Transplantation

BackgroundOrgan availability limits kidney transplantation, the best treatment for end-stage kidney disease. Globally, deceased donor acceptance criteria have been relaxed to include older donors, which comes with a higher risk of inferior posttransplant outcomes. Donor age, although negatively impacts transplant outcomes, lacks granularity in predicting graft dysfunction. Better donor kidney assessment and characterization of the biological mechanisms underlying age-associated donor organ damage and transplant outcomes is key to improving donor kidney utilisation and transplant longevity. Methods185 deceased pretransplant biopsies (from brain and circulatory death donors aged 18-78 years) were obtained from the Quality in Organ Donation (QUOD) biobank and proteomic profiles were acquired by mass spectrometry. Machine learning exploration using prediction rule ensembles guided LASSO regression modeling of kidney proteomes that identified protein signatures and biological mechanisms associated with 12-m posttransplant outcome. Data modeling was validated on held-out data and contextualised against published spatially resolved kidney injury related transcriptomes. ResultsOur analysis highlighted that outcomes were best modeled using combination of donor age and protein abundance signatures, revealing 539 proteins with these characteristics. Modeled age:protein interactions demonstrated stronger associations with transplant outcomes than age and protein alone and revealed mechanisms of kidney injury including metabolic changes and innate immune responses correlated with poor outcome. Comparison to single-cell transcriptome data suggests protein-outcome associations to specific cell types. ConclusionsMolecular signatures resulted from integration of donor age and proteomic profiles in deceased donor kidney biopsies offer the potential to develop improved pretransplant organ assessment and aid decisions on perfusion interventions.

Solid organ transplant is a curative treatment for end organ disease. However, the immunosuppressive therapy required to prevent graft rejection increases the likelihood of developing a subsequent malignancy. This retrospective cohort study from a multi-center academic hospital system investigated solid organ transplantation, immunosuppression, and the risk of subsequent malignancy. Of the 5,591 patients and 6,142 transplanted organs studied, there were 517 subsequent malignancies identified. Skin cancer was the most common type of malignancy to be diagnosed, whereas liver cancer was the first malignancy to present at a median time of one-year post-transplant. Subsequent malignancy was proportionally more often diagnosed in non-Hispanic White transplant recipients compared to other racial groups. Heart and lung transplant recipients had relatively higher rates of subsequent malignancy than liver and kidney transplant recipients, but this finding was not significant upon adjusting for immunosuppressive medications. Multivariate cox proportional hazard analysis and random forest variable importance calculations identified statistically significant correlations with sirolimus and azathioprine and high rates of malignancy after transplant, while tacrolimus was associated with low rates of post-transplant neoplasia.

The integration of regenerative medicine into dynamic organ preservation may mitigate ischemia-reperfusion injury in kidney transplantation. This systematic review and meta-analysis evaluated the therapeutic potential of stem cell-based interventions during machine perfusion. Following PRISMA guidelines, PubMed, Embase, and Scopus were searched for experimental studies using stem cells or extracellular vesicles (EVs) during hypothermic or normothermic machine perfusion in animal or discarded human kidneys. Outcomes included renal function, injury biomarkers, inflammation, and histology. Nine studies were included, seven in meta-analysis. Despite heterogeneity in models and protocols, several reported reductions in inflammatory cytokines (e.g., IL-6, IL-1{beta}) and biomarkers (e.g., NGAL) following stem cell or EVs administration. However, meta-analysis showed no significant effects on creatinine clearance (Standardized Means (SMD): 0.00; 95% CI: -0.54 to 0.55), urine output (SMD: 0.54; 95% CI: -0.46 to 1.55), or NGAL (SMD: -1.68; 95% CI: -5.60 to 2.25). Histological protection varied, and stem cell retention was limited. Only one study assessed post-transplant function. While stem cell therapies during perfusion may have immunomodulatory and cytoprotective effects, consistent functional benefits were not observed. Further standardized studies, including transplant models and long-term outcomes, are needed to clarify therapeutic potential and optimize delivery strategies.

