCLUSION: During this study period, our center transplanted 189 kidneys through EA following local-regional declines. These kidneys often came from AKI donors and had more DGF but had similar outcomes to KDPI-matched SA kidneys. Although it has been suggested that EA has the potential to worsen transplant disparities, transplant center level decisions on organ acceptance contribute to these variations.

Differences between Kidney Transplant Recipients from Deceased Donors with Diabetes Mellitus as Identified by Machine Learning Consensus Clustering.

Clinical outcomes of deceased donor kidney transplants coming from diabetic donors currently remain inconsistent, possibly due to high heterogeneities in this population. Our study aimed to cluster recipients of diabetic deceased donor kidney transplants using an unsupervised machine learning approach in order to identify subgroups with high risk of inferior outcomes and potential variables associated with these outcomes. Consensus cluster analysis was performed based on recipient-, donor-, and transplant-related characteristics in 7876 recipients of diabetic deceased donor kidney transplants from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between the clusters. Consensus cluster analysis identified three clinically distinct clusters. Recipients in cluster 1 (n = 2903) were characterized by oldest age (64 ± 8 years), highest rate of comorbid diabetes mellitus (55%). They were more likely to receive kidney allografts from donors that were older (58 ± 6.3 years), had hypertension (89%), met expanded criteria donor (ECD) status (78%), had a high rate of cerebrovascular death (63%), and carried a high kidney donor profile index (KDPI). Recipients in cluster 2 (n = 687) were younger (49 ± 13 years) and all were re-transplant patients with higher panel reactive antibodies (PRA) (88 [IQR 46, 98]) who received kidneys from younger (44 ± 11 years), non-ECD deceased donors (88%) with low numbers of HLA mismatch (4 [IQR 2, 5]). The cluster 3 cohort was characterized by first-time kidney transplant recipients (100%) who received kidney allografts from younger (42 ± 11 years), non-ECD deceased donors (98%). Compared to cluster 3, cluster 1 had higher incidence of primary non-function, delayed graft function, patient death and death-censored graft failure, whereas cluster 2 had higher incidence of delayed graft function and death-censored graft failure but comparable primary non-function and patient death. An unsupervised machine learning approach characterized diabetic donor kidney transplant patients into three clinically distinct clusters with differing outcomes. Our data highlight opportunities to improve utilization of high KDPI kidneys coming from diabetic donors in recipients with survival-limiting comorbidities such as those observed in cluster 1.

Liver transplant outcomes using late allocation grafts.

Post-cross clamp late allocation (LA) liver allografts are at increased risk for discard for many reasons including logistical complexity. Nearest neighbor propensity score matching was used to match 2 standard allocation (SA) offers to every 1 LA liver offer performed at our center between 2015 and 2021. Propensity scores were based on a logistic regression model including recipient age, recipient sex, graft type (donation after circulatory death vs. donation after brain death), Model for End-stage Liver Disease (MELD), and DRI score. During this time, 101 liver transplants (LT) were performed at our center using LA offers. In comparing LA and SA offers, there were no differences in recipient characteristics including indication for transplant ( p = 0.29), presence of PVT ( p = 0.19), TIPS ( p = 0.83), and HCC status ( p = 0.24). LA grafts came from younger donors (mean age 43.6 vs. 48.9 y, p = 0.009) and were more likely to come from regional or national Organ Procurement Organizations (OPOs) ( p < 0.001). Cold ischemia time was longer for LA grafts (median 8.5 vs 6.3 h, p < 0.001). Following LT, there were no differences between the 2 groups in intensive care unit ( p = 0.22) and hospital ( p = 0.49) lengths of stay, need for endoscopic interventions ( p = 0.55), or biliary strictures ( p = 0.21). Patient (HR 1.0, 95% CI, 0.47-2.15, p = 0.99) and graft (HR 1.23, 95% CI, 0.43-3.50, p = 0.70) survival did not vary between the LA and SA cohorts. One-year LA and SA patient survival was 95.1% and 95.0%; 1-year graft survival was 93.1% and 92.1%, respectively. Despite the additional logistical complexity and longer cold ischemia time, LT outcomes utilizing LA grafts are similar to those allocated by means of SA. Improving allocation policies specific to LA offers, as well as the sharing of best practices between transplant centers and OPOs, are opportunities to further help minimize unnecessary discards.

Favorable outcomes in Hispanic recipients receiving simultaneous pancreas kidney transplantation.

The objective of this study was to compare the long-term outcomes of Hispanic versus white recipients who underwent simultaneous pancreas kidney transplantation (SPKT). This single-center study, conducted from 2003 to 2022, had a median follow-up of 7.5 years. The study included 91 Hispanic and 202 white SPKT recipients. The mean age (44 vs. 46 years), percentage of males (67% vs. 58%), and body mass index (BMI) (25.6 vs. 25.3 kg/m2 ) were similar between the Hispanic and white groups. The Hispanic group had more recipients with type 2 diabetes (38%) compared to the white group (5%, p < .001). The duration of dialysis was longer in Hispanics (640 vs. 473 days, p = .02), and fewer patients received preemptive transplants (10% vs. 29%, p < .01) compared to whites. Hospital length of stay, rates of BK Viremia, and acute rejection episodes within 1 year were similar between the groups. The estimated 5-year kidney, pancreas, and patient survival rates were also similar between the groups, 94%, 81%, and 95% in Hispanics, compared to 90%, 79%, and 90% in whites. Increasing age and longer duration of dialysis were risk factors for death. Although Hispanic recipients had a longer duration on dialysis and fewer preemptive transplants, the survival rates were similar to those of white recipients. However, referring providers and many transplant centers continue to overlook pancreas transplants for appropriately selected patients with type 2 diabetes, particularly among minority populations. As a transplant community, it is crucial that we make efforts to comprehend and tackle these obstacles to transplantation.

Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering.

Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients.

Reassessing Geographic, Logistical, and Cold Ischemia Cutoffs in Liver Transplantation.

INTRODUCTION: Liver acceptance patterns vary significantly between transplant centers. Data pertaining to outcomes of livers declined by local and regional centers and allocated nationally remains limited. PROJECT AIM: The objective was to compare post-liver transplant outcomes between liver allografts transplanted as a result of national and local-regional allocation. DESIGN: This was a retrospective evaluation of 109 nationally allocated liver allografts used for transplant by a single center. Outcomes of nationally allocated grafts were compared to standard allocation grafts (N = 505) during the same period. RESULTS: Recipients of nationally allocated grafts had lower model for end stage liver disease scores (17 vs 22, P = .001). Nationally allocated grafts were more likely to be post-cross clamp offers (29.4% vs 13.4%, P = .001) and have longer cold ischemia times (median hours 7.8 vs 5.5, P = .001). Early allograft dysfunction was common (54.1% vs 52.5%, P = .75) and did not impact hospital length of stay (median 5 vs 6 days, P = .89). There were no differences in biliary complications (P = .11). There were no differences in patient (P = .88) or graft survival (P = .35). In a multivariate model, after accounting for differences in cold ischemia time and posttransplant biliary complications, nationally allocated grafts were not associated with increased risk for graft loss (HR 0.9, 95% CI 0.4-1.8). Abnormal liver biopsy findings (33.0%) followed by donor donation after circulatory death status (22.9%) were the most common reasons for decline by local-regional centers. CONCLUSION: Despite longer cold ischemia times, patient and graft survival outcomes remain excellent and comparable to those seen from standard allocation grafts.