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Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.

Aims The epidemiology of sudden cardiac death (SCD) after heart transplantation (HTx) remains imprecisely described. We aimed to assess the incidence and determinants of SCD in a large cohort of HTx recipients, compared with the general population. Methods and results Consecutive HTx recipients (n = 1246, 2 centres) transplanted between 2004 and 2016 were included. We prospectively assessed clinical, biological, pathologic, and functional parameters. SCD was centrally adjudicated. We compared the SCD incidence beyond the first year post-transplant in this cohort with that observed in the general population of the same geographic area (registry carried out by the same group of investigators; n = 19 706 SCD). We performed a competing risk multivariate Cox model to identify variables associated with SCD. The annual incidence of SCD was 12.5 per 1,000 person-years [95% confidence interval (CI), 9.7–15.9] in the HTx recipients cohort compared with 0.54 per 1,000 person-years (95% CI, 0.53–0.55) in the general population (P < 0.001). The risk of SCD was markedly elevated among the youngest HTx recipients with standardized mortality ratios for SCD up to 837 for recipients ≤30 years. Beyond the first year, SCD was the leading cause of death. Five variables were independently associated with SCD: older donor age (P = 0.003), younger recipient age (P = 0.001) and ethnicity (P = 0.034), pre-existing donor-specific antibodies (P = 0.009), and last left ventricular ejection fraction (P = 0.048). Conclusion HTx recipients, particularly the youngest, were at very high risk of SCD compared with the general population. The consideration of specific risk factors may help identify high-risk subgroups.

Clinical decisions are mainly driven by the ability of physicians to apply risk stratification to patients. However, this task is difficult as it requires complex integration of numerous parameters and is impacted by patient heterogeneity. We sought to evaluate the ability of transplant physicians to predict the risk of long-term allograft failure and compare them to a validated artificial intelligence (AI) prediction algorithm.We randomly selected 400 kidney transplant recipients from a qualified dataset of 4000 patients. For each patient, 44 features routinely collected during the first-year post-transplant were compiled in an electronic health record (EHR). We enrolled 9 transplant physicians at various career stages. At 1-year post-transplant, they blindly predicted the long-term graft survival with probabilities for each patient. Their predictions were compared with those of a validated prediction system (iBox). We assessed the determinants of each physician's prediction using a random forest survival model.Among the 400 patients included, 84 graft failures occurred at 7 years post-evaluation. The iBox system demonstrates the best predictive performance with a discrimination of 0.79 and a median calibration error of 5.79%, while physicians tend to overestimate the risk of graft failure. Physicians' risk predictions show wide heterogeneity with a moderate intraclass correlation of 0.58. The determinants of physicians' prediction are disparate, with poor agreement regardless of their clinical experience.This study shows the overall limited performance and consistency of physicians to predict the risk of long-term graft failure, demonstrated by the superior performances of the iBox. This study supports the use of a companion tool to help physicians in their prognostic judgement and decision-making in clinical care.The ability to predict the risk of a particular event is key to clinical decision-making, for example when predicting the risk of a poor outcome to help decide which patients should receive an organ transplant. Computer-based systems may help to improve risk prediction, particularly with the increasing volume and complexity of patient data available to clinicians. Here, we compare predictions of the risk of long-term kidney transplant failure made by clinicians with those made by our computer-based system (the iBox system). We observe that clinicians’ overall performance in predicting individual long-term outcomes is limited compared to the iBox system, and demonstrate wide variability in clinicians’ predictions, regardless of level of experience. Our findings support the use of the iBox system in the clinic to help clinicians predict outcomes and make decisions surrounding kidney transplants.

Recent large meta-analyses suggested a poorer long-term patients’ and grafts’ outcomes after ABO incompatible (ABOi) living-donor kidney transplantation (LDKT) compared to ABO compatible LDKT. However, little is known about the long-term histological pattern after ABOi LDKT. We compared the histological features observed on protocol biopsies from 03/11 to 11/19 in 94 ABOi LDKT (including 14 with preformed Donor Specific Antibodies, pDSAs), 27 LDKT ABO compatible (ABOc) with pDSAs, and 21 ABOc without pDSAs) during the first five years post transplantation. During the first 5 years post-transplantation, a progression of chronic lesions (patients with a ci >0 raised from 11% to 65%, p0 raised from 29% to 78%, pHence, long-term histological analysis of ABOi LDKT shows only an increase of chronic interstitial and tubular atrophy changes, without active lesions. These data confirm that ABOi LDKT programs can be securely developed.

