Recurrence of membranous nephropathy after kidney transplantation: A multicenter retrospective cohort study.

Membranous nephropathy (MN) is a leading cause of kidney failure worldwide and frequently recurs after transplant. Available data originated from small retrospective cohort studies or registry analyses; therefore, uncertainties remain on risk factors for MN recurrence and response to therapy. Within the Post-Transplant Glomerular Disease Consortium, we conducted a retrospective multicenter cohort study examining the MN recurrence rate, risk factors, and response to treatment. This study screened 22,921 patients across 3 continents and included 194 patients who underwent a kidney transplant due to biopsy-proven MN. The cumulative incidence of MN recurrence was 31% at 10 years posttransplant. Patients with a faster progression toward end-stage kidney disease were at higher risk of developing recurrent MN (hazard ratio [HR], 0.55 per decade; 95% confidence interval [CI], 0.35-0.88). Moreover, elevated pretransplant levels of anti-phospholipase A2 receptor (PLA2R) antibodies were strongly associated with recurrence (HR, 18.58; 95% CI, 5.37-64.27). Patients receiving rituximab for MN recurrence had a higher likelihood of achieving remission than patients receiving renin-angiotensin-aldosterone system inhibition alone. In sum, MN recurs in one-third of patients posttransplant, and measurement of serum anti-PLA2R antibody levels shortly before transplant could aid in risk-stratifying patients for MN recurrence. Moreover, patients receiving rituximab had a higher rate of treatment response.

A comparative analysis of kidney allograft outcomes in steroid use versus steroid discontinuation after basiliximab and ATG induction: A UNOS database study.

BACKGROUND AND OBJECTIVES: The relative safety and efficacy of early steroid withdrawal in kidney transplant patients after basiliximab compared to anti-thymocyte globulin (ATG) induction therapy is unknown. We aimed to compare kidney allograft outcomes in steroid use versus steroid discontinuation after basiliximab and ATG induction from the United Network for Organ Sharing (UNOS) database. MATERIALS AND METHODS: We conducted a retrospective cohort analysis of the UNOS database and included first kidney transplant recipients who received ATG or basiliximab induction therapy. We compared graft and patient outcomes in those who received steroid maintenance and those who were discharged off steroids. RESULTS: Of 106,061 patients, 25,344 (86.7%) received basiliximab induction and were maintained on steroids (B-Sm), and 3,880 (13.3%) were on a steroid-free regimen (B-Sf). Graft failure rate was significantly higher in the B-Sf compared to B-Sm at 1-year (4.1 vs. 1.8%, p < 0.001), 3-year (6.0 vs. 4.3%, p < 0.001) and 5-year follow-up (7.7 vs. 6.4%, p = 0.0004). The mortality rate was significantly higher in B-Sf at 1-year (3.3 vs. 2.4%, p = 0.0005), 3-year (7.6 vs. 5.5%, p < 0.001) and 5-year follow-up (11.5 vs. 8.8%, p < 0.001) when compared to the B-Sm. 76,837 recipients received ATG induction therapy, 51,745 (72.4%) were on steroid maintenance therapy (A-Sm) and 25,092 (32.6%) were on a steroid-free regimen (A-Sf). The graft failure rate was significantly higher in A-Sf compared to A-Sm at 1-year follow-up (2.6 vs. 2.3%, p = 0.0006), however, there was no difference at 3-year (5.0 vs. 5.0%, p = 0.53) or 5-year follow-up (7.2 vs. 8.1%, p = 0.17). There was no difference in mortality rates between A-Sf vs. A-Sm at 1 year (2.5 vs. 2.4%, p = 0.98) and at 3 years (5.5 vs. 5.4%, p = 0.45), respectively. CONCLUSION: Patients who were maintained on steroids after basiliximab induction had better 5-year allograft survival and patient survival compared to those who were not maintained on steroids. However, steroid maintenance conferred no additional benefit after ATG induction and was associated with higher mortality.

The current therapeutic approach for anaemia after kidney transplant.

PURPOSE OF REVIEW: Anaemia after kidney transplantation is a common finding with no uniform management guideline. Most approaches are derived from the chronic kidney disease (CKD) population. Recent advances for the treatment of anaemia in patients with CKD/End stage renal disease include hypoxia-inducible factor-prolyl hydroxylase inhibitor (HIF-PHi), a novel class of oral erythropoietin-stimulating agents (ESAs). We present relevant studies of HIF-PHi in the transplant population and its implications on the management of posttransplant anaemia. RECENT FINDINGS: Data on HIF-PHi use in the kidney transplant population are promising. Limited data demonstrate a significant increase in haemoglobin, with a comparable safety profile to epoetin. Reported adverse effects include overcorrection and low iron stores. SUMMARY: Current therapeutic approaches to anaemia in the kidney transplant population is mostly derived from the CKD population. More studies are needed on HIF-Phi, a novel class of ESAs that has thus far demonstrated promise in the kidney transplant population.

