American Journal of Transplantation

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.

Unplanned readmissions after liver transplantation occur in over 30% of recipients, yet no validated prediction models exist, and prior observational studies suffer from immortal time bias. The optimal readmission window for outcome prediction and the feasibility of early risk stratification remain undefined. This study is a retrospective analysis of 922 adult liver transplant recipients (August 2018-August 2025) at a single center. Time-varying Cox regression evaluated 14-, 30-, and 90-day readmission windows as predictors of 1-year mortality, correcting for immortal time bias. Gradient-boosted machine learning models leveraging 528,400 laboratory measurements (28 analytes) predicted 90-day readmission using either complete hospitalization data or data restricted to postoperative day 7. Feature importance was quantified by gain, and clinical utility was assessed through risk stratification. Among 902 hospital survivors, 342 (37.9%) experienced an unplanned readmission within 90 days of initial discharge. Only the 90-day readmission window predicted 1-year mortality in time-varying analysis (HR 1.73, 95% CI 1.17-2.57, p=0.006). The model for readmission using complete data achieved AUC 0.614 (95% CI 0.576-0.652); the postoperative day 7 restricted model achieved AUC 0.615 (95% CI 0.577-0.652), with no meaningful performance difference. The tacrolimus coefficient of variation x peak creatinine interaction was the dominant predictor in both the complete model (17.3% importance, rank 1) and the day 7 restricted model (20.4% importance, rank 2). This interaction stratified patients into high-risk (tacrolimus CV >0.3 and creatinine >2.0 mg/dL; 49.8% readmission) versus low-risk (24.8% readmission) groups (risk ratio 2.01, p<0.001). These results identify a modifiable biological determinant of readmission and establish a framework for targeted interventions to reduce unplanned readmission and improve post-transplant outcomes.

The iBox is a validated prognostication system that predicts graft loss in kidney transplant recipients (KTRs), but its performance in the specific population of African American recipients has not been fully studied. We conducted a multicenter study on 3,588 KTRs from North America, including 866 (24.1%) African American KTRs, to assess the impact of race on the iBoxs performance in predicting graft loss. Performance metrics for the prediction of graft loss were similar for both African American and non-African American KTRs in terms of discrimination (C-index 0.81 [95% CI: 0.78-0.84] and 0.83 [95%CI: 0.81-0.85], respectively, p=0.25), and calibration (observed/expected ratio 1.13 [95%CI: 1.03-1.24] and 1.03 [95%CI: 0.95-1.11], respectively, p=0.13). No significant interaction between iBox score values and race was found in multivariate analysis stratified by transplant center (p=0.80 for the interaction term). Results were consistent regardless of the equation used to estimate glomerular filtration rate (Kidney Recipient Specific, CKD-EPI, or MDRD). In conclusion, the iBox prognostication system accurately predicts graft loss up to 7 years post-risk evaluation in African American KTRs and confirms its relevance as a surrogate endpoint in this population.

BackgroundInter- and intra-individual variability in tacrolimus dose requirements mandates empirical clinician-titrated dosing that frequently results in deviation from a narrow target range. Improved methods to individually dose tacrolimus are needed. Our objective was to determine whether a quantitative, dynamically-customized, phenotypic-outcome-guided dosing method termed Phenotypic Personalized Medicine (PPM) would improve target drug trough maintenance. MethodsIn a single-center, randomized, pragmatic clinical trial (NCT03527238), 62 adults were screened, enrolled, and randomized prior to liver transplantation 1:1 to standard-of-care (SOC) clinician-determined or PPM-guided dosing of tacrolimus. The primary outcome measure was percent days with large (>2 ng/mL) deviation from target range from transplant to discharge. Secondary outcomes included percent days outside-of-target-range and mean area-under-the-curve (AUC) outside-of-target-range per day. Safety measures included rejection, graft failure, death, infection, nephrotoxicity, or neurotoxicity. Results56 (29 SOC, 27 PPM) patients completed the study. The primary outcome measure was found to be significantly different between the two groups. Patients in the SOC group had a mean of 38.4% of post-transplant days with large deviations from target range; the PPM group had 24.3% of post-transplant days with large deviations; (difference -14.1%, 95% CI: -26.7 to -1.5 %, P=0.029). No significant differences were found in the secondary outcomes. In post-hoc analysis, the SOC group had a 50% longer median length-of-stay than the PPM group [15 days (Q1-Q3: 11-20) versus 10 days (Q1-Q3: 8.5-12); difference 5 days, 95% CI: 2-8 days, P=0.0026]. ConclusionsPPM guided tacrolimus dosing leads to better drug level maintenance than SOC. The PPM approach leads to actionable dosing recommendations on a day-to-day basis. Lay SummaryIn a study on 62 adults who underwent liver transplantation, researchers investigated whether a new dosing method called Phenotypic Personalized Medicine (PPM) would improve daily dosing of the immunosuppression drug tacrolimus. They found that PPM guided tacrolimus dosing leads to better drug level maintenance than the standard-of-care clinician-determined dosing. This means that the PPM approach leads to actionable dosing recommendations on a day-to-day basis and can help improve patient outcomes.