Objective To compare the performance of a newly developed race-free kidney recipient specific glomerular filtration rate (GFR) equation with the three current main equations for measuring GFR in kidney transplant recipients. Design Development and validation study Setting 17 cohorts in Europe, the United States, and Australia (14 transplant centres, three clinical trials). Participants 15 489 adults (3622 in development cohort (Necker, Saint Louis, and Toulouse hospitals, France), 11 867 in multiple external validation cohorts) who received kidney transplants between 1 January 2000 and 1 January 2021. Main outcome measure The main outcome measure was GFR, measured according to local practice. Performance of the GFR equations was assessed using P 30 (proportion of estimated GFR (eGFR) within 30% of measured GFR (mGFR)) and correct classification (agreement between eGFR and mGFR according to GFR stages). The race-free equation, based on creatinine level, age, and sex, was developed using additive and multiplicative linear regressions, and its performance was compared with the three current main GFR equations: Modification of Diet in Renal Disease (MDRD) equation, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 equation, and race-free CKD-EPI 2021 equation. Results The study included 15 489 participants, with 50 464 mGFR and eGFR values. The mean GFR was 53.18 mL/min/1.73m 2 (SD 17.23) in the development cohort and 55.90 mL/min/1.73m 2 (19.69) in the external validation cohorts. Among the current GFR equations, the race-free CKD-EPI 2021 equation showed the lowest performance compared with the MDRD and CKD-EPI 2009 equations. When race was included in the kidney recipient specific GFR equation, performance did not increase. The race-free kidney recipient specific GFR equation showed significantly improved performance compared with the race-free CKD-EPI 2021 equation and performed well in the external validation cohorts (P 30 ranging from 73.0% to 91.3%). The race-free kidney recipient specific GFR equation performed well in several subpopulations of kidney transplant recipients stratified by race (P 30 73.0-91.3%), sex (72.7-91.4%), age (70.3-92.0%), body mass index (64.5-100%), donor type (58.5-92.9%), donor age (68.3-94.3%), treatment (78.5-85.2%), creatinine level (72.8-91.3%), GFR measurement method (73.0-91.3%), and timing of GFR measurement post-transplant (72.9-95.5%). An online application was developed that estimates GFR based on recipient’s creatinine level, age, and sex ( https://transplant-prediction-system.shinyapps.io/eGFR_equation_KTX/ ). Conclusion A new race-free kidney recipient specific GFR equation was developed and validated using multiple, large, international cohorts of kidney transplant recipients. The equation showed high accuracy and outperformed the race-free CKD-EPI 2021 equation that was developed in individuals with native kidneys. Trial registration ClinicalTrials.gov NCT05229939 .

BACKGROUND AND AIMS Graphical Abstract In kidney transplantation, day-zero biopsies are used to assess organ quality and discriminate lesions inherited from the donor or acquired after transplantation. However, many centres worldwide do not perform those biopsies which remain invasive, costly and may delay the transplant procedure. We aimed to develop and validate a noninvasive virtual biopsy system. METHOD A total of 17 centres were included from Europe, North America and Australia from 2000 to 2019. Candidate predictors were assessed following a prespecified protocol. Outcome measures were the day-zero biopsy lesions (Banff classification) including CV, AH, IFTA scores and % of sclerotic glomeruli. Six machine learning models were developed and their performances were assessed. RESULTS A total of 12 992 day-zero biopsies were included. Eleven parameters were used to build the classifiers, including donor age, kidney function, hypertension, BMI, proteinuria, diabetes, sex, donor type, cause of death and Hep-C status. The ensemble models (random forests, neural networks, gradient boosting, extreme gradient boosting tree, linear discriminant analysis, and naive Bayes) showed multi-AUC of 0.738, 0.817 and 0.788 for prediction of CV, AH and IFTA scores, and a good performance for predicting glomerulosclerosis (mean absolute error, MAE = 4.766). We confirmed the robustness and generalizability in multiple clinical scenarios and subpopulations and built an online interface for clinicians: https://transplant-pred/Virtual_Biopsy. CONCLUSION We developed and validated the first virtual biopsy system that enables the prediction of day-zero biopsy, based on routinely collected parameters. This can assist clinicians in assessing allograft quality, discrimination of donor derived versus acquired lesions after transplantation and prevent overdiagnosis of calcineurin inhibitor (CNI) toxicity.

