Citations

Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients 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 clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 ± 13 years) who received dual kidney transplant from pediatric (mean donor age 3 ± 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 ± 9 years) who received dual kidney transplant from adult (mean donor age 59 ± 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI ≥ 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage.

Background: Our study aimed to characterize kidney transplant recipients who received high kidney donor profile index (KDPI) kidneys using unsupervised machine learning approach. Methods: We used the OPTN/UNOS database from 2010 to 2019 to perform consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 8935 kidney transplant recipients from deceased donors with KDPI ≥ 85%. We identified each cluster’s key characteristics using the standardized mean difference of >0.3. We compared the posttransplant outcomes among the assigned clusters. Results: Consensus cluster analysis identified 6 clinically distinct clusters of kidney transplant recipients from donors with high KDPI. Cluster 1 was characterized by young, black, hypertensive, non-diabetic patients who were on dialysis for more than 3 years before receiving kidney transplant from black donors; cluster 2 by elderly, white, non-diabetic patients who had preemptive kidney transplant or were on dialysis less than 3 years before receiving kidney transplant from older white donors; cluster 3 by young, non-diabetic, retransplant patients; cluster 4 by young, non-obese, non-diabetic patients who received dual kidney transplant from pediatric, black, non-hypertensive non-ECD deceased donors; cluster 5 by low number of HLA mismatch; cluster 6 by diabetes mellitus. Cluster 4 had the best patient survival, whereas cluster 3 had the worst patient survival. Cluster 2 had the best death-censored graft survival, whereas cluster 4 and cluster 3 had the worst death-censored graft survival at 1 and 5 years, respectively. Cluster 2 and cluster 4 had the best overall graft survival at 1 and 5 years, respectively, whereas cluster 3 had the worst overall graft survival. Conclusions: Unsupervised machine learning approach kidney transplant recipients from donors with high KDPI based on their pattern of clinical characteristics into 6 clinically distinct clusters.

Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.

Background: There have been concerns regarding increased perioperative mortality, length of hospital stay, and rates of graft loss in kidney transplant recipients with functional limitations. The application of machine learning consensus clustering approach may provide a novel understanding of unique phenotypes of functionally limited kidney transplant recipients with distinct outcomes in order to identify strategies to improve outcomes. Methods: Consensus cluster analysis was performed based on recipient-, donor-, and transplant-related characteristics in 3205 functionally limited kidney transplant recipients (Karnofsky Performance Scale (KPS) < 40% at transplant) in the OPTN/UNOS database from 2010 to 2019. Each cluster’s key characteristics were identified using the standardized mean difference. Posttransplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection were compared among the clusters Results: Consensus cluster analysis identified two distinct clusters that best represented the clinical characteristics of kidney transplant recipients with limited functional status prior to transplant. Cluster 1 patients were older in age and were more likely to receive deceased donor kidney transplant with a higher number of HLA mismatches. In contrast, cluster 2 patients were younger, had shorter dialysis duration, were more likely to be retransplants, and were more likely to receive living donor kidney transplants from HLA mismatched donors. As such, cluster 2 recipients had a higher PRA, less cold ischemia time, and lower proportion of machine-perfused kidneys. Despite having a low KPS, 5-year patient survival was 79.1 and 83.9% for clusters 1 and 2; 5-year death-censored graft survival was 86.9 and 91.9%. Cluster 1 had lower death-censored graft survival and patient survival but higher acute rejection, compared to cluster 2. Conclusion: Our study used an unsupervised machine learning approach to characterize kidney transplant recipients with limited functional status into two clinically distinct clusters with differing posttransplant outcomes.