The long-term impact of SARS-CoV-2 infection on kidney allograft survival remains incompletely understood, particularly regarding the influence of vaccination, acute kidney injury (AKI), and post-infection immunosuppression. We conducted a retrospective analysis of 129 kidney transplant recipients with confirmed SARS-CoV-2 infection between March 2020 and March 2022 with a median follow-up of 50 months. Among 129 recipients, 106 (82%) received vaccination at any time before or after SARS-CoV-2 infection (82%) while 23 (18%) remained unvaccinated. Unvaccinated patients experienced significantly lower long-term graft survival (52% vs. 85%; p = 0.0004) and patient survival (83% vs. 99%; p = 0.0003) compared with vaccinated recipients. AKI occurred in 15% of recipients and independently predicted graft failure (aHR 2.88; p = 0.0341). Post-SARS-CoV-2 serum creatinine and albuminuria were strong prognostic markers of graft loss. Unvaccinated status independently predicted graft failure in both transplantation-anchored (aHR 2.80; p = 0.0342) and SARS-CoV-2-anchored models (aHR 5.31; p = 0.0004). Continuation of mycophenolate mofetil at post-infection assessment was associated with reduced graft-failure risk (aHR 0.99; p = 0.0193). These findings underscore the importance of sustained vaccination in preserving long-term allograft function.

The international Banff classification for kidney transplant pathology discretizes the rejection continuum into distinct diagnostic categories, introducing artificial dichotomization and threshold effects. To better reflect the underlying disease spectrum, we developed, in this cohort study, two novel indices for quantifying antibody-mediated (AMR) and T-cell mediated rejection (TCMR) from histological lesion scores, and calculated indices for overall activity and chronicity. These indices were evaluated in one derivation cohort and two independent validation cohorts, totaling 19,500 biopsies from 8,873 kidney transplant patients across 10 centers worldwide. The AMR, TCMR, and activity indices demonstrated hierarchical ordering between No rejection, intermediate and complete rejection histology. The chronicity index showed limited association with the major diagnostic categories. In the derivation cohort, the AMR and TCMR indices discriminated AMR from absence of AMR, and TCMR from absence of TCMR, with an AUC of 0.98 (95% confidence interval 0.97 to 0.98) and 0.99 (0.99 to 1.00) respectively). This excellent discrimination was confirmed in the validation cohorts. Those indices strictly confined intermediate phenotypes to a range of low index values and related to graft failure rates even within the diagnostic categories, thus reflecting the underlying rejection continuum. The four continuous indices offer an implementable and interpretable global evaluation of kidney transplant biopsy histology while eliminating the need for intermediate diagnostic categories and enable more probabilistic reasoning in the diagnostic approach to the spectrum of kidney transplant rejection.

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.