Prior studies on belatacept conversion from calcineurin inhibitor (CNI) have been limited by an absence of postconversion surveillance biopsies that could underestimate subclinical rejection, or a case-controlled design. A total of 53 adult patients with allograft dysfunction underwent belatacept conversion (median: 6 months) post-transplant. At a median follow-up = 2.5 years, patient survival was 94% with a death-censored graft survival of 85%. Seven (13%) patients had acute rejection (including 3 subclinical) at median 6 months postconversion. Overall, eGFR improved (P =

Background: Cardiac allograft vasculopathy (CAV) is a major contributor of heart transplant recipient mortality. Little is known about the prototypes of CAV trajectories at the population level. We aimed to identify the different evolutionary profiles of CAV and to determine the respective contribution of immune and nonimmune factors in CAV development. Methods: Heart transplant recipients were from 4 academic centers (Pitié-Salpêtrière and Georges Pompidou Hospital, Paris, Katholieke Universiteit Leuven, and Cedars-Sinai, Los Angeles; 2004–2016). Patients underwent prospective, protocol-based monitoring consisting of repeated coronary angiographies together with systematic assessments of clinical, histological, and immunologic parameters. The main outcome was a prediction for CAV trajectory. We identified CAV trajectories by using unsupervised latent class mixed models. We then identified the independent predictive variables of the CAV trajectories and their association with mortality. Results: A total of 1301 patients were included (815 and 486 in the European and US cohorts, respectively). The median follow-up after transplantation was 6.6 (interquartile range, 4–9.1) years with 4710 coronary angiographies analyzed. We identified 4 distinct profiles of CAV trajectories over 10 years. The 4 trajectories were characterized by (1) patients without CAV at 1 year and nonprogression over time (56.3%), (2) patients without CAV at 1 year and late-onset slow CAV progression (7.6%), (3) patients with mild CAV at 1 year and mild progression over time (23.1%), and (4) patients with mild CAV at 1 year and accelerated progression (13.0%). This model showed good discrimination (0.92). Among candidate predictors assessed, 6 early independent predictors of these trajectories were identified: donor age ( P P P =0.001), recipient dyslipidemia ( P =0.009), class II anti–human leukocyte antigen donor-specific antibodies ( P =0.004), and acute cellular rejection ≥2R ( P =0.028). The 4 CAV trajectories manifested consistently in the US independent cohort with similar discrimination (0.97) and in different clinical scenarios, and showed gradients for overall-cause mortality ( P Conclusions: In a large multicenter and highly phenotyped prospective cohort of heart transplant recipients, we identified 4 CAV trajectories and their respective independent predictive variables. Our results provide the basis for a trajectory-based assessment of patients undergoing heart transplantation for early risk stratification, patient monitoring, and clinical trials. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04117152.

Background Transplant glomerulopathy, a common glomerular lesion observed after kidney transplant that is associated with poor prognosis, is not a specific entity but rather the end stage of overlapping disease pathways. Its heterogeneity has not been precisely characterized to date. Methods Our study included consecutive kidney transplant recipients from three centers in France and one in Canada who presented with a diagnosis of transplant glomerulopathy (Banff cg score ≥1 by light microscopy), on the basis of biopsies performed from January of 2004 through December of 2014. We used an unsupervised archetype analysis of comprehensive pathology findings and clinical, immunologic, and outcome data to identify distinct groups of patients. Results Among the 8207 post-transplant allograft biopsies performed during the inclusion period, we identified 552 biopsy samples (from 385 patients) with transplant glomerulopathy (incidence of 6.7%). The median time from transplant to transplant glomerulopathy diagnosis was 33.18 months. Kidney allograft survival rates at 3, 5, 7, and 10 years after diagnosis were 69.4%, 57.1%, 43.3%, and 25.5%, respectively. An unsupervised learning method integrating clinical, functional, immunologic, and histologic parameters revealed five transplant glomerulopathy archetypes characterized by distinct functional, immunologic, and histologic features and associated causes and distinct allograft survival profiles. These archetypes showed significant differences in allograft outcomes, with allograft survival rates 5 years after diagnosis ranging from 88% to 22%. Based on those results, we built an online application, which can be used in clinical practice on the basis of real patients. Conclusions A probabilistic data-driven archetype analysis approach applied in a large, well defined multicenter cohort refines the diagnostic and prognostic features associated with cases of transplant glomerulopathy. Reducing heterogeneity among such cases can improve disease characterization, enable patient-specific risk stratification, and open new avenues for archetype-based treatment strategies and clinical trials optimization.