The mammalian liver's regenerative ability has led researchers to engineer animals as incubators for expansion of human hepatocytes. The expansion properties of human hepatocytes in immunodeficient mice are well known. However, little has been reported about larger animals that are more scalable and practical for clinical purposes. Therefore, we engineered immunodeficient swine to support expansion of human hepatocytes and identify barriers to their clinical application. Immunodeficient swine were engineered by knockout of the recombinase-activating gene 2 ( RAG2 ) and fumarylacetoacetate hydrolase (FAH). Immature human hepatocytes (ihHCs) were injected into fetal swine by intrauterine cell transplantation (IUCT) at day 40 of gestation. Human albumin was measured as a marker of engraftment. Cytotoxicity against ihHCs was measured in transplanted piglets and control swine. We initially detected higher levels of human albumin in cord blood of newborn FAH/ RAG2 -deficient (FR) pigs compared with immunocompetent controls (196.26 ng/dL vs. 39.29 ng/dL, p = 0.008), indicating successful engraftment of ihHCs after IUCT and adaptive immunity in the fetus. Although rare hepatocytes staining positive for human albumin were observed, levels of human albumin did not rise after birth, but declined, suggesting rejection of xenografted ihHCs. Cytotoxicity against ihHCs increased after birth by 3.8% (95% CI: [2.1%–5.4%], p < 0.001) and inversely correlated with declining levels of human albumin ( p = 2.1 × 10 −5 , R 2 = 0.17). Circulating numbers of T cells and B cells were negligible in FR pigs. However, circulating natural killer (NK) cells exerted cytotoxicity against ihHCs. NK cell activity was lower in immunodeficient piglets after IUCT than in naive controls (30.4% vs. 40.1%, p = 0.011, 95% CI for difference [2.7%–16.7%]). In conclusion, ihHCs were successfully engrafted in FR swine after IUCT. NK cells were a significant barrier to expansion of hepatocytes. New approaches are needed to overcome this hurdle and allow large-scale expansion of human hepatocytes in immunodeficient swine. Impact statement There is currently a need for robust expansion of human hepatocytes. We describe an immunodeficient swine model into which we engrafted immature human hepatocytes (ihHCs). We identified the mechanism of the eventual graft rejection by the intact NK cell population, which has not been previously shown to have a significant role in xenograft rejection. By both improving engraftment and reducing NK cell-mediated cytotoxicity toward the graft through intrauterine cell transfer, we confirmed the presence of residual adaptive immunity in this model of immunodeficiency and the ability to induce hyposensitization in the NK cell population by taking advantage of the fetal microenvironment.

Background: Lower patient survival has been observed in sickle cell disease (SCD) patients who go on to receive a kidney transplant. This study aimed to assess the post-transplant outcomes of SCD kidney transplant recipients in the contemporary era. Methods: We used the OPTN/UNOS database to identify first-time kidney transplant recipients from 2010 through 2019. We compared patient and allograft survival between recipients with SCD (n = 105) vs. all other diagnoses (non-SCD, n = 146,325) as the reported cause of end-stage kidney disease. We examined whether post-transplant outcomes improved among SCD in the recent era (2010–2019), compared to the early era (2000–2009). Results: After adjusting for differences in baseline characteristics, SCD was significantly associated with lower patient survival (HR 2.87; 95% CI 1.75–4.68) and death-censored graft survival (HR 1.98; 95% CI 1.30–3.01), compared to non-SCD recipients. The lower patient survival and death-censored graft survival in SCD recipients were consistently observed in comparison to outcomes of recipients with diabetes, glomerular disease, and hypertension as the cause of end-stage kidney disease. There was no significant difference in death censored graft survival (HR 0.99; 95% CI 0.51–1.73, p = 0.98) and patient survival (HR 0.93; 95% CI 0.50–1.74, p = 0.82) of SCD recipients in the recent versus early era. Conclusions: Patient and allograft survival in SCD kidney recipients were worse than recipients with other diagnoses. Overall SCD patient and allograft outcomes in the recent era did not improve from the early era. The findings of our study should not discourage kidney transplantation for ESKD patients with SCD due to a known survival benefit of transplantation compared with remaining on dialysis. Urgent future studies are needed to identify strategies to improve patient and allograft survival in SCD kidney recipients. In addition, it may be reasonable to assign risk adjustment for SCD patients.