Vascularized composite allotransplantation (VCA) involves immunologically heterogeneous tissues and is associated with high rates of acute rejection (AR), particularly due to the immunogenicity of the skin. This study assessed biomarkers to monitor AR in a recipient of full-face and bilateral hand transplantation. Over 4.6 years postoperatively, we analyzed serial blood and tissue samples, including donor-derived cell-free DNA (dd-cfDNA) in plasma, recipient DNA in allograft biopsies using short tandem repeat (STR) analysis, lymphocyte subsets via flow cytometry, and level of angiotensin II type 1 receptor antibodies (AT1R-Abs). dd-cfDNA and STR showed trends toward elevated recipient signal during acute rejection, though differences were not statistically significant. CD8+ T-cell percentages increased before AR onset, suggesting potential as a prognostic biomarker. AT1R-Ab levels did not differ significantly between AR and non-rejection episodes, possibly due to prophylactic immune cell depletion. While dd-cfDNA and STR levels correlate with rejection episodes and reflect key cellular events within graft tissue, CD8+ T-cell count remains the most robust prognostic biomarker for predicting the onset of cytotoxicity in this patient. These findings highlight the importance of further longitudinal, multi-patient studies to validate emerging biomarkers and improve rejection monitoring strategies in VCA.

The COVID-19 pandemic has reduced access to solid organ transplantation, compounding organ shortages and waitlist mortality. A continued area of uncertainty is the safety of transplanting organs recovered from SARS-CoV-2 infected donors, as autopsies of patients who died with COVID-19 show that the virus can be found in extra-pulmonary organs1. Case reports and series on transplantation of these organs have been published 2, 3, but population-level data is lacking. We queried a national transplant database for recipients of organs recovered from donors recently infected by SARS-CoV-2. For organs with more than 50 cases, these were then propensity-score matched at a ratio of 1:10 to similar recipients of organs recovered from donors who tested negative for SARS-CoV-2 (controls). Data were extracted from the Scientific Registry of Transplant Recipients (SRTR - v2203 - updated March 2022), which collects detailed information on all solid organ transplants in the United States since 1986. Cases were defined as adult ([≥] 18 years) recipients of organs recovered from deceased donors who tested positive for SARS-CoV-2 by nasopharyngeal or lower respiratory sample polymerase chain reaction or antigen assay within 7 days of organ transplantation. Multiple organ transplants were excluded. There were 775 kidney, 330 liver, 123 heart, 44 kidney-pancreas, 16 lung, 5 pancreas, and 3 small bowel transplants of organs recovered from 393 deceased donors recently infected by COVID-19. For kidney, liver, and heart transplants, Kaplan-Meier curves of both overall and graft survival at 90 days were similar between cases and controls. Our data shows that transplanting kidneys, livers, and hearts recovered from deceased donors recently infected by SARS-CoV-2 was not associated with increased recipient mortality or worse graft-survival. This should help transplant providers make decisions regarding acceptance of these organs, and counsel transplant candidates on the safety of receiving them. The limited number of kidney-pancreas, lung, pancreas, and intestinal cases precludes significant conclusions for these organs. Our data also strongly supports the notion that donors with recent COVID-19 infection should not be automatically excluded from the donor pool. The limited number of kidney-pancreas, lung, pancreas, and intestinal cases precludes significant conclusions for these organs. Limitations include lack of data on donor infection timeline and estimates of viral load (PCR cycle thresholds), description of donor COVID-19 symptomatology at organ procurement, donor or recipient vaccination or prior COVID-19 infection status, which are not tracked in the database. We did not have information regarding transmission of COVID-19 to transplant recipients. Future analysis of updated versions of the database should help address. Our data strongly support the notion that donors with recent COVID infection should not be automatically excluded from the donor pool. Prospective studies are needed to confirm our findings and provide insights on optimal post-transplant management of these recipients.

A central dogma in physiology is that oxygen release at tissues is not diffusion-limited because gas exchange at capillaries is rapid. This assertion has influenced clinical care, which focuses on optimising oxygen delivery through improving blood flow and oxygen content, rather than oxygen unloading from red blood cells (RBCs). Since storage of blood causes profound changes that slow oxygen release from RBCs, transfusions could compromise tissue respiration. We investigated this in transplant human kidneys normothermically perfused with stored blood. During perfusions, renal respiration was measured from blood gases and RBCs were analysed for oxygen-unloading kinetics. Respiratory rate did not correlate significantly with the standard definition of oxygen delivery based on blood flow and oxygen content. However, a strong correlation was obtained after introducing a factor describing oxygen release from RBCs. Oxygen release to tissues can become diffusion-limited with transfused blood, and therefore the kinetic quality of RBCs should be considered.