Differences between kidney retransplant recipients as identified by machine learning consensus clustering.

BACKGROUND: Our study aimed to characterize kidney retransplant recipients using an unsupervised machine-learning approach. METHODS: We performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 17 443 kidney retransplant recipients in the OPTN/UNOS database from 2010 to 2019. We identified each cluster's key characteristics using the standardized mean difference of >.3. We compared the posttransplant outcomes, including death-censored graft failure and patient death among the assigned clusters RESULTS: Consensus cluster analysis identified three distinct clusters of kidney retransplant recipients. Cluster 1 recipients were predominantly white and were less sensitized. They were most likely to receive a living donor kidney transplant and more likely to be preemptive (30%) or need ≤1 year of dialysis (32%). In contrast, cluster 2 recipients were the most sensitized (median PRA 95%). They were more likely to have been on dialysis >1 year, and receive a nationally allocated, low HLA mismatch, standard KDPI deceased donor kidney. Recipients in cluster 3 were more likely to be minorities (37% Black; 15% Hispanic). They were moderately sensitized with a median PRA of 87% and were also most likely to have been on dialysis >1 year. They received locally allocated high HLA mismatch kidneys from standard KDPI deceased donors. Thymoglobulin was the most commonly used induction agent for all three clusters. Cluster 1 had the most favorable patient and graft survival, while cluster 3 had the worst patient and graft survival. CONCLUSION: The use of an unsupervised machine learning approach characterized kidney retransplant recipients into three clinically distinct clusters with differing posttransplant outcomes. Recipients with moderate allosensitization, such as those represented in cluster 3, are perhaps more disadvantaged in the kidney retransplantation process. Potential opportunities for improvement specific to these re-transplant recipients include working to improve opportunities to improve access to living donor kidney transplantation, living donor paired exchange and identifying strategies for better HLA matching.

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.

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.

Protocol-based donor-derived cell-free DNA surveillance in kidney transplant recipients: A single-center experience.

AIMS: Donor-derived cell-free DNA (dd-cfDNA) surveillance testing has never been studied in comparison with other surveillance tests. In this study we aim to describe our center's clinical experience with routine dd-cfDNA monitoring and to assess whether monitoring dd-cfDNA by protocol provides additional information that aids in detection of acute rejection. MATERIALS AND METHODS: We implemented the dd-cfDNA (Allosure) surveillance protocol in addition to measurements of serum creatinine, proteinuria, and donor-Specific antibody. We retrospectively reviewed all kidney recipients transplanted from July 2018 to April 2020. 366 dd-cfDNA test results were reviewed from 82 patients. RESULTS: There were 13/366 positive dd-cfDNA tests in 8/82 patients. Five of the 8 patients had kidney biopsy which showed: 3 rejections (1 antibody-mediated rejection, 1 T-cell-mediated rejection, and 1 mixed), 1 acute tubular necrosis, and 1 transplant glomerulopathy. The remaining 3 patients did not undergo a biopsy and repeat dd-cfDNA testing improved without intervention. In the 353/366 negative dd-cfDNA tests in 74 patients: 7 patients underwent a biopsy: 1 patient with increased creatinine showed borderline cellular rejection, 3 had recurrent disease (membranoproliferative glomerulonephritis, diabetes mellitus, immunoglobulin A nephropathy), and 3 showed interstitial fibrosis and tubular atrophy. dd-cfDNA levels were not elevated in recipients with infection (BK viruria/viremia, CMV viremia, or urinary tract infection (UTI). CONCLUSION: The addition of surveillance dd-cfDNA testing resulted in marginal added benefit. Whether this offsets the cost of testing needs to be further explored. In our cohort of low-risk patients, the cost of protocol dd-cfDNA testing may not be justified by its limited benefits.

Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering.

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.

Association of Helicobacter pylori with Chronic Kidney Diseases: A Meta-Analysis.