Introduction Les systemes de prediction actuel de perte du rein transplante n’integrent pas l’effet dynamique des parametres repetes apres la greffe, ce qui limite leur implementation en pratique clinique. Description Nous avons eu pour but de construire un modele de prediction dynamique du risque de perte du greffon pour le transplante renal. Methodes Etude internationale en population composee d’une cohorte francaise de derivation (4 centres) et de cohortes de validation d’Europe et d’Amerique (14 centres) et 6 essais controles randomises (ECR). Les parametres cliniques, histologiques, immunologiques et les mesures repetees fonctionnelles ont ete consideres. Des modeles joints ont ete utilises pour deriver un modele dynamique de prediction. Resultats Au total, 13,608 patients ont ete inclus (3774 dans la cohorte de derivation et 9834 dans les cohortes de validation et les ECRs). En tout, 1893 pertes de greffon et 416,510 mesures de DFG et de proteinurie ont ete observees. L’approche par modele joint a revele que le profil immunologique, la fibrose et l’inflammation du greffon (evaluee avec les scores de Banff), ainsi que les mesures repetees de DFG et de proteinurie etaient independamment associes a la perte du greffon. Les performances du modele final etaient hautes dans la cohorte de derivation (AUC = 0,857). La discrimination et la calibration etaient egalement tres bonnes dans les cohortes de validation (AUC = 0,845 en Europe, 0,820 aux Etats-Unis, 0,868 en Amerique du Sud, et 0,857 dans les ECRs). Nous avons egalement valide le modele final dans une large serie de scenarios cliniques et de sous-populations ( Fig. 1 ). Conclusion Nous avons developpe pour la premiere fois un modele dynamique de prediction qui predit avec precision la survie du greffon au long terme, surpassant les precedents modeles de prediction en transplantation renale. Ce modele a ete valide dans differents centres et pays et pourrait ainsi aider a ajuster le pronostic des cliniciens dans leurs pratiques quotidiennes.

Background Sudden cardiac death (SCD) is a major contributor to the rate of mortality after heart transplantation. However, little is known about the incidence of SCD in heart recipients. Objective To assess the incidence of SCD after heart transplantation. Methods We enrolled consecutive patients transplanted between 2004 and 2017 in two French referral centers. We defined 7 main groups of causes of deaths: SCD, cardiovascular (including Cardiac allograft vasculopathy), infection, primary graft dysfunction, graft failure (including late graft dysfunction, rejection), malignancy and others. Causes of deaths were independently adjudicated by two senior cardiologists based on the analysis of death certificates and medical records. Discrepancies were resolved by discussion until a consensus was made. SCD was defined as an unexpected out-of-hospital cardiac arrest without obvious non-cardiac cause, in the first hour after initiation of symptoms. Results A total of 1,363 patients were included. The median follow-up post-transplant was 3.99 years (IQR=0.49–7.49). 450 patients (33%) deceased during the first year. The leading cumulative causes of death in the first year after transplantation were infection, primary graft failure, multiple organ failure during the period in intensiv car unit. Beyond the post-operativ high-risk period of the first year, the leading cumulative cause of death was SCD: among the 213 deaths that occurred beyond the first year, 44 patients (21%) died from SCD. In this period, the incidence rate of SCD reached 0,82 per 100 person-year (95% CI: 0.51–2.05). Conclusion In a large multicentric and highly phenotyped cohort of heart transplant recipients, the leading cumulative cause of death beyond the first-year post transplant was sudden cardiac death. Our results open discussion on management of heart recipient, such as the implementation of cardioverter-defibrillators. Figure 1. Cumulative incidence of causes of death in heart transplant recipients beyond the first year (n=913). Funding Acknowledgement Type of funding source: None