BACKGROUNDDespite a sharp rise in kidney graft short-term survival rates and better donor-recipient HLA matching, mid- and long-term survival have not sufficiently improved over the past decades. Several studies suggest that non-HLA factors could be involved in kidney allograft injury, but no validated marker has yet been identified. Here, we aimed at finding genetic variations and mismatches associated with graft function and survival. METHODSUsing genome-wide strategies, we tested the recipients and donors common variations (SNPs and CNVs), and the donor-recipient genetic mismatches for association with 1-year kidney graft function and with time-to-death-censored kidney graft failure in a monocentric European cohort of 1,482 complete donor-recipient pairs. We validated our findings through a meta-analysis in two independent European cohorts gathering a total of 1,842 additional complete pairs. RESULTSWe did not identify any significant association with 1-year graft function. However, we discovered four non-HLA mismatches (3 SNPs and 1 CNV) associated with time-to-kidney graft failure. One signal in a regulatory region upstream the TOM1L1 gene (p=6.3x10-9, HR=4.1) was successfully replicated in the validation cohorts (pmeta-analysis=6.7x10-9, HR=2.9) and ranked among the top 50 rejection-specific genes in a pan-organ transcriptomics study. This locus was also associated with time-to-cellular and humoral rejection (p=0.02) in the discovery cohort in patients achieving primary graft function. CONCLUSIONSBy running one of the largest ever performed kidney transplantation genomic analyses, we identified and confirmed a novel donor-recipient genetic mismatch in a biologically relevant non-HLA locus associated with kidney allograft failure. Lay SummaryDespite significant advances in immunosuppression, kidney transplant recipients remain at risk of graft rejection and, in more severe cases, graft failure, which can lead to retransplantation, return to dialysis, or even patient death. Donor-recipient HLA compatibility is integrated into kidney transplant clinical care, as this genetic region is key for immunity and graft tolerance. However, several studies suggest that compatibility outside of the HLA region could also influence graft survival. We aimed at investigating this hypothesis in a cohort of 1,482 donor-recipient pairs and found a novel genetic region involved in kidney graft dysfunction that was validated after meta-analysis of two independent cohorts. These findings contribute to a better understanding of the impact of donor-recipient genetic compatibility on kidney transplant outcomes.

The development of de novo donor-specific antibodies (DSAs) against HLA is associated with premature graft failure in kidney transplantation. However, rates and factors influencing de novo DSA formation vary widely across the literature. We aimed to identify pre-transplant factors influencing the development of de novo HLA-specific antibodies following kidney transplantation using machine learning. Data from 460 kidney transplant recipients at a single centre between 2009-2014 were analysed. Pre-transplant variables were collected, and post-transplant sera were screened for HLA antibodies. Positive samples were investigated using Single Antigen Bead (SAB) testing. Machine learning models (Classification and Regression Trees, Random Forest, XGBoost, CatBoost) were trained on a training set of pre-transplant data to predict de novo DSA formation, with and without SMOTE oversampling. Model performance was evaluated on an independent testing set using F1 scores, and feature importance was assessed using SHAP. In the full cohort analysis, XGBoost models performed the best, with F1 scores of 0.54-0.59 without SMOTE and 0.72-0.79 with SMOTE. The strongest predictors were pre-transplant HLA antibodies, number of kidney transplants, cold ischemia time (CIT), recipient age and female gender. SHAP dependence plots showed that pre-existing HLA antibodies and past transplants increased the risk of de novo DSA development. In the unsensitised subgroup analysis, model performance was poor. Machine learning models can be used to identify pre-transplant risk factors for de novo HLA-specific antibody development in kidney transplantation. Monitoring and risk-stratifying patients based on these factors may help guide preventive immunological strategies and recipient selection to improve long-term allograft outcomes. Translational statementThis study identified pre-transplant risk factors for the development of de novo HLA-specific antibody in kidney transplantation. Monitoring and risk-stratifying patients based on these factors may help guide preventive immunological strategies and recipient selection to improve long-term allograft outcomes.