BACKGROUND/OBJECTIVES: The reported risk of chronic kidney disease (CKD) in patients with Helicobacter pylori infection is conflicting. This meta-analysis was conducted to summarize all available data and to estimate the prevalence and association between H. pylori and kidney disease and CKD. METHODS: Comprehensive literature review was conducted using MEDLINE and EMBASE database through October 2016 to identify studies that reported the prevalence or the association between H. pylori infection and non-dialysis-dependent kidney diseases or CKD. Effect estimates from the individual study were extracted and combined using random-effect, generic inverse variance method of DerSimonian and Laird. RESULTS: Of 4546 studies, nine cross-sectional studies met the eligibility criteria and were included in the meta-analysis. The estimated prevalence of H. pylori infection among subjects with kidney disease was 53% (95% CI 45-61%). The pooled OR of H. pylori in patients with non-dialysis-dependent kidney diseases was 1.20 (95% CI 0.73-1.97) when compared with the patients without kidney diseases. The meta-analysis was then limited to only studies evaluating the risk of H. pylori in CKD; the pooled OR of H. pylori in patients with CKD was 1.00 (95% CI 0.58-1.71). CONCLUSIONS: The estimated prevalence of H. pylori in patients with non-dialysis-dependent kidney diseases is 53%. This study does not support the association between H. pylori infection and non-dialysis-dependent kidney diseases nor CKD.

Hypertension in Pregnancy: Defining Blood Pressure Goals and the Value of Biomarkers for Preeclampsia.

Hypertensive disorders in pregnancy have been the cause of much clinical dilemma, affecting up to 10 % of all pregnancies. The precise blood pressure to achieve in a pregnant woman is usually a battle between minimizing end organ damage to the mother and providing adequate perfusion to the placenta and the fetus. This predicament is becoming more, not less, frequent as maternal ages increase in high resource nations. Biomarkers to predict preeclampsia, a subcategory of hypertension in pregnancy, have always been elusive. The discovery of angiogenic factors relevant to preeclampsia in the last decade, however, has propelled much needed research, both in the basic science and clinical arenas. In this review, we summarize the latest clinical studies and international guidelines on blood pressure goals in pregnancy, and discuss the most promising of biomarkers to predict or diagnose preeclampsia.

A pragmatic approach to selective genetic testing in kidney transplant candidates.

Introduction: Advances in the field of genetic testing have spurred its use in transplantation. Potential benefits of genetic testing in transplant nephrology include diagnosis, treatment, risk stratification of recurrent disease, and risk stratification in potential donors. However, it is unclear how to best apply genetic testing in this population to maximize its yield. We describe our transplant center's approach to selective genetic testing as part of kidney transplant candidate and donor evaluation. Methods: Transplant recipient candidates were tested if they had a history of ESRD at age <50, primary FSGS, complement-mediated or unknown etiology of kidney disease, or had a family history of kidney disease. Donors were tested if age <35, were related to their potential recipients with known genetic susceptibility or had a first-degree relative with a history of kidney disease of unknown etiology. A targeted NGS gene panel of 385 genes was used. Clinical implications and downstream effects were monitored. Results: Over 30% of recipients tested within the established criteria were positive for a pathogenic variant. The most common pathogenic variants were APOL1 high-risk genotypes as well as collagen 4-alpha-3, -4 and -5. Donor testing done according to our inclusion criteria resulted in about 12% yield. Positive test results in recipients helped with stratification of the risk of recurrent disease. Positive test results in potential donors guided informed decisions on when not to move forward with a donation. Discussion: Integrating targeted panel genetic testing into a kidney transplant clinic in conjunction with a selective criteria for testing donors and recipients ensured a reasonable diagnostic yield. The results had implications on clinical management, risk stratification and in some cases were instrumental in directing downstream changes including when to stop the evaluation process. Given the impact on management and transplant decisions, we advocate for the widespread use of genetic testing in selected individuals undergoing transplant evaluation and donation who meet pre-defined criteria.

Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering.