Background Cardiac allograft vasculopathy (CAV) is a major contributor of heart transplant recipient's mortality. However, little is known about CAV trajectories at a population level. Purpose We aimed to identify the different profiles of CAV trajectories. Methods Heart transplant recipients receiving care at 4 academic centers (2004 to 2016) were included. Patients underwent prospective, protocol-based monitoring consisting of repeated coronary angiographies together with systematic assessment of clinical, functional, histological and immunological parameters. The mainoutcome was the CAV trajectories, identified with unsupervised latent class mixed models. Results Overall, 1,301 patients were included (609 in France, 206 in Belgium and 486 in the US). The median follow-up post-transplant was 6.6 years (IQR=4.7) with 4,710 coronary angiographies analyzed (3.6±1.6 CAV assessments per patient). In the French development cohort, we identified 4 distinct profiles of CAV trajectories over 10 years that were characterized by i) Patients without CAV at baseline and non-progression (n=317, 52.1%), ii) patients without CAV at baseline and late onset CAV progression (n=52, 8.5%), iii) patients with mild baseline CAV and mild progression (n=151, 24.8%), iv) patients with mild baseline CAV and accelerated CAV progression (n=89, 14.6%, discrimination 0.92). The 4 CAV trajectories were independently validated in the external validation cohorts from Belgium (discrimination=0.92) and the US (discrimination=0.97). Conclusion In a large multicentric and highly phenotyped prospective cohort of heart transplant recipients, we identified and validated 4 distinct CAV trajectories corresponding to specific initial CAV grades and subsequent evolutions. Our results provide the basis for a trajectory-based assessment for risk stratification at early-stage post heart transplantation. Figure 1. Cardiac allograft vasculopathy trajectories in France (n=609), in Belgium (n=206), in USA (n=486). Thick lines represent latent class trajectory; thin lines represent CAV individual patient trajectory. Funding Acknowledgement Type of funding source: None

Background Cardiac allograft vasculopathy (CAV) is a major contributor of heart transplant recipient's mortality. Little is known about determinants of CAV trajectories at a population level. Purpose We aimed to identify the respective contribution of immune and non-immune factors in the different evolutive profiles of CAV. Methods Heart transplant recipients receiving care at 2 academic centers (2004 to 2016) were included. Patients underwent prospective, protocol-based monitoring consisting of repeated coronary angiographies together with systematic assessment of clinical, functional, histological and immunological parameters. The outcome was CAV trajectories, identified with unsupervised latent class mixed models. The independent, predictive factors of CAV trajectories were investigated with multinomial regressions (NCT04117152). Results Overall, 815 patients were included. The median follow-up post-transplant was 7.7 years (IQR=5.14) with 2,742 coronary angiographies analyzed. We identified 4 distinct profiles of CAV trajectories over 10 years that were characterized by i) Patients without CAV at baseline and non-progression (n=459, 56.3%), ii) patients without CAV at baseline and late onset CAV progression (n=62, 7.6%), iii) patients with mild baseline CAV and mild progression (n=188 23.1%), iv) patients with mild baseline CAV and accelerated CAV progression (n=106, 13.0%, discrimination 0.92). Six early independent predictors of CAV trajectories were identified: donor age (p Conclusion In a large multicentric and highly phenotyped prospective cohort of heart transplant recipients, we identified 4 robust CAV trajectories and their respective immune and non-immune determinants. Our results provide the basis for a trajectory-based assessment of heart transplant patients for early patient risk stratification and patient monitoring. Factors associated CAV trajectories in multivariate analyses in the derivation cohort. This table shows the association of clinical, immunological, functional and structural parameters associated with CAV trajectories in multivariate multinomial regression analysis. The trajectory of reference was trajectory #1, including patients with no CAV at baseline and stable CAV grade over time. Funding Acknowledgement Type of funding source: None

Background Cardiac allograft vasculopathy (CAV) is a major contributor of heart transplant recipient's mortality. However, the associations between CAV trajectories and mortality remains poorly described. Purpose We aimed to identify the different evolutive profiles of CAV and to determine the respective association with all-cause mortality. Methods Heart transplant recipients receiving care at 4 academic centers were included. Patients underwent prospective, protocol-based monitoring consisting of repeated coronary angiographies together with systematic assessment of clinical, functional, histological and immunological parameters. The mainoutcome was a prediction for CAV trajectories using unsupervised latent class mixed models. We then identified their association with all-cause mortality (NCT04117152). Results Overall, 1,301 patients were included (815 and 486 in the development and validation cohorts, respectively). The median follow-up post-transplant was 6.6 years (IQR=4.7) with 4,710 coronary angiographies analyzed (3.6±1.6 CAV assessments per patient). We identified 4 distinct profiles of CAV trajectories over 10 years that were characterized by i)Patients without CAV at baseline and non-progression (n=823, 63.3%), ii) patients without CAV at baseline and late onset CAV progression (n=79, 6.1%), iii) patients with mild baseline CAV and mild progression (n=261, 20.1), iv) patients with mild baseline CAV and accelerated CAV progression (n=138, 10.6%, discrimination 0.95). The 4 CAV trajectories showed gradient for all-cause mortality (p Conclusion In a large multicentric and highly phenotyped prospective cohort of heart transplant recipients, we identified 4 robust CAV trajectories. These different profiles were associated with distinct prognosis. Our results provide the basis for a trajectory-based assessment of heart transplant patients for early patient risk stratification and patient monitoring. Figure 1. Overall 10-year survival probability according to the CAV trajectory in the overall cohort (n=1,301). The left part represents the main profiles CAV grades identified with latent class mixed models. Thick lines represent latent class trajectory; thin lines represent CAV individual patient trajectory. The right part represent the Kaplan Meier curves of the different trajectories. Funding Acknowledgement Type of funding source: None