IntroductionHypothermic machine perfusion (HMP) has greatly facilitated kidney allograft preservation. However, tissue damage still occurs during HMP, deleteriously affecting post-transplant graft function. Therefore, improved methods to assess organ quality and to predict post-transplant graft function and survival are needed. We propose that soluble DNA (sDNA) measured in HMP perfusate can used as a non-invasive biomarker for this purpose. MethodsPerfusate samples of kidney grafts placed on HMP were collected after 5 minutes and at the conclusion of HMP. sDNA of nuclear origin within the perfusate was quantified by real-time polymerase chain reaction and correlated with HMP parameters and post-transplant clinical outcomes. ResultsKidney grafts from 52 donors placed on HMP were studied. Perfusate sDNA concentration was significantly elevated in transplanted kidneys with delayed graft function. Grafts with higher concentrations of perfusate sDNA at 5min and at HMP conclusion also had reduced graft function in the initial post-transplant period, as measured by post-operative day 2, 3, and 4 creatinine reduction ratios (CRR). Standard pump parameters such as renal vascular resistance and renal vascular flow were poor indicators of early post-transplant graft function. ConclusionsDNA concentration in HMP perfusate of kidney grafts can predict the quality of kidney graft preservation and indicate post-transplant renal function. This biomarker should be explored further to improve renal organ assessment and transplantation outcomes.

BackgroundHeart transplantation (HT) in the United States continues to be increasingly performed in the setting of expanding Medicare and Medicaid public insurance coverage. We analyze the impact of recipient insurance trajectories on long-term graft and recipient survival following the 2018 United Network for Organ Sharing (UNOS) heart allocation change. MethodsAdults aged 18-64 undergoing first-time HT between 10/2018 and 3/2024 were identified in the UNOS database. Patients surviving [&ge;]1 year were stratified by insurance trajectory (private vs. public, including Medicare, Medicaid, and VA) at waitlist, transplantation, and 1-year follow-up. Kaplan-Meier and Cox regression models were used to assess survival and graft failure risk. ResultsAmong 15,864 patients, 42.5% had continuous private insurance, 33.9% had continuous public insurance, 12.6% transitioned from private to public, 4.8% transitioned from public to private, and 6.3% experienced multiple transitions. Five-year survival (p<0.0001) and graft survival (p=0.03) were poorer in patients with continuous public insurance or who transitioned from private to public insurance. Primary graft dysfunction rates were highest among recipients with continuous public insurance (1.4%; p<0.0001). Continuous public insurance (HR 1.27, p<0.0001) and multiple transitions (HR 1.19, p=0.04) increased mortality risk compared to continuous private insurance. Continuous public insurance (HR 1.29, p=0.01), private-to-public transition (HR 1.32, p=0.03), and multiple transitions (HR 1.09, p=0.02) were linked to higher graft failure risk. Public-to-private transition was associated with lower graft failure risk (HR 0.73, p=0.02). Residence in distressed communities increased mortality (HR 1.21, p=0.003) and graft failure risk (HR 1.31, p=0.001). ConclusionsPublic insurance and insurance instability are associated with worse HT outcomes, while gaining private insurance was associated with improved outcomes, highlighting disparities in post-transplant care. What is KnownO_LIPost-heart-transplant outcomes differ by insurance type, with publicly insured recipients generally experiencing worse long-term survival and higher complication rates than privately insured recipients. C_LIO_LIPrior transplant analyses often treat insurance as a static exposure rather than a time-varying trajectory spanning waitlist, transplant, and follow-up. C_LIO_LIInsurance transitions ("churn") are detrimental: in pre-Affordable Care Act and the 2018 allocation change cohorts, private to public switch within 1-year associates with higher mortality. C_LIO_LISocioeconomic disadvantage independently correlates with poorer post-transplant outcomes beyond clinical risk. C_LI What the Study AddsO_LIThis is the first national, post-2018 allocation analysis to model insurance as a time-varying trajectory across the waitlist, transplant hospitalization, and the first post-transplant year. C_LIO_LIContinuous public insurance and insurance instability (multiple coverage transitions) are each independently associated with higher adjusted risks of mortality and graft failure than continuous private insurance. C_LIO_LIThe direction of churn is clinically meaningful: a private to public switch increases graft-failure risk, whereas public to private associates with lower mortality and graft failure rates compared with remaining on public insurance C_LIO_LIDefines a distinct and actionable morbidity profile linked to continuous public coverage--longer hospitalizations and higher rates of dialysis, acute rejection, and treatment for rejection, along with the highest incidence of primary graft dysfunction -- pinpointing concrete targets for perioperative pathways, discharge planning, and payer-continuity interventions. C_LIO_LINeighborhood socioeconomic distress independently predicts mortality and graft failure above and beyond clinical covariates and insurance trajectory, highlighting structural levers for quality-improvement and policy. C_LI