Pre–liver transplantation (LT) renal dysfunction is associated with poor post‐LT survival. We studied whether early allograft dysfunction (EAD) modifies this association. Data on 2,856 primary LT recipients who received a transplant between 1998 and 2018 were retrospectively reviewed. Patients who died within the first post‐LT week or received multiorgan transplants and previous LT recipients were excluded. EAD was defined as (1) total bilirubin ≥ 10 mg/dL on postoperative day (POD) 7, (2) international normalized ratio ≥1.6 on POD 7, and/or (3) alanine aminotransferase or aspartate aminotransferase ≥2000 IU/mL in the first postoperative week. Pre‐LT renal dysfunction was defined as serum creatinine >1.5 mg/dL or on renal replacement therapy at LT. Patients were divided into 4 groups according to pre‐LT renal dysfunction and post‐LT EAD development. Recipients who had both pre‐LT renal dysfunction and post‐LT EAD had the worst unadjusted 1‐year, 3‐year, and 5‐year post‐LT patient and graft survival, whereas patients who had neither renal dysfunction nor EAD had the best survival (P < 0.001). After adjusting for multiple factors, the risk of death was significantly higher only in those with both pre‐LT renal dysfunction and post‐LT EAD (adjusted hazard ratio [aHR], 2.19; 95% confidence interval [CI], 1.58‐3.03; P < 0.001), whereas those with renal dysfunction and no EAD had a comparable risk of death to those with normal kidney function at LT (aHR, 1.12; 95% CI, 0.86‐1.45; P = 0.41). Results remained unchanged when pre‐LT renal dysfunction was redefined using different glomerular filtration rate cutoffs. Pre‐LT renal dysfunction negatively impacts post‐LT survival only in patients who develop EAD. Livers at higher risk of post‐LT EAD should be used with caution in recipients with pre‐LT renal dysfunction.

AbstractLittle is known about the surgical challenges and outcomes of kidney transplantation (KT) in the face of severe iliac occlusive disease (IOD). We aim to examine our institution's experience and outcomes compared with all KT patients. Retrospective review of our multi-institutional transplant database identified patients with IOD requiring vascular surgery involvement for iliac artery endarterectomy at time of KT from 2000 to 2018. Clinical data, imaging studies, and surgical outcomes of 22 consecutive patients were reviewed. Our primary end-point was allograft survival. Secondary end-points included mortality and perioperative complications. A total of 6,757 KT were performed at our three sites (Florida, Arizona, and Minnesota); there were 22 (0.32%) patients receiving a KT with concomitant IOD requiring iliac artery endarterectomy. Mean patient age was 61.45 ± 7 years. There were 13 (59.1%) male patients. The most common etiology of renal failure was diabetic nephropathy in 10 patients (45.5%) followed by a combination of hypertensive/diabetic nephropathy in five patients (22.7%), and hypertensive nephrosclerosis in three patients (13.6%). The majority (n = 16, 72.7%) of patients received renal allografts from deceased donors and six (27.3%) were recipients from living donors. Mean time from dialysis to transplantation was 2.9 ± 2.9 years. Mean follow-up was 3.5 ± 2.5 years. Mean length of hospital stay was 6.3 ± 4.3 days (range: 3–18 days). Graft loss within 90 days occurred in two (9.1%) patients, one due to renal vein thrombosis and another due to acute tubular necrosis. Overall allograft survival was 90.1% at 1-year and 86.4% at 3-year follow-up. Overall mortality occurred in 6 (27.3%) patients. Perioperative complications (Clavien-Dindo Grade 2–4) occurred in 13 (59.1%) patients, including 10 (45.5%) with acute blood loss anemia requiring transfusion, 2 (9.1%) reoperations for hematoma evacuation, 1 (4.5%) ischemic colitis requiring total abdominal colectomy, and 1 (4.5%) renal vein thrombosis requiring nephrectomy. IOD patients selected for KT are not common and although challenging, they have similar outcomes to our standard KT patients. The 1- and 3-year allograft survivals were 90.1 and 86.4% versus 96.0 and 90.3% in the general KT patient population. With these excellent outcomes, we recommend expanding the criteria for KT to include patients with IOD with prior vascular surgery consultation to prevent progression of IOD or prevention of wait list removal in select patients who are otherwise good candidates for KT.