The impact of induction therapy on the risk of posttransplant lymphoproliferative disorder in adult kidney transplant recipients with donor-recipient serological Epstein-Barr virus mismatch.

Posttransplant lymphoproliferative disorder (PTLD) poses a significant concern in Epstein-Barr virus (EBV)-negative patients transplanted from EBV-positive donors (EBV R-/D+). Previous studies investigating the association between different induction agents and PTLD in these patients have yielded conflicting results. Using the Organ Procurement and Transplant Network database, we identified EBV R-/D+ patients >18 years of age who underwent kidney-alone transplants between 2016 and 2022 and compared the risk of PTLD with rabbit antithymocyte globulin (ATG), basiliximab, and alemtuzumab inductions. Among the 6620 patients included, 64.0% received ATG, 23.4% received basiliximab, and 12.6% received alemtuzumab. The overall incidence of PTLD was 2.5% over a median follow-up period of 2.9 years. Multivariable analysis demonstrated that the risk of PTLD was significantly higher with ATG and alemtuzumab compared with basiliximab (adjusted subdistribution hazard ratio [aSHR] = 1.98, 95% confidence interval [CI] 1.29-3.04, P = .002 for ATG and aSHR = 1.80, 95% CI 1.04-3.11, P = .04 for alemtuzumab). However, PTLD risk was comparable between ATG and alemtuzumab inductions (aSHR = 1.13, 95% CI 0.72-1.77, P = .61). Therefore, the risk of PTLD must be taken into consideration when selecting the most appropriate induction therapy for this patient population.

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.

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.

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

Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT) recipients with DGF using OPTN/UNOS data. DGF was observed in 20.9% (n = 17,073) of KT and most kidneys had a KDPI score <85%. Four distinct clusters were identified. Cluster 1 recipients were young, high PRA re-transplants. Cluster 2 recipients were older diabetics and more likely to receive higher KDPI kidneys. Cluster 3 recipients were young, black, and non-diabetic; they received lower KDPI kidneys. Cluster 4 recipients were middle-aged, had diabetes or hypertension and received well-matched standard KDPI kidneys. By cluster, one-year patient survival was 95.7%, 92.5%, 97.2% and 94.3% (p < 0.001); one-year graft survival was 89.7%, 87.1%, 91.6%, and 88.7% (p < 0.001). There were no differences between clusters after accounting for death-censored graft loss (p = 0.08). Clinically meaningful differences in recipient characteristics were noted between clusters, however, after accounting for death and return to dialysis, there were no differences in death-censored graft loss. Greater emphasis on recipient comorbidities as contributors to DGF and outcomes may help improve utilization of DGF at-risk kidneys.

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.

Outcomes of kidney retransplantation in recipients with prior posttransplant lymphoproliferative disorders: An analysis of the 2000-2019 UNOS/OPTN database.

This study utilized the UNOS database to assess clinical outcomes after kidney retransplantation in patients with a history of posttransplant lymphoproliferative disease (PTLD). Among second kidney transplant patients from 2000 to 2019, 254 had history of PTLD in their first kidney transplant, whereas 28,113 did not. After a second kidney transplant, PTLD occurred in 2.8% and 0.8% of patients with and without history of PTLD, respectively (p = .001). Over a median follow-up time of 4.5 years after a second kidney transplant, 5-year death-censored graft failure was 9.5% vs. 12.6% (p = .21), all-cause mortality was 8.3% vs. 11.8% (p = .51), and 1-year acute rejection was 11.0% vs. 9.3% (p = .36) in the PTLD vs. non-PTLD groups, respectively. There was no significant difference in death-censored graft failure, mortality, and acute rejection between PTLD and non-PTLD groups in adjusted analysis and after propensity score matching. We conclude that graft survival, patient survival, and acute rejection after kidney retransplantation are comparable between patients with and without history of PTLD, but PTLD occurrence after kidney retransplantation remains higher in patients with history of PTLD.

Outcomes of kidney retransplantation after graft loss as a result of BK virus nephropathy in the era of newer immunosuppressant agents.

We conducted this study using the updated 2005-2016 Organ Procurement and Transplantation Network database to assess clinical outcomes of retransplant after allograft loss as a result of BK virus-associated nephropathy (BKVAN). Three hundred forty-one patients had first graft failure as a result of BKVAN, whereas 13 260 had first graft failure as a result of other causes. At median follow-up time of 4.70 years after the second kidney transplant, death-censored graft survival at 5 years for the second renal allograft was 90.6% for the BK group and 83.9% for the non-BK group. In adjusted analysis, there was no difference in death-censored graft survival (P = .11), acute rejection (P = .49), and patient survival (P = .13) between the 2 groups. When we further compared death-censored graft survival among the specific causes for first graft failure, the BK group had better graft survival than patients who had prior allograft failure as a result of acute rejection (P < .001) or disease recurrence (P = .003), but survival was similar to those with chronic allograft nephropathy (P = .06) and other causes (P = .05). The better allograft survival in the BK group over acute rejection and disease recurrence remained after adjusting for potential confounders. History of allograft loss as a result of BKVAN should not be a contraindication to retransplant among candidates who are otherwise acceptable.