BACKGROUNDChronic antibody-mediated rejection (CAMR) is the main cause of late kidney allograft loss, and no specific effective treatment has been identified so far. Here, we proposed to explore the non-invasive peripheral blood transcriptome signature of CAMR. METHODSFirst, we compared PBMC gene expression from bulk RNA-seq between 35 patients experiencing late CAMR (mean=7.1 years) vs. 43 patients without graft dysfunction at late stages (Stable, mean=4.4 years) to identify the molecular drivers of CAMR. Second, we explored the 1-year gene expression signature in stable patients exhibiting (n=11) or not (n=51) a subsequent CAMR to define possible predictive biomarkers of CAMR. RESULTSWe reported 188 differentially regulated genes during late CAMR (q<0.05). Importantly, CAMR is associated with an upregulation of genes from the degranulation pathway (e.g. MMP9, MMP8 and LCN2) and from the C1q complement complex (e.g. C1QA, C1QB and C1QC), as well as with a downregulation of genes associated with subclinical rejection (e.g. TCL1A). The upregulated degranulation and complement signatures were validated in six independent cohorts gathering a total of 360 stable and 131 chronic rejection patients. Contrary to the injury effect observed during late stages, MMP9 was downregulated at 1-year in PBMCs of patients who later experienced CAMR. CONCLUSIONSThese results suggest a dual role for MMP9 expression with an early protective effect against CAMR and deleterious effects in the later stage. MMP9 peripheral expression appears as a promising biomarker candidate for kidney transplantation follow-up. Translational StatementCAMR is the main cause of late kidney allograft loss, and we aimed to identify molecular targets and biomarkers. By comparing CAMR and stable kidney-transplanted patients prior and during diagnosis, we identified MMP9 as a potential biomarker for both CAMR prognosis and diagnosis. We also highlighted the C1q complement complex and TCL1A in CAMR as potential diagnostic biomarkers. These results provide new insights into CAMR pathophysiology and may guide the development of innovative treatment targeting MMP9 expression in kidney transplanted patients. Ultimately, this work laid the foundation for exploring MMP9 expression kinetics and develop new ways of treating CAMR patients.

BackgroundThe effects of Banff histological diagnoses on kidney transplant outcome have been well characterized. However, repeated observation of such histological injury across multiple biopsies in kidney transplant recipients remains insufficiently explored. MethodsIn an observational cohort (N=1819 transplantations with 5736 post-transplant biopsies, recurrent event survival models quantified transitions between diagnoses of T-cell mediated rejection (TCMR), antibody-mediated rejection (AMR), DSA-negative C4d-negative microvascular inflammation (MVIDSA-/C4d-), BK polyomavirus nephropathy (BKPyVAN), borderline TCMR (bTCMR), and probable AMR (pAMR), revealing patterns in the disease trajectories. In two observational cohorts (N=1818 transplantations with 5732 biopsies, N=853 transplantations with 975 biopsies), time-dependent cumulative covariates were constructed for TCMR, AMR, MVIDSA-/C4d- and BKPyVAN, enabling estimation of associations of repeated diagnoses with graft failure using multivariable cause-specific Cox models. ResultsThe incidence rate of a diagnosis was most strongly associated with earlier diagnosis of the same type, but associations between different types of diagnoses also occurred. The hazard of kidney graft failure was significantly increased by repeated observation of TCMR in multiple biopsies (HR 7.97, 95% CI 4.94 - 12.86), as well as by repeated AMR (HR 6.19, 95% CI 3.15 - 12.17), repeated MVIDSA-/C4d- (HR 4.53, 95% CI 2.15-9.54) and repeated BKPyVAN (HR 10.90, 95% CI 5.83 - 20.35). The hazard of graft failure was increased more after repeated diagnoses in transplants than after first diagnoses. The effects of repeated TCMR and repeated AMR remained significant even when observed in protocol biopsies in the absence of graft dysfunction. Repeated observation of BKPyVAN was the most detrimental of all diagnoses when observed in indication biopsies, but it was the least harmful when observed in protocol biopsies. ConclusionIncidence of Banff histological diagnoses appears to be affected by earlier diagnoses, especially those of the same type. These repeated observations of a specific diagnosis have an additional effect on the hazard of graft failure, underscoring a critical unmet need for adequate treatment strategies for these recurrent or persistent injury processes. Lay summaryIn two observational cohorts of 1819 and 750 kidney transplant recipients, kidney transplant biopsies were taken at multiple time points after transplantation. Based on the Banff classification for transplant pathology, various post-transplant diseases were diagnosed, often at more than one time point during follow-up. We assessed patterns in the occurrence of diagnoses over time, and related these diagnoses to survival of the kidney grafts using survival models with time-dependent cumulative diagnoses. We found that repeated observation of the same diagnosis was much more common than consecutive observations of different diagnoses. Repeated diagnoses of tissue injury also decreased kidney graft survival more compared to single diagnoses. This indicates that treatment options for patients with repeated or persistent diagnoses are currently inadequate and novel strategies are needed.