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Background Here, we present our initial experience with a prospective protocol of belatacept conversion in patients with chronic active antibody-mediated rejection (caAMR) and a high degree of chronicity at the time of diagnosis. Methods We converted 19 patients (mean age, 45 ± 12 y) with biopsy-proven caAMR from tacrolimus to belatacept at a median of 44 months post-kidney transplant. Results At a median of 29 months (interquartile range, 16-46 mo) postconversion, death-censored graft and patient survivals were 89% and 95%, respectively. When compared to a 1:2 propensity-matched control cohort from the INSERM U970 registry maintained on calcineurin inhibitor, the belatacept group had progressive improvement (P = 0.02) in estimated glomerular filtration rate from a mean of 33.9 ± 10 at baseline to 37.8 ± 13 at 6 months and 38.5 ± 12 mL/min/1.73 m2 at 12 months postconversion, as compared to a steady decline noted in the controls (36.2 [baseline] → 33.1 [6 mo] → 32.7 mL/min/1.73 m2 [12 mo] of follow-up). A paired histologic comparison of preconversion and postconversion (performed at median 9.5 mo postconversion) biopsies showed no worsening in microvascular inflammation or chronicity. The paired tissue gene expression analysis showed improved mean total rejection score (0.68 ± 0.26-0.56 ± 0.33; P = 0.02) and a trend toward improved antibody-mediated rejection score (0.64 ± 0.34-0.56 ± 0.39; P = 0.06). Conclusions Here, we report that in patients diagnosed with caAMR who were not subjected to intensive salvage immunosuppressive therapies, isolated belatacept conversion alone was associated with stabilization in renal function. These results are bolstered by molecular evidence of improved inflammation.

Introduction En transplantation renale, les biopsies pre-implantatoires sont utilisees pour evaluer la qualite de l’organe et determiner les lesions histologiques heritees du donneur de celles acquissent apres la transplantation. Neanmoins, de nombreux centres ne realisent pas ces biopsies qui reste un acte invasif, couteux et chronophage. Description Cette etude vise a developper et valider un outil non invasif de biopsie virtuelle en transplantation renale base sur les caracteristiques du donneur au moment du prelevement. Methodes Les donneurs vivants et decedes preleves avec une biopsie preimplantatoire entre 2000 et 2019 de 17 centres de transplantation renale d’Europe, d’Amerique du Nord et d’Australie ont ete inclus. Les caracteristiques demographiques, cliniques et biologiques du donneur ont ete utilisees pour predire 4 lesions chroniques de la biopsie preimplantatoire : le pourcentage de glomerules scleroses et les scores de BANFF arteriosclerose (cv), hyalinose arteriolaire (ah) et fibrose interstitielle et atrophie tubulaire (FIAT). Resultats Au total, 12 992 biopsies preimplantatoire ont ete incluses et 11 parametres du donneur ont ete utilises : âge, sexe, IMC, creatinine, proteinurie, comorbidites (hypertension, diabetes, hepatite C), type de donneur et cause du deces si donneur decedes. Un metaclassifier regroupant les modeles de random forest, neural network, gradient boosting machine, extreme gradient boosting tree, linear discriminant analysis et naive Bayes a permis de predire avec une AUC de 0,736 le score cv, 0,810 le score ah et 0,793 la FIAT. Le random forest a ete utilise pour predire le pourcentage de glomerules sclereux avec une erreur absolu moyenne de 4,766. L’outil est disponible en ligne ( Fig. 1 ) : https://transplant-prediction-system.shinyapps.io/Virtual_Biopsy_System . Conclusion Cette etude presente le premier outil de biopsie virtuelle non invasive en transplantation renale montrant de bonnes performances pour predire le resultat de la biopsie preimplantatoire a partir de caracteristiques du donneur. Cet outil pourrait aider a mieux contextualiser les lesions chroniques presentes sur les biopsies apres la transplantation.