Cytomegalovirus mismatch still negatively affects patient and graft survival in the era of routine prophylactic and preemptive therapy: A paired kidney analysis.

The impact of cytomegalovirus (CMV) serostatus on kidney transplant outcomes in an era when CMV prophylactic and preemptive strategies are used routinely is not clearly established. Using United Network for Organ Sharing/Organ Procurement and Transplantation Network data, recipients with first deceased donor kidney transplant (≥18 years, 2010-2015) were stratified into 4 groups in the main cohort: CMV-seronegative donor (D-)/CMV-seronegative recipient (R-), CMV-seropositive donor (D+)/R-, D+/CMV-seropositive recipient (R+), and D-/R+. In a paired kidney cohort, we identified 2899 pairs of D- kidney transplant with discordance of recipient serostatus (D-/R- vs D-/R+) and 4567 pairs of D+ kidney transplant with discordance of recipient serostatus (D+/R- vs D+/R+). In the main cohort, D+/R- was associated with a higher risk of graft failure (hazard ratio [HR] = 1.17, P = .01), all-cause mortality (HR = 1.18, P < .001), and infection-related mortality (HR = 1.38, P = .03) compared with D-/R-. In the paired kidney analysis, D+/R- was an independent risk factor for all-cause mortality (HR = 1.21, P = .003) and infection-related mortality (HR = 1.47, P = .04) compared with D+/R+. No difference in graft loss between D+/R- and D+/R+. CMV mismatch is still an independent risk factor for graft loss and patient mortality. The negative impact of D+/R- serostatus on mortality persists after fully matching for donor factors.

Recurrence of IgA nephropathy after kidney transplantation in steroid continuation versus early steroid-withdrawal regimens: a retrospective analysis of the UNOS/OPTN database.

In the past 20 years, there has been an increase in use of steroid-withdrawal regimens in kidney transplantation. However, steroid withdrawal may be associated with an increased risk of recurrent IgA nephropathy (IgAN). Using United Network of (Organ Sharing/Organ Procurement and Transplantation Network) UNOS/OPTN data, we analyzed adult patients with end-stage renal disease (ESRD) due to IgAN who received their first kidney transplant between 2000 and 2014. For the primary outcome, we used a competing risk analysis to compare the cumulative incidence of graft loss due to IgAN recurrence between early steroid-withdrawal (ESW) and steroid continuation groups. The secondary outcomes were patient survival and death-censored graft survival (DCGS). A total of 9690 recipients were included (2831 in ESW group and 6859 in steroid continuation group). In total, 1238 recipients experienced graft loss, of which 191 (15.43%) were due to IgAN recurrence. In multivariable analysis, steroid use was associated with a decreased risk of recurrence (subdistribution hazard ratio 0.666, 95% CI 0.482-0.921; P = 0.014). Patient survival and DCGS were not different between the two groups. In the USA, ESW in transplant for ESRD due to IgAN is associated with a higher risk of graft loss due to disease recurrence. Future prospective studies are warranted to further address which patients with IgAN would benefit from steroid continuation.

Idiopathic granulomatous interstitial nephritis responsive to mycophenolate mofetil therapy.

Granulomatous interstitial nephritis (GIN) is a rare histologic disease. Various causes have been reported in the literature, including drugs, sarcoidosis, and infections. Other incidents have no discernible cause and are identified as idiopathic. We report a 68-year-old white man who presented with acute kidney injury and was given a diagnosis of idiopathic GIN. Mycophenolate mofetil treatment was elected because of steroid toxicity. He responded well to mycophenolate mofetil and has been in remission for more than 3 years. To our knowledge, this is the first report of successful treatment with mycophenolate mofetil of an adult patient with idiopathic GIN.

Prevalence of cancer in membranous nephropathy: a systematic review and meta-analysis of observational studies.

BACKGROUND: The association between membranous nephropathy (MN) and cancer has been well documented. However, the true prevalence and characteristics of cancer associated with MN have not been well described. METHODS: A systematic review and meta-analysis of cohort studies was conducted to summarize the prevalence of cancer-associated MN as well as patient characteristics and types of cancer in this population. We used a random-effects meta-analysis model to estimate the prevalence of cancer. RESULTS: We included 6 studies (n = 785). The estimated prevalence of cancer was 10.0% (95% CI, 6.1-14.6). The mean age of MN patients with cancer was 67 ± 7 years. The diagnosis of cancer preceded the diagnosis of MN in 20 ± 6.8%. Lung cancer was the most common type of tumor, accounting for 22 cases (26%), followed by prostate cancer (13 cases, 15%), hematologic malignancies (12 cases, 14%), colorectal cancer (9 cases, 11%), breast cancer (6 cases, 7%), and stomach and esophageal cancer (5 cases, 6%). CONCLUSION: The estimated prevalence of cancer in patients with MN is 10% (95% CI, 6.1-14.6). The vast majority of tumors associated with MN are lung and prostate cancer. Hematologic malignancies should also be considered as one of the potential cancers associated with MN. Our study was based on a largely Caucasian population; therefore, the findings might not be applicable to other populations.

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.

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.

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.

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.

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 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.