Clonal hematopoiesis (CH) promotes inflammation and is associated with the development of cardiovascular disease. Previous studies assessing CH mutations in orthotopic heart transplant (OHT) recipients have revealed inconsistent findings, likely due to small sample size and differing sample collection time. In this study, we investigated the association between CH and post-transplant outcomes with a more consistent sample collection window. This retrospective study included 209 patients who underwent OHT between 2015 and 2022. Targeted sequencing detected CH mutations from samples obtained within a window of six months before or after transplantation. Clinical data were collected from the electronic medical record. Patients undergoing OHT had a median age of 53 years, and 27% were female. CH-associated mutations with a variant allele frequency (VAF) greater than 2% were detected in 29 patients (13.9%). The commonly mutated genes included DNMT3A, TET2, and ASXL1. CH mutations were associated with an increased risk of antibody-mediated rejection (AMR) (HR 2.42, 95% CI 1.07-5.47, p=0.033), but without detected differences in mortality or cardiac allograft vasculopathy (CAV). CH mutations detected at the time of transplant were associated with clinically significant AMR. Sample analysis at the time of transplant provides the clearest association between CH mutations and outcomes in OHT.

The demographics of donor and recipient candidates for kidney transplantation (KT) have substantially changed. Recipients tend to be older and polymorbid and KT to marginal recipients is associated with delayed graft function (DGF), prolonged hospitalization, inferior long-term allograft function, and poorer patient survival. In parallel, donors are also older, suffer from several comorbidities, and donations coming from circulatory death (DCD) predominate, which in turn leads to early and late complications. However, it is unclear how donor and recipient risk factors interact. In this retrospective cohort study, we assess the overall and combined impact of a KT from marginal donors to marginal recipients. We focused on: 1) DGF; 2) hospital stay and number of dialysis days after KT and 3) allograft function at 6 months. Among the 369 KT included, the overall DGF rate was 25% (n = 92) and median time from reperfusion to DGF resolution was 7.8 days (IQR: 3.0-13.8 days). Overall, patients received four dialysis sessions (IQR: 2-8). The combination of pre-KT anuria (< 200 ml/24h, 32%) and DCD procurement (14%) was significantly associated with DGF, length of hospital stay, and severe perioperative complications, predominantly in recipients 50 years and older.

BackgroundHypothermic machine perfusion (HMP) has become a standard method for preserving deceased donor kidneys, offering advantages over static cold storage. Perfusion parameters like renal vascular resistance (RR) have been explored as potential decision-making tools for kidney transplantability, but their clinical use remains unclear. AimWe aimed to investigate the use of perfusion parameters in decision-making regarding the acceptance of pumped deceased donor kidneys among Organ Procurement Organizations (OPOs) in the USA and Canada. MethodsAn anonymous, internet-based survey was sent to 69 OPOs in the USA and Canada, collecting data on the use of HMP, perfusion parameters, and thresholds for transplantability decisions. Descriptive statistics were used for analysis. ResultsOf the 67 OPOs contacted, 15 (22%) responded, with 13 complete responses (87%). All OPOs used HMP, with 93% perfusing both donation after brain death and circulatory death kidneys. While 97% of OPOs used perfusion parameters in decision-making, none relied solely on these parameters. Two OPOs (15%) did not use them at all, while six OPOs (46%) considered them with other data or on a case-by-case basis. Only one OPO (9%) reported using specific thresholds for perfusion parameters, applying flow [&ge;]100 mL/min, resistance <0.3 mmHg/mL/min, and pressure between 15-35 mmHg. ConclusionHMP is widely used, but substantial variability exists in the use of perfusion parameters for transplant decisions. Most OPOs do not rely on these parameters alone and lack standardized thresholds, though specific thresholds are still used.