Introduction La stratification du risque de perte de greffon apres transplantation renale est une tâche de plus en plus difficile du fait de l’augmentation quantitative et de la complexite des donnees a disposition du clinicien. Description Cette etude vise a comparer les performances pour predire la perte du greffon de cliniciens et de l’outil pronostique iBox en transplantation renale. Methodes 400 patients ont ete selectionnes aleatoirement dans la cohorte prospective de 4000 patients transplantes du Paris Transplant Group. Un dossier medical anonyme regroupant 44 parametres disponible a 1 an post-transplantation a ete cree pour chaque patient incluant les donnees du donneur et du receveur ainsi qu’une evaluation a 1 an avec fonction renale, proteinurie, recherche de DSA et une biopsie du greffon. Neuf cliniciens (3 internes, 3 assistants, 3 seniors) ont ete recrutes pour predire la survie du greffon 7 ans apres l’evaluation. Les predictions des cliniciens etaient comparees aux predictions calculees par l’iBox et la survie observee. Des modeles de survival random forest ont ete utilises pour hierarchiser les parametres ayant influences les cliniciens. Resultats Au total, 108 patients ont perdu leur greffon apres un suivi moyen post-evaluation de 7,16 ± 3,61 annees. Les courbes de calibration montrent que l’iBox presentait les meilleures performances predictives, alors que les cliniciens surestimaient le risque ( Fig. 1 ). Les predictions etaient tres heterogenes entre les cliniciens avec une correlation intraclasse de 0,58, et respectivement 0,48 pour les internes, 0,61 pour les assistants et 0,59 pour les seniors. Mis a part la fonction renale, les parametres ayant influences le choix des cliniciens etaient differents (Kappa 0,13) et ceci quel que soit l’experience clinique. Conclusion Cette etude demontre l’heterogeneite de la stratification du risque de perte du greffon par les cliniciens en transplantation renale. L’outil iBox pourrait aider les cliniciens a mieux apprecier le risque de perte de greffon apres transplantation.

Background Heart transplant recipients are at high-risk of sudden cardiac death (SCD). However, risk factors of SCD in heart recipients remained poorly described. Objective To assess the predictors of SCD beyond the first-year post-transplant. Methods We enrolled consecutive patients transplanted between 2004 and 2017 in two French referral centers. We excluded patients deceased during the first year. Patients underwent an evaluation at the day of transplantation and during the first year, comprising clinical, biological, histological, immunological (circulating anti-HLA DSA) and interventional (cardiac allograft vasculopathy assessment) parameters. Echocardiographies were routinely performed in all included patients according to a prespecified protocol. According to the last consensus, SCD was defined as an unexpected out-of-hospital cardiac arrest without obvious non-cardiac cause, in the first hour after initiation of symptoms. Cox model analysis was used to determine the parameters associated with sudden death risk. Results A total of 913 patients were included. The median follow-up post-HT was 5.9 years (IQR=2.9–8.5). Among the 213 deaths after one year, 44 patients (21%) died from SCD. In this population, the incidence rate of SCD was 0,82 per 100 person-year (95% CI: 0,51–2,05). Among the 60 parameters tested in univariate analysis, we identified 2 independent factors of sudden death after 1 year post-HT: left ventricular ejection fraction (LVEF) ≤55% any time after transplantation ( HR 4.07, 95% CI 1.94–8.53, p Conclusion In a large multicentric and highly phenotyped cohort of heart transplant recipients, we identified two independent factors associated with SCD beyond the first year. This study provides fresh evidence of SCD assessment for improving risk stratification of HT recipients. Funding Acknowledgement Type of funding source: None