ImportanceTransplantation is one of the few areas in medicine where the definitive treatment is rationed. Subjective decision-making pose challenges towards the transplant selection process. It has been proposed that large language models (LLMs) as artificial intelligent (AI) agents could provide objectivity in decision-making to solve complex problems. ObjectiveTo examine the performance of a multidisciplinary selection committee of AI agents (AI-SC) as a proof-of-concept towards objectivity in the liver transplant (LT) selection process. DesignThe AI-SC consisted of four LLMs: transplant hepatologist, transplant surgeon, cardiologist, and social worker. Zero-shot prompting with chain-of thought was used. Decisions were made based on clinicodemographic characteristics at time of waitlisting and LT. SettingNational LT cohort. ParticipantsAdult patients receiving deceased donor LT from 2004-2023 were extracted from the Scientific Registry of Transplant Recipients (SRTR) and clinical vignettes were generated. Standard absolute contraindications to LT were randomly assigned to a subset of patients to expose the AI-SC to cases of patients declined for LT. ExposuresClinicodemographic characteristics at waitlisting and transplantation. Main Outcomes and MeasuresThe AI-SCs accuracy with either: 1) listing candidates if LT would offer a 6-month or 1-year survival benefit or 2) declining candidates if contraindications to LT are present or if LT would not offer those survival benefits. ResultsOf 8,412 patients, 83.6% were waitlisted and 16.4% had contraindications to LT. The AI-SC was able to accurately identify contraindications to LT (accuracy: 98.2%, 95%CI 97.9%-98.4%), predict 6-month (94.9%, 95%CI 94.4%-95.3%) and 1-year (92.0%, 95%CI 91.4%-92.6%) survival. HCC burden beyond Milan criteria was the most common reason for accepted patients who were declined by AI-SC (False Negative). Malignancy was the most common cause of death prior to 6-month or 1-year end points (False Positive). The AI-SC most frequently did not perceive a lack of social support or severe cardiopulmonary disease as barriers to LT. Conclusions and RelevanceLLMs can be leveraged to simulate the LT-SC meetings and provide accurate, objective insights on patients who may or may not benefit from LT. Lessons learned from this proof-of-concept are a provocative step towards making the LT selection process more equitable and objective. Key PointsO_ST_ABSQuestionC_ST_ABSCan a multidisciplinary selection committee of artificial intelligence-based agents (AI-SC) accurately select liver transplant (LT) candidates based on potential survival benefit and contraindications to LT? FindingsClinical vignettes were generated from 8,412 LT candidates from the Scientific Registry of Transplant Recipients (SRTR). Of these, 16.4% were randomly assigned standard absolute contraindications to LT. The AI-SC (GPT-4, OpenAI) reviewed and selected LT candidates with accuracies of 98.2% in identifying contraindications to LT, 94.9% in predicting 6-month survival benefit, and 92.0% in predicting 1-year survival benefit. MeaningMulti-agent models may be leveraged to provide guidance towards objective decision-making in transplant candidacy.

BackgroundGenetic analysis in transplantation offers potential for personalized medicine. Given the crucial role of the complement system in renal allograft injury, we investigated in kidney transplant pairs the impact of complement polymorphisms on long-term outcomes. MethodsIn this observational cohort study, we analyzed polymorphisms in C3 (C3R102G), factor B (CFBR32Q), and factor H (CFHV62I) genes of 1,271 donor-recipient kidney transplant pairs and assessed their association with 15-year death-censored allograft survival. ResultsIndividually, only the presence of the CFB32Q variant in the donor and the combined presence in donor-recipient pairs were associated with better graft survival (P=0.027 and P=0.045, respectively). In the combined analysis, the C3R102G, CFBR32Q, and CFHV62I variants in the donor independently associated with the risk of graft loss (HR 1.32; 95%-CI, 1.08- 1.58; P=0.005). Thus, donor kidneys carrying the genetic variants that promote the highest complement activity exhibited the worst graft survival, whereas those with the genetic variants causing the lowest complement activity showed the best graft survival (15-year death-censored allograft survival: 48.8% vs 87.8%, P=0.001). ConclusionOur study demonstrates that the combination of complement polymorphisms in the donor strongly associates with long-term allograft survival following kidney transplantation. These findings hold significance for therapeutic strategies involving complement inhibition in kidney transplantation.

BackgroundData on age-related differences in rejection rates, infectious episodes and tacrolimus exposure in pediatric kidney transplant recipients (pKTR) on a uniform tacrolimus-based immunosuppressive regimen are scarce. MethodsWe therefore performed a large-scale analysis of 802 pKTR from the CERTAIN registry from 40 centers in 14 countries. Inclusion criteria were a tacrolimus-based immunosuppressive regimen and at least two years of follow-up. The patient population was divided into three age groups (infants <6 years, school-aged children 6-12 years, and adolescents >12 years) to assess age-related differences in outcome. ResultsMedian follow-up was 48 months (IQR, 36-72). Within the first 2 years post-transplant, infants had a significantly higher incidence of infections (80.6% vs. 55.0% in adolescents, P<0.001) and a significantly higher number of cumulative hospital days (median 13 days vs. 7 days in adolescents, P < 0.001). Adolescents had a significantly higher rate of biopsy-proven acute rejection episodes in the first year post-transplant (21.7%) than infants (12.6%, P=0.007). Infants had significantly lower tacrolimus trough levels, lower concentration-to-dose ratios as an approximation for higher tacrolimus clearance, and higher intra-patient variability (all P < 0.01) than adolescents. ConclusionsThis largest study to date in European pKTR on a tacrolimus-based immunosuppressive regimen shows important age-related differences in rejection rates, infection episodes, tacrolimus exposure and clearance. These data suggest that immunosuppressive therapy in pKTR should be tailored according to the age-specific risk profiles of this heterogeneous patient population.

