Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study

A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation

BACKGROUND

After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms.

METHODS

Unsensitized recipients who received their first KTx deceased donor between January 1, 2010, and December 31, 2019, were classified according to the conduct of renal function after transplantation. Variables related to the donor, recipient, kidney preservation, and immunology were used. The patients were randomly divided into 2 groups: 70% were assigned to the training and 30% to the test group. Popular machine learning algorithms were used: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Gradient Boosting classifier, Logistic Regression, CatBoost classifier, AdaBoost classifier, and Random Forest classifier. Comparative performance analysis on the test dataset was performed using the results of the AUC values, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score.

RESULTS

Of the 859 patients, 21.7% (n = 186) had IGF. The best predictive performance resulted from the eXtreme Gradient Boosting model (AUC, 0.78; 95% CI, 0.71-0.84; sensitivity, 0.64; specificity, 0.78). Five variables with the highest predictive value were identified.

CONCLUSIONS

Our results indicated the possibility of creating a model for the prediction of IGF, enhancing the selection of patients who would benefit from an expensive treatment, as in the case of machine perfusion preservation.

Artificial intelligence, machine learning, and deep learning in liver transplantation

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Race-free estimated glomerular filtration rate equation in kidney transplant recipients: development and validation study

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 P30 (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/1.73m2 (SD 17.23) in the development cohort and 55.90 mL/min/1.73m2 (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 (P30 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 (P30 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.

Kidney transplant in the next decade: Strategies, challenges and vision of the future

Although the results of kidney transplantation (KT) have improved substantially in recent years, a chronic and inexorable loss of grafts mainly due to the death of the patient and chronic dysfunction of the KT, continues to be observed. The objectives, thus, to optimize this situation in the next decade are fundamentally focused on minimizing the rate of kidney graft loss, improving patient survival, increasing the rate of organ procurement and its distribution, promoting research and training in health professionals and the development of scientific registries providing clinical and reliable information that allow us to optimize our clinical practice in the field of KT. With this perspective, this review will deep into: (1) strategies to avoid chronic dysfunction and graft loss in the medium and long term; (2) to prolong patient survival; (3) strategies to increase the donation, maintenance and allocation of organs; (4) promote clinical and basic research and training activity in KT; and (5) the analysis of the results in KT by optimizing and merging scientific registries.

New insights into maladaptive vascular responses to donor specific HLA antibodies in organ transplantation

Transplant vasculopathy (TV) causes thickening of donor blood vessels in transplanted organs, and is a significant cause of graft loss and mortality in allograft recipients. It is known that patients with repeated acute rejection and/or donor specific antibodies are predisposed to TV. Nevertheless, the exact molecular mechanisms by which alloimmune injury culminates in this disease have not been fully delineated. As a result of this incomplete knowledge, there is currently a lack of effective therapies for this disease. The immediate intracellular signaling and the acute effects elicited by anti-donor HLA antibodies are well-described and continuing to be revealed in deeper detail. Further, advances in rejection diagnostics, including intragraft gene expression, provide clues to the inflammatory changes within allografts. However, mechanisms linking these events with long-term outcomes, particularly the maladaptive vascular remodeling seen in transplant vasculopathy, are still being delineated. New evidence demonstrates alterations in non-coding RNA profiles and the occurrence of endothelial to mesenchymal transition (EndMT) during acute antibody-mediated graft injury. EndMT is also readily apparent in numerous settings of non-transplant intimal hyperplasia, and lessons can be learned from advances in those fields. This review will provide an update on these recent developments and remaining questions in our understanding of HLA antibody-induced vascular damage, framed within a broader consideration of manifestations and implications across transplanted organ types.

Allograft tissue under the microscope: only the beginning

PURPOSE OF REVIEW

To review novel modalities for interrogating a kidney allograft biopsy to complement the current Banff schema.