Approximately 3500 donated kidneys are discarded in the United States each year, drawing concern from Medicare and advocacy groups.To estimate the effects of more aggressive allograft acceptance practices on the donor pool and allograft survival for the population of US wait-listed kidney transplant candidates.A nationwide study using validated registries from the United States and France comprising comprehensive cohorts of deceased donors with organs offered to kidney transplant centers between January 1, 2004, and December 31, 2014. Data were analyzed between September 1, 2018, and April 5, 2019.The primary outcome was kidney allograft discard. The secondary outcome was allograft failure after transplantation. We used logistic regression to model organ acceptance and discard practices in both countries. We then quantified using computer simulation models the number of kidneys discarded in the United States that a more aggressive system would have instead used for transplantation. Finally, based on actual survival data, we quantified the additional years of allograft life that a redesigned US system would have saved.In the United States, 156 089 kidneys were recovered from deceased donors between 2004 and 2014, of which 128 102 were transplanted, and 27 987 (17.9%) were discarded. In France, among the 29 984 kidneys recovered between 2004 and 2014, 27 252 were transplanted, and 2732 (9.1%, P .001 vs United States) were discarded. The mean (SD) age of kidneys transplanted in the United States was 36.51 (17.02) years vs 50.91 (17.34) years in France (P .001). Kidney quality showed little change in the United States over time (mean [SD] kidney donor risk index [KDRI], 1.30 [0.48] in 2004 vs 1.32 [0.46] in 2014), whereas a steadily rising KDRI in France reflected a temporal trend of more aggressive organ use (mean [SD] KDRI, 1.37 [0.47] in 2004 vs 1.74 [0.72] in 2014; P .001). We applied the French-based allocation model to the population of US deceased donor kidneys and found that 17 435 (62%) of kidneys discarded in the United States would have instead been transplanted under the French system. We further determined that a redesigned system with more aggressive organ acceptance practices would generate an additional 132 445 allograft life-years in the United States over the 10-year observation period.Greater acceptance of kidneys from deceased donors who are older and have more comorbidities could provide major survival benefits to the population of US wait-listed patients.ClinicalTrials.gov identifier: NCT03723668.

Background Sudden cardiac death (SCD) is a major contributor to the rate of mortality after heart transplantation. However, little is known about the incidence of SCD in heart recipients. Objective To assess the incidence of SCD after heart transplantation. Methods We enrolled consecutive patients transplanted between 2004 and 2017 in two French referral centers. We defined 7 main groups of causes of deaths: SCD, cardiovascular (including Cardiac allograft vasculopathy), infection, primary graft dysfunction, graft failure (including late graft dysfunction, rejection), malignancy and others. Causes of deaths were independently adjudicated by two senior cardiologists based on the analysis of death certificates and medical records. Discrepancies were resolved by discussion until a consensus was made. SCD was defined as an unexpected out-of-hospital cardiac arrest without obvious non-cardiac cause, in the first hour after initiation of symptoms. Results A total of 1,363 patients were included. The median follow-up post-transplant was 3.99 years (IQR = 0.49–7.49). A total of 450 patients (33%) deceased during the first year. The leading cumulative causes of death in the first year after transplantation were infection, primary graft failure, multiple organ failure during the period in intensive car unit. Beyond the post-operative high-risk period of the first year, the leading cumulative cause of death was SCD: among the 213 deaths that occurred beyond the first year, 44 patients (21%) died from SCD. In this period, the incidence rate of SCD reached 0,82 per 100 person-year (95% CI: 0.51–2.05) ( Fig. 1 ). Conclusion In a large multicentric and highly phenotyped cohort of heart transplant recipients, the leading cumulative cause of death beyond the first-year post transplant was sudden cardiac death. Our results open discussion on management of heart recipient, such as the implementation of cardioverter-defibrillators.

Purpose To identify distinct profiles of cardiac allograft vasculopathy (CAV) trajectory and their determinants. Methods We performed a multicenter study (Pitie & HEGP, Paris). We included all patients transplanted between 2004 and 2011 who had at least 2 coronary angiograms during follow-up (n = 436). We performed an comprehensive evaluation of potential risk factors that might be associated with CAV including in-depth characterization of donor-specific antibodies (DSA) and retrospective endomyocardial biopsies reviewing. A semi parametric mixture model was used to identify distinct trajectories of CAV progression from year 1 to year 10 post-transplantation among the 1407 coronary angiograms (3.4±1.4 angiograms per patient). Multivariable logistic regression model was used to determine risk factors associated with each trajectory. Results Median follow-up post-transplant was 7 years. Four latent classes of CAV trajectories were identified: #1-non-progressors (n=240, 55%), #2- late progressors (n=35, 8%), #3- early progressors (n=99, 22.7%) and #4- severe progressors (n = 62, 14.3%, figure). Taking latent class #1 as reference, 5 parameters were associated with CAV progression: donor age (HR = 1.06 and 1.08 per 1-yr increment for latent classes #3 and #4, respectively, p Conclusion In a large and highly phenotyped cohort of heart transplant recipients, we identified 4 distinct prototypes of CAV trajectories. Donor-derived cardiovascular risk factors, 1-year post-transplant cardiovascular profile (dyslipidemia) and immunological parameters (pre-formed DSA) were independent risk factors for CAV severity and progression.

States that international companies must consider the cultural differences between countries or risk serious and expensive problems. Uses real‐life examples to examine mis‐understanding and failed communication in global business. Describes how such problems can be identified and anticipated. Observes that many companies now accept the need for intercultural management as a fact of life.