Kidney transplantation remains the optimal treatment for end-stage kidney disease (ESKD). However, the persistent disparity between the demand and supply of deceased donor (DD) kidneys underscores the need for better tools to assess transplant outcomes and donor kidney quality. The current Kidney Allocation System (KAS) relies on the Kidney Donor Risk Index (KDRI) to quantify DD kidney quality, yet it combines allograft failure and patient death into a single outcome, limiting its accuracy. In this paper we present refined statistical models to predict post-transplantation risk, differentiating between allograft failure and patient death as competing risks. Using comprehensive data from the Organ Procurement and Transplantation Network/Scientific Registry of Transplant Recipient (OPTN/SRTR) for 2000-2017, our models incorporate biological and clinical factors instead of donor race, account for within-center clustering and between-center variation, and capture non-linear relationships between risk factors. Our results reveal distinct risk factors for allograft failure and patient death. These models provide more personalized risk estimates tailored to donor kidney quality and recipient characteristics, aiding shared decision-making on kidney acceptance. Comparisons with the original KDRI demonstrate the superiority of our separate models, with improved predictability and reduced bias. Our approach eliminates the need to conflate allograft failure and patient death, leading to more accurate risk assessment and better-informed decisions regarding kidney offers. In conclusion, our study underscores the importance of distinguishing between allograft failure and patient death in kidney transplant risk assessment. By offering more precise risk estimates, our models enhance the transparency and efficiency of kidney acceptance decisions, ultimately benefiting both transplant providers and candidates. We also provide a web-based tool to facilitate this process, promoting better outcomes in kidney transplantation. Key PointsO_LIImproved statistical models for kidney transplant risk assessment, separating the risks of allograft failure and patient death. C_LIO_LIModels provide more personalized risk estimates, outperforming the existing Kidney Donor Risk Index (KDRI). C_LIO_LIModels enhance transparency and accuracy in evaluating donor kidney quality, aiding both providers and candidates in decision-making. C_LIO_LIResearch improves the efficiency of kidney acceptance processes, leading to more successful transplants. C_LI

BackgroundDespite current matching efforts to identify optimal donor-recipient pairs in kidney transplantation, alloimmunity remains a major proponent of late transplant failure. While kidney allocation based on human leukocyte antigen (HLA) matching has markedly prolonged short-term graft survival, new data suggests that additional genetic parameters in donor-recipient matching could help improve the long-term outcomes. Here, we studied the impact of a recently discovered non-muscle myosin heavy chain 9 gene (MYH9) polymorphism on kidney allograft failure. MethodsWe conducted a prospective observational cohort study, analyzing the DNA of 1,271 kidney donor-recipient transplant pairs from a single academic hospital for the MYH9 rs11089788 C>A polymorphism. The association of the MYH9 genotype with the risk of graft failure (primary outcome), biopsy-proven acute rejection (BPAR), and delayed graft function (DGF) (secondary outcomes) were determined. ResultsThe MYH9 polymorphism in the donor was not associated with 15-year death-censored kidney graft survival, whereas a trend was seen for the association between the MYH9 polymorphism in the recipient and graft failure (recessive model, P=0.056). Having the AA-genotype of the MYH9 polymorphism in recipients was associated with a higher risk of DGF (P=0.031) and BPAR (P=0.021), although the significance was lost after adjustment for potential confounders (P=0.15 and P=0.10, respectively). The combined presence of the MYH9 polymorphism in donor-recipient pairs was significantly associated with long-term kidney allograft survival (P=0.036), in which recipients with an AA-genotype receiving a graft with an AA-genotype had the worst outcome. After adjustment for covariates, this combined genotype remained significantly associated with 15-year death-censored kidney graft survival (HR 1.68, 95%-CI: 1.05 - 2.70, P=0.031). ConclusionsOur results reveal that recipients with an AA-genotype MYH9 polymorphism receiving a donor kidney with an AA-genotype, have a significantly elevated risk of graft failure after kidney transplantation. Key pointsO_LIIn recipients, the MYH9 SNP was associated with delayed graft function and biopsy-proven acute rejection after kidney transplantation, although the significance was lost in multivariable analysis. C_LIO_LIPresence of the MYH9 variant in both the donor and recipient significantly associated with long-term kidney allograft survival in multivariable analysis. C_LIO_LIOur present findings suggests that matching donor-recipient transplant pairs based on the MYH9 polymorphism may attenuate the risk of graft loss. C_LI