Underlying medical conditions, graft failure, and immunosuppression place solid organ transplant recipients ("recipients) at heighten risk of death. We report the underlying causes of death among adult US recipients and compare their mortality to the US general population. We obtained causes of death by linking a sample of deaths from the US organ transplant registry to the National Death Index and weighted the linked deaths to represent all deaths among recipients aged [&ge;]18 years during 1999-2019. To compare mortality to the US general population, we calculated standardized mortality ratios (SMRs). Among 496,467 recipients, 153,491 deaths occurred, of which 99,373 were NDI-linked. Leading causes of death were heart disease (16.9% of deaths), graft failure (14.9%), and cancer (14.4%). Compared to the US general population, recipients had 4.02 times the risk of death, and mortality was elevated for all 16 causes analyzed (e.g., 3.32-fold for heart disease and 2.08-fold for cancer) except dementia/Alzheimers disease. During 2015-2019, overall mortality was elevated 3.08-fold, with lung recipients experiencing the highest elevation (SMR=7.91), followed by heart (3.20), liver (2.86), and kidney (2.81) recipients. Although mortality improved over time, US recipients continue to face substantially elevated mortality, both overall and for common causes of death.

Optimizing immunosuppressive therapy remains central to improving long-term outcomes after kidney transplantation. Both induction and maintenance therapies are widely used, yet their comparative effectiveness across heterogeneous populations requires further evaluation. To this end, this national retrospective cohort study analyzed 228,855 deceased-donor kidney transplant recipients using the over two-decade data (2000-2024). We employed multivariable Cox proportional hazards (PH) models for clinical inference and four machine learning (ML) survival models: random survival forest (RSF), support vector machine (SVM), penalized Cox regression (CoxNet), and extreme gradient boosting optimized with the Cox partial likelihood (XGBoost-Cox) to assess predictive performance of death-censored graft failure and all-cause patient mortality. Model performance was evaluated using the concordance index (C-index) and time-dependent area under the curve (tdAUC). Maintenance regimens with calcineurin inhibitors (CNI) and mycophenolate mofetil (MMF) demonstrated protective effects for both graft failure (CNI+MMF: hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.70-0.74; CNI+MMF+steroids: HR 0.84, 95% CI 0.82-0.87) and patient mortality (CNI+MMF: HR 0.78, 95% CI 0.76-0.81; CNI+MMF+steroids: HR 0.90, 95% CI 0.88-0.93). Among induction therapies, antithymocyte globulin (ATG) showed protective associations (HR 0.93 for both outcomes), while interleukin-2 receptor (IL-2R) antagonists and Alemtuzumab demonstrated neutral effects. Combined ATG + IL-2R therapy comparatively increased the hazard of graft failure (HR 1.09). Recipient diabetes, dialysis dependence, older age, and higher Kidney Donor Profile Index (KDPI) were strong adverse predictors. Traditional Cox regression achieved robust discrimination (graft failure concordance index: 0.685; patient mortality concordance index: 0.704) comparable to ML survival models. These findings reinforce CNI and MMF maintenance regimens as foundational to contemporary immunosuppression while demonstrating differential effectiveness across induction strategies. The dual analytical framework, which integrates classical Cox PH modeling with ML survival models, shows that Cox models remain highly competitive for clinical inference while ML approaches offer complementary predictive value to support individualized post-transplant risk stratification.

BACKGROUND: Machine learning (ML) models have been used to evaluate one-year post-transplant mortality in donor-recipient pairs. Previous modeling utilizing noninterpretable ML methods (deep neural networks [DNN] and XGBoost) showed modest gains in area under the receiver operating curve (AUC) beyond logistic regression, but suffered a significant drop in predictive AUC applied to the subsequent years' data and lacked statistical significance in validating previously identified risk factors. METHODS: Using balanced SRTR datasets, we evaluated non-interpretable models against interpretable ML models (adaptive logistic regression with interaction terms [aLR], Classification and Regression tree [CART], and conditional inference tree [CIT]) for one-year mortality, including comprehensive clinician-supervised data curation and inclusion of variables describing pre- and post-2018 listing status changes. Models were trained/tested using rolling-window validation across years and further analyzed with repeated ten-fold crossvalidation. Interaction terms were obtained via Adaptive Best-Subset Selection (ABESS). RESULTS: Predictive validation before the listing policy change in 2018 showed similar AUCs between DNN (0.579), aLR (0.642), CART (0.579), and CIT (0.584), with XGBoost having a higher (0.763) AUC. However, in the post-2018 predictive analysis, aLR outperformed XGBoost (AUC 0.613 vs. 0.586). The interpretable ML models confirm the significance of previously reported risk factors (recipient bilirubin and creatinine) and identify risk factors not previously reported (donor pH, potential recipient distance, and recipient transfusion), as well as clinically relevant interaction terms. CONCLUSIONS: Carefully developed interpretable ML models of one-year transplant mortality have similar predictive performance to black-box models, while identifying novel risk factors, and showing improved performance after recent listing policy changes. With appropriate validation and additional data, interpretable ML modeling may allow real-time data-driven donor selection.

BackgroundSubclinical kidney allograft acute rejection (SCR) corresponds to "the unexpected histological evidence of acute rejection in a stable patient". The diagnosis of SCR relies on surveillance biopsy. Positron emission tomography (PET/CT) after injection of F18-fluorodeoxyglucose ([18F]FDG) has been proposed as a non-invasive screening approach. In the present multicenter prospective study, we assess the diagnostic yield [18F]FDGPET/CT to rule out SCR in stable KTR at 3 months post KTx. MethodsFrom 01/2021 to 03/2025, we prospectively combined surveillance biopsy and [18F]FDGPET/CT at [~]3 months post transplantation in adult kidney transplant recipients from 4 independent imaging centers. The mean standardized uptake value (mSUV) was measured in kidney cortex and referenced as a ratio to psoas muscle mSUV (mSUVR). ResultsOur multicentric 185-patient cohort was categorized upon Banff-2022: normal (n=158); borderline (n=18); SCR (n=9, including 6 T-cell-mediated rejection and 3 microvascular inflammation). No significant correlation was observed between the mSUVR and ti score (R=0.032, p-value=0.67). The mSUVR reached 2.33 [1.97-2.93], 2.71 [2.50-3.33] and 2.42 [2.27-3.14] in normal, borderline and SCR groups, respectively. In multivariate models stratified by center, the risk of non-normal histology (n=27, including borderline and SCR) increased with donor age (OR=1.05 [1.01-1.1], p=0.02) but not with the mSUVR (OR=4.11 [0.91-18.48], p=0.07). The risk of biopsy-proven SCR (n=9) was not significantly associated with mSUVR. ConclusionsThe mSUVR of [18F]FDG PET/CT does not reliably rule out SCR on surveillance biopsy.

Background.There are known racial disparities in the organ transplant allocation system in the United States. However, prior work has yet to establish if transplant center decisions on offer acceptance--the final step in the allocation process-- contribute to these disparities. ObjectiveTo estimate racial differences in the acceptance of organ offers by transplant center physicians on behalf of their patients. DesignRetrospective cohort analysis using data from the Scientific Registry of Transplant Recipients (SRTR) on patients who received an offer for a heart, liver, or lung transplant between January 1, 2010 and December 31, 2020. SettingNationwide, waitlist-based. Patients32,268 heart transplant candidates, 102,823 liver candidates, and 25,780 lung candidates, all aged 18 or older. Measurements1) Association between offer acceptance and two race-based variables: candidate race and donor-candidate race match; 2) association between offer rejection and time to patient mortality. ResultsBlack race was associated with significantly lower odds of offer acceptance for livers (OR=0.93, CI: 0.88-0.98) and lungs (OR=0.80, CI: 0.73-0.87). Donor-candidate race match was associated with significantly higher odds of offer acceptance for hearts (OR=1.11, CI: 1.06-1.16), livers (OR=1.10, CI: 1.06-1.13), and lungs (OR=1.13, CI: 1.07-1.19). Rejecting an offer was associated with lower survival times for all three organs (heart hazard ratio=1.16, CI: 1.09-1.23; liver HR=1.74, CI: 1.66-1.82; lung HR=1.21, CI: 1.15-1.28). LimitationsOur study analyzed the observational SRTR dataset, which has known limitations. ConclusionOffer acceptance decisions are associated with inequity in the organ allocation system. Our findings demonstrate the additional barriers that Black patients face in accessing organ transplants and demonstrate the need for standardized practice, continuous distribution policies, and better organ procurement.