Objectives: This study aimed to identify distinct clusters of very elderly kidney transplant recipients aged ≥80 and assess clinical outcomes among these unique clusters. Design: Cohort study with machine learning (ML) consensus clustering approach. Setting and participants: All very elderly (age ≥80 at time of transplant) kidney transplant recipients in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database database from 2010 to 2019. Main outcome measures: Distinct clusters of very elderly kidney transplant recipients and their post-transplant outcomes including death-censored graft failure, overall mortality and acute allograft rejection among the assigned clusters. Results: Consensus cluster analysis was performed in 419 very elderly kidney transplant and identified three distinct clusters that best represented the clinical characteristics of very elderly kidney transplant recipients. Recipients in cluster 1 received standard Kidney Donor Profile Index (KDPI) non-extended criteria donor (ECD) kidneys from deceased donors. Recipients in cluster 2 received kidneys from older, hypertensive ECD deceased donors with a KDPI score ≥85%. Kidneys for cluster 2 patients had longer cold ischaemia time and the highest use of machine perfusion. Recipients in clusters 1 and 2 were more likely to be on dialysis at the time of transplant (88.3%, 89.4%). Recipients in cluster 3 were more likely to be preemptive (39%) or had a dialysis duration less than 1 year (24%). These recipients received living donor kidney transplants. Cluster 3 had the most favourable post-transplant outcomes. Compared with cluster 3, cluster 1 had comparable survival but higher death-censored graft failure, while cluster 2 had lower patient survival, higher death-censored graft failure and more acute rejection. Conclusions: Our study used an unsupervised ML approach to cluster very elderly kidney transplant recipients into three clinically unique clusters with distinct post-transplant outcomes. These findings from an ML clustering approach provide additional understanding towards individualised medicine and opportunities to improve care for very elderly kidney transplant recipients.

Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.

BACKGROUND: Better understanding of the different phenotypes/subgroups of non-U.S. citizen kidney transplant recipients may help the transplant community to identify strategies that improve outcomes among non-U.S. citizen kidney transplant recipients. This study aimed to cluster non-U.S. citizen kidney transplant recipients using an unsupervised machine learning approach; Methods: We conducted a consensus cluster analysis based on recipient-, donor-, and transplant- related characteristics in non-U.S. citizen kidney transplant recipients in the United States from 2010 to 2019 in the OPTN/UNOS database using recipient, donor, and transplant-related characteristics. Each cluster's key characteristics were identified using the standardized mean difference. Post-transplant outcomes were compared among the clusters; Results: Consensus cluster analysis was performed in 11,300 non-U.S. citizen kidney transplant recipients and identified two distinct clusters best representing clinical characteristics. Cluster 1 patients were notable for young age, preemptive kidney transplant or dialysis duration of less than 1 year, working income, private insurance, non-hypertensive donors, and Hispanic living donors with a low number of HLA mismatch. In contrast, cluster 2 patients were characterized by non-ECD deceased donors with KDPI <85%. Consequently, cluster 1 patients had reduced cold ischemia time, lower proportion of machine-perfused kidneys, and lower incidence of delayed graft function after kidney transplant. Cluster 2 had higher 5-year death-censored graft failure (5.2% vs. 9.8%; p < 0.001), patient death (3.4% vs. 11.4%; p < 0.001), but similar one-year acute rejection (4.7% vs. 4.9%; p = 0.63), compared to cluster 1; Conclusions: Machine learning clustering approach successfully identified two clusters among non-U.S. citizen kidney transplant recipients with distinct phenotypes that were associated with different outcomes, including allograft loss and patient survival. These findings underscore the need for individualized care for non-U.S. citizen kidney transplant recipients.

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.

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.

Distinct Phenotypes of Kidney Transplant Recipients in the United States with Limited Functional Status as Identified through Machine Learning Consensus Clustering.

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.

Case report: Successful simultaneous heart-kidney transplantation across a positive complement-dependent cytotoxic crossmatch.

Preformed donor-specific antibodies are associated with a higher risk of rejection and worse graft survival in organ transplantation. However, in heart transplantation, the risk and benefit balance between high mortality on the waiting list and graft survival may allow the acceptance of higher immunologic risk donors in broadly sensitized recipients. Transplanting donor-recipient pairs with a positive complement dependent cytotoxic (CDC) crossmatch carries the highest risk of hyperacute rejection and immediate graft loss and is usually avoided in kidney transplantation. Herein we report the first successful simultaneous heart-kidney transplant with a T- and B-cell CDC crossmatch positive donor using a combination of rituximab, intravenous immunoglobulin, plasmapheresis, bortezomib and rabbit anti-thymocyte globulin induction followed by eculizumab therapy for two months post-transplant. In the year following transplantation, both allografts maintained stable graft function (all echocardiographic left ventricular ejection fractions ≥ 65%, eGFR>60) and showed no histologic evidence of antibody-mediated rejection. In addition, the patient has not developed any severe infections including cytomegalovirus or BK virus infection. In conclusion, a multitarget immunosuppressive regimen can allow for combined heart/kidney transplantation across positive CDC crossmatches without evidence of antibody-mediated rejection or significant infection. Longer follow-up will be needed to further support this conclusion.

Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering.

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.

Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States.

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.