BackgroundPlasma cell rich rejection (PCRR) is an uncommon, ill-defined type of renal allograft rejection in the current literature considered a subtype of T cell-mediated rejection (TCMR). PCRR has poorer clinical outcome and is often refractory to classic immunosuppressive therapy. Our study analyzed clinical course, Banff lesion scores and mRNA expression of PCRR compared to (late) rejection. MethodsWe retrospectively scored and reclassified the last known biopsy of 263 renal transplant recipients, morphologically classified as rejection according to the 2019 Banff classification. mRNA expression analysis was performed using the Nanostring B-HOT panel on a subset of cases. PCRR was compared to (late) TCMR, ABMR and mixed rejection for renal function follow-up and graft survival. ResultsmRNA analysis revealed uniquely expressed genes in PCRR including LOX, CPA3, IL4, IL17F, and MMP12. PCRR is enriched for genes related to mast cells, memory B- and T-cells and transcripts involved in NK cells and allograft fibrosis with heterogeneity in gene expression in biopsies with PCRR. PCRR might be a late event compared to late TCMR and ABMR, with a higher degree of total inflammation and fibrosis. Graft survival and renal function was similar to late TCMR and ABMR during a 5-year follow-up period after renal biopsy. ConclusionPCRR represents a distinct late-onset stage of inflammation displaying diverse gene expression patterns, with presence of mainly mast cells, NK cells and transcripts involved in renal allograft fibrosis. Clinical outcomes in patients with PCRR appeared more similar to late TCMR and ABMR.

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.

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.

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.

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 ([&ge;] 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.

BackgroundDespite the increasing disparity between the number of patients awaiting kidney transplants and the availability of deceased donor kidneys, a significant number of donated kidneys go unused. Early identification of organs at high risk of nonuse can facilitate effective allocation interventions, ensuring these organs are offered to patients who could potentially benefit from them. While several machine learning models have been developed to predict nonuse risk, the complexity of these models compromises their practical implementation. MethodsWe propose implementable nonuse risk prediction models that consist of a minimal set of variables, including the Kidney Donor Risk Index (KDRI), along with factors selected by machine learning models or transplantation experts. Our approach takes into account the influence of Organ Procurement Organization (OPO) behavior on kidney disposition. ResultsThe proposed models demonstrate competitive performance compared to more complex models that involve a large number of variables. Importantly, they maintain simplicity and interpretability. ConclusionsOur results provide accurate risk predictions, offer valuable insights into key factors contributing to kidney nonuse, and underscore significant variations among OPOs in the allocation of hard-to-place kidneys. These findings can inform the design of effective organ allocation interventions, increasing the likelihood of transplantation for hard-to-place kidneys.

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.

Costimulation blockade using belatacept results in improved renal function after kidney transplant as well as decreased likelihood of death/graft loss and reduced cardiovascular risk; however, higher rates and grades of acute rejection have prevented its widespread clinical adoption. Treatment with belatacept blocks both positive (CD28) and negative (CTLA-4) T cell signaling. CD28-selective therapies may offer improved potency by blocking CD28-mediated costimulation while leaving CTLA-4 mediated coinhibitory signals intact. Here we test a novel domain antibody directed at CD28 (anti-CD28 dAb (BMS-931699)) in a non-human primate kidney transplant model. Sixteen macaques underwent native nephrectomy and received life-sustaining renal allotransplantation from an MHC-mismatched donor. Animals were treated with belatacept alone, anti-CD28 dAb alone, or anti-CD28 dAb plus clinically relevant maintenance (MMF, Steroids) and induction therapy with either anti-IL-2R or T cell depletion. Treatment with anti-CD28 dAb extended survival compared to belatacept monotherapy (MST 187 vs. 29 days, p=0.07). The combination of anti-CD28 dAb and conventional immunosuppression further prolonged survival to MST [~]270 days. Animals maintained protective immunity with no significant infectious issues. These data demonstrate CD28-directed therapy is a safe and effective next-generation costimulatory blockade strategy with a demonstrated survival benefit and presumed advantage over belatacept by maintaining intact CTLA-4 coinhibitory signaling.