RECENT FINDINGS

Newer approaches of Artificial Intelligence (AI), Machine Learning (ML), digital pathology including Ex Vivo Microscopy, evaluation of the biopsy gene expression using bulk, single cell, and spatial transcriptomics and spatial proteomics are now available for tissue interrogation.

SUMMARY

Banff Schema of classification of allograft histology has standardized reporting of tissue pathology internationally greatly impacting clinical care and research. Inherent sampling error of biopsies, and lack of automated morphometric analysis with ordinal outputs limit its performance in prognostication of allograft health. Over the last decade, there has been an explosion of newer methods of evaluation of allograft tissue under the microscope. Digital pathology along with the application of AI and ML algorithms could revolutionize histopathological analyses. Novel molecular diagnostics such as spatially resolved single cell transcriptomics are identifying newer mechanisms underlying the pathologic diagnosis to delineate pathways of immunological activation, tissue injury, repair, and regeneration in allograft tissues. While these techniques are the future of tissue analysis, costs and complex logistics currently limit their clinical use.

Non-Traditional Non-Immunological Risk Factors for Kidney Allograft Loss-Opinion

Rates of late allograft loss have improved slowly in the last decades. Well described traditional risk factors that influence allograft survival include cardiovascular events, rejection, infections and post-transplant neoplasia. Here, we critically evaluate the influence of several non-immunological, non-traditional risk factors and describe their impact on allograft survival and cardiovascular health of kidney transplant recipients. We assessed the following risk factors: arterial stiffness, persistent arteriovenous access, mineral bone disease, immunosuppressive drugs residual levels variability, hypomagnesemia, glomerular pathological alterations not included in Banff criteria, persistent inflammation and metabolic acidosis.

Practical application of the French two-score heart allocation scheme: Insights from a high-volume heart transplantation centre

BACKGROUND

In 2018, a cardiac allocation scheme based on an individual score considering the risk of death both on the waitlist and after heart transplantation was implemented in France.

AIMS

To analyse the practical application of the pre- and post-transplant risk score in a French high-volume heart transplantation centre.

METHODS

All consecutive adult patients listed for a first non-combined heart transplantation between 02 January 2018 and 30 June 2022 at our centre were included. Baseline characteristics of candidates and recipients were retrieved from the national CRISTAL registry. Both scores were calculated at listing and at transplant.

RESULTS

Overall, 364 patients were included. During follow-up, 257 patients (70.6%) were transplanted, and 57 (15.6%) died or were removed from the waitlist. Post-transplant 3-month survival was 84.8%. Total bilirubin and natriuretic peptides had the most important weight in the pretransplant risk score. This score had a major impact on waitlist outcomes: quartile 1 was characterized by low access to heart transplantation (58.2%) and risk of death on the waitlist (9.9%) compared with quartile 4 (heart transplantation rate 74.1%, mortality on the waiting list>20%). According to the post-transplant risk score, a minimal number of candidates were considered ineligible for heart transplantation (<1%), but 12.4% were contraindicated to at least one donor category. The number of contraindicated donor categories had a significant impact on waitlist outcomes. Although adequately calibrated, the post-transplant score had a limited discrimination (area under the curve 0.65, 95% confidence interval 0.59-0.71).

CONCLUSION

Our results highlight the major impact of pre- and post-transplantation risk scores on waitlist outcomes following the allocation scheme update.

The IMBG Test for Evaluating the Pharmacodynamic Response to Immunosuppressive Therapy in Kidney Transplant Patients: Current Evidence and Future Applications

Immunosuppressive drugs are widely used to prevent rejection after kidney transplantation. However, the pharmacological response to a given immunosuppressant can vary markedly between individuals, with some showing poor treatment responses and/or experiencing serious side effects. There is an unmet need for diagnostic tools that allow clinicians to individually tailor immunosuppressive therapy to a patient’s immunological profile. The Immunobiogram (IMBG) is a novel blood-based in vitro diagnostic test that provides a pharmacodynamic readout of the immune response of individual patients to a range of immunosuppressants commonly used in kidney transplant recipients. Here, we discuss the current approaches used to measure the pharmacodynamic responses of individual patients to specific immunosuppressive drugs in vitro, which can then be correlated with patient’s clinical outcomes. We also describe the procedure of the IMBG assay, and summarize the results obtained using the IMBG in different kidney transplant populations. Finally, we outline future directions and other novel applications of the IMBG, both in kidney transplant patients and other autoimmune diseases.

Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol

BACKGROUND

Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland.

METHODS

The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis.

DISCUSSION

Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration.

STUDY REGISTRATION

Open Science Framework ID: z6mvj.

A viewpoint describing the American Society of Transplantation rationale to conduct a comprehensive patient survey assessing unmet immunosuppressive therapy needs

This viewpoint aims to "set the stage" and provide the rationale for the proposed development of a large-scale, comprehensive survey assessing transplant patients' perceived unmet immunosuppressive therapy needs. Research in organ transplantation has historically focused on reducing the incidence and impact of rejection on allograft survival and minimizing or eliminating the need for chronic immunosuppressive therapies. There has been less emphasis and investment in therapies to improve patient-reported outcomes including health-related quality of life and side-effects. Patient-focused drug development (PFDD) is a new and important emphasis of the Food and Drug Administration (FDA) that provides a guiding philosophy for incorporating the patient experience into drug development and evaluation. The American Society of Transplantation (AST) Board of Directors commissioned this working group to prepare for the conduct of a comprehensive patient survey assessing unmet immunosuppressive therapy needs. This paper aims to describe the basis for why it is important to conduct this survey and briefly outline the plan for broad stakeholder engagement to ensure the information gained is diverse, inclusive, and relevant for advancing PFDD in organ transplant recipients.

Association Between Dietary Quality and Serum Cystatin C in Kidney Transplant Recipients Based on Chinese Diet Balance Index 2016 (DBI-16)

BACKGROUND Cystatin C (Cys) is considered to be a better marker than serum creatinine in assessing kidney function, predicting cardiovascular events, and all-cause mortality. It seems to be associated with nutritional status in the general population, but little is known about kidney transplant recipients (KTRs). This study aimed to explore the relationship between dietary balance index and serum Cys in KTRs. MATERIAL AND METHODS In a cross-sectional study, 215 KTRs completed an FFQ questionnaire and information on serum Cys. Dietary intake was assessed using the Food Frequency Questionnaire (FFQ). Dietary Balance Index 2016 (DBI-16) edition scores were calculated as an indicator of dietary quality. Data on the patient's serum Cys were obtained through the hospital information system. RESULTS The majority of KTRs were male (75.34%), 76.74% were aged 18-44 years, and 79.53% were abnormal serum Cys. Dairy (z=-2.161, P<0.05), meat (z=-2.578, P<0.05), and dietary diversity (z=-3.393, P<0.05) in the normal group were higher than those in the abnormal group, and the dietary quality distance (DQD) score (t=-2.264, P<0.05) was lower than that in the abnormal group. After adjusting for confounders, a low-quality diet was a risk factor for maintaining the normal level of serum Cys (OR 3.022, 95% CI 1.263-7.231, P<0.05). CONCLUSIONS The present study suggested that KTRs with a high dietary quality might be associated with normal serum Cys levels. Dairy, meat, and varied diet seems to impact the serum Cys levels of KTRs. Dietary imbalances were prevalent among KTRs.

Multiparametric renal magnetic resonance imaging: A reproducibility study in renal allografts with stable function

Monitoring renal allograft function after transplantation is key for the early detection of allograft impairment, which in turn can contribute to preventing the loss of the allograft. Multiparametric renal MRI (mpMRI) is a promising noninvasive technique to assess and characterize renal physiopathology; however, few studies have employed mpMRI in renal allografts with stable function (maintained function over a long time period). The purposes of the current study were to evaluate the reproducibility of mpMRI in transplant patients and to characterize normal values of the measured parameters, and to estimate the labeling efficiency of Pseudo-Continuous Arterial Spin Labeling (PCASL) in the infrarenal aorta using numerical simulations considering experimental measurements of aortic blood flow profiles. The subjects were 20 transplant patients with stable kidney function, maintained over 1 year. The MRI protocol consisted of PCASL, intravoxel incoherent motion, and T1 inversion recovery. Phase contrast was used to measure aortic blood flow. Renal blood flow (RBF), diffusion coefficient (D), pseudo-diffusion coefficient (D*), flowing fraction ( f ), and T1 maps were calculated and mean values were measured in the cortex and medulla. The labeling efficiency of PCASL was estimated from simulation of Bloch equations. Reproducibility was assessed with the within-subject coefficient of variation, intraclass correlation coefficient, and Bland-Altman analysis. Correlations were evaluated using the Pearson correlation coefficient. The significance level was p less than 0.05. Cortical reproducibility was very good for T1, D, and RBF, moderate for f , and low for D*, while medullary reproducibility was good for T1 and D. Significant correlations in the cortex between RBF and f (r = 0.66), RBF and eGFR (r = 0.64), and D* and eGFR (r = -0.57) were found. Normal values of the measured parameters employing the mpMRI protocol in kidney transplant patients with stable function were characterized and the results showed good reproducibility of the techniques.

Are protocol graft biopsies after pediatric liver transplantation useful? Experience in a single center over 20 years

BACKGROUND

The role of protocol liver biopsies (PLB) in the follow-up of pediatric liver transplant recipients remains questionable. This single-center retrospective study aimed to evaluate their clinical impact on the long-term management of pediatric liver transplant recipients.

METHODS

We described histopathological lesions and clinical consequences for patient management of PLB performed 1, 5, 10, 15, 20, and 25 years after pediatric liver transplantation (LT).

RESULTS

A total of 351 PLB performed on 133 patients between 1992 and 2021 were reviewed. PLB found signs of rejection in 21.7% of cases (76/351), and moderate to severe fibrosis in 26.5% of cases (93/351). Overall, 264 PLB (75.2%) did not cause any changes to patient care. Immunosuppression was enhanced after 63 PLB, including 23 cases of occult rejection. The 1-year PLB triggered significantly more changes, while biopsies at 15, 20, and 25 years produced the lowest rates of subsequent modifications. PLB had a significantly higher probability of inducing therapeutic changes if the patient had abnormal biological or imaging results (odds ratio [OR] 2.82 and 2.06), or a recent history of rejection or bacterial infection (OR 2.22 and 2.03).

CONCLUSION

Our results suggest that, although it often does not prompt any treatment changes, PLB could be performed because of its ability to detect silent rejection requiring an increase in immunosuppression. PLB could be carried out 1, 5, and 10 years after LT and then every 10 years in patients with normal biological and imaging results and no recent complications, while other patients could be kept on a 5-year protocol.

A Banff-based histologic chronicity index is associated with graft loss in patients with a kidney transplant and antibody-mediated rejection

Antibody-mediated rejection (AMR) is the major cause of graft loss in kidney transplant recipients. The Banff classification defines two classes of AMR, active and chronic active but over time this classification has become increasingly complex. To simplify the approach to AMR, we developed activity and chronicity indices based on kidney transplant biopsy findings and examined their association with graft survival in 147 patients with active or chronic active AMR, all of whom had donor-specific antibodies and were treated for AMR. The activity index was determined as the sum of Banff glomerulitis (g), peritubular capillaritis (ptc), arteritis (v) and C4d scores, with a maximum score of 12. The chronicity index was the sum of interstitial fibrosis (ci), tubular atrophy (ct), chronic vasculopathy (cv), and chronic glomerulopathy (cg) scores, the latter doubled, with a maximum score of 15. While the activity index was generally not associated with graft loss, the chronicity index was significantly associated with graft loss with an optimal threshold value of 4 or greater for predicting graft loss. The association of the chronicity index of 4 or greater with graft loss was independent of other parameters associated with graft loss, including the estimated glomerular filtration rate at the time of biopsy, chronic active (versus active) AMR, AMR with de novo (versus persistent/rebound) donor-specific antibodies, Banff (g+ptc) scores, concurrent T cell-mediated rejection and donor-specific antibody reduction post-biopsy. The association of the chronicity index of 4 or greater with graft loss was confirmed in an independent cohort of 61 patients from Necker Hospital, Paris. Thus, our findings suggest that the chronicity index may be valuable as a simplified approach to decision-making in patients with AMR.

Infliximab Induction Lacks Efficacy and Increases BK Virus Infection in Deceased Donor Kidney Transplant Recipients: Results of the CTOT-19 Trial

BACKGROUND

Ischemia-reperfusion (IR) of a kidney transplant (KTx) upregulates TNF α production that amplifies allograft inflammation and may negatively affect transplant outcomes.

METHODS

We tested the effects of blocking TNF peri-KTx via a randomized, double-blind, placebo-controlled, 15-center, phase 2 clinical trial. A total of 225 primary transplant recipients of deceased-donor kidneys (KTx; 38.2% Black/African American, 44% White) were randomized to receive intravenous infliximab (IFX) 3 mg/kg or saline placebo (PLBO) initiated before kidney reperfusion. All patients received rabbit anti-thymocyte globulin induction and maintenance immunosuppression (IS) with tacrolimus, mycophenolate mofetil, and prednisone. The primary end point was the difference between groups in mean 24-month eGFR.

RESULTS

There was no difference in the primary end point of 24-month eGFR between IFX (52.45 ml/min per 1.73 m 2 ; 95% CI, 48.38 to 56.52) versus PLBO (57.35 ml/min per 1.73 m 2 ; 95% CI, 53.18 to 61.52; P =0.1). There were no significant differences between groups in rates of delayed graft function, biopsy-proven acute rejection (BPAR), development of de novo donor-specific antibodies, or graft loss/death. Immunosuppression did not differ, and day 7 post-KTx plasma analyses showed approximately ten-fold lower TNF ( P <0.001) in IFX versus PLBO. BK viremia requiring IS change occurred more frequently in IFX (28.9%) versus PLBO (13.4%; P =0.004), with a strong trend toward higher rates of BKV nephropathy in IFX (13.3%) versus PLBO (4.9%; P =0.06).

CONCLUSIONS

IFX induction therapy does not benefit recipients of kidney transplants from deceased donors on this IS regimen. Because the intervention unexpectedly increased rates of BK virus infections, our findings underscore the complexities of targeting peritransplant inflammation as a strategy to improve KTx outcomes.Clinical Trial registry name and registration number:clinicaltrials.gov (NCT02495077).

Pretransplant serum levels of endothelial cell activation markers are associated with graft loss and mortality after kidney transplantation

Long-term allograft survival remains a challenge in kidney transplantation. In this study, we aimed to identify biomarkers for potentially modifiable pathways involved in the outcome of kidney transplantation. We tested the hypothesis that a pre-existing systemic environment with endothelial cell activation in the recipient is associated with the outcome after kidney transplantation. In a retrospective study cohort of 611 kidney transplanted patients, we investigated associations between serum levels of soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1) before transplantation and delayed graft function, acute rejection, graft loss and mortality after transplantation. We adjusted associations for age, sex, preformed donor-specific antibodies (DSA), pretransplant diabetes, cardiovascular disease and dialysis. Additionally, we investigated if associations between endothelial cell activation markers and outcomes differed in recipients with and without preformed DSA. Serum levels of endothelial cell activation markers were associated with delayed graft function and mortality but not with rejection. Additionally, high levels of sICAM-1 were associated with graft loss. Associations were most pronounced in recipients without DSA, adjusted for potential confounders. Data suggest that endothelial cell activation at the time of transplantation is associated with graft loss and mortality after kidney transplantation, especially in transplant candidates without preformed DSA.

Clinical recommendations for posttransplant assessment of anti-HLA (Human Leukocyte Antigen) donor-specific antibodies: A Sensitization in Transplantation: Assessment of Risk consensus document

Although anti-HLA (Human Leukocyte Antigen) donor-specific antibodies (DSAs) are commonly measured in clinical practice and their relationship with transplant outcome is well established, clinical recommendations for anti-HLA antibody assessment are sparse. Supported by a careful and critical review of the current literature performed by the Sensitization in Transplantation: Assessment of Risk 2022 working group, this consensus report provides clinical practice recommendations in kidney, heart, lung, and liver transplantation based on expert assessment of quality and strength of evidence. The recommendations address 3 major clinical problems in transplantation and include guidance regarding posttransplant DSA assessment and application to diagnostics, prognostics, and therapeutics: (1) the clinical implications of positive posttransplant DSA detection according to DSA status (ie, preformed or de novo), (2) the relevance of posttransplant DSA assessment for precision diagnosis of antibody-mediated rejection and for treatment management, and (3) the relevance of posttransplant DSA for allograft prognosis and risk stratification. This consensus report also highlights gaps in current knowledge and provides directions for clinical investigations and trials in the future that will further refine the clinical utility of posttransplant DSA assessment, leading to improved transplant management and patient care.

Diagnostic Potential of Minimally Invasive Biomarkers: A Biopsy-centered Viewpoint From the Banff Minimally Invasive Diagnostics Working Group

With recent advances and commercial implementation of minimally invasive biomarkers in kidney transplantation, new strategies for the surveillance of allograft health are emerging. Blood and urine-based biomarkers can be used to detect the presence of rejection, but their applicability as diagnostic tests has not been studied. A Banff working group was recently formed to consider the potential of minimally invasive biomarkers for integration into the Banff classification for kidney allograft pathology. We review the existing data on donor-derived cell-free DNA, blood and urine transcriptomics, urinary protein chemokines, and next-generation diagnostics and conclude that the available data do not support their use as stand-alone diagnostic tests at this point. Future studies assessing their ability to distinguish complex phenotypes, differentiate T cell-mediated rejection from antibody-mediated rejection, and function as an adjunct to histology are needed to elevate these minimally invasive biomarkers from surveillance tests to diagnostic tests.

Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning

Background

Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods.

Methods

Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR.

Results

The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively.

Conclusions

This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.

Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation

With its robust ability to integrate and learn from large sets of clinical data, artificial intelligence (AI) can now play a role in diagnosis, clinical decision making, and personalized medicine. It is probably the natural progression of traditional statistical techniques. Currently, there are many unmet needs in nephrology and, more particularly, in the kidney transplantation (KT) field. The complexity and increase in the amount of data, and the multitude of nephrology registries worldwide have enabled the explosive use of AI within the field. Nephrologists in many countries are already at the center of experiments and advances in this cutting-edge technology and our aim is to generalize the use of AI among nephrologists worldwide. In this paper, we provide an overview of AI from a medical perspective. We cover the core concepts of AI relevant to the practicing nephrologist in a consistent and simple way to help them get started, and we discuss the technical challenges. Finally, we focus on the KT field: the unmet needs and the potential role that AI can play to fill these gaps, then we summarize the published KT-related studies, including predictive factors used in each study, which will allow researchers to quickly focus on the most relevant issues.

How to improve results after DCD (donation after circulation death)

The shortage of organs for transplantation has led health professionals to look for alternative sources of donors. One of the avenues concerns donors who have died after circulatory arrest. This is a special situation because the organs from these donors are exposed to warm ischaemia-reperfusion lesions that are unavoidable during the journey of the organs from the donor to the moment of transplantation in the recipient. We will address and discuss the key issues from the perspective of team organization, legislation and its evolution, and the ethical framework. In a second part, the avenues to improve the quality of organs will be presented following the itinerary of the organs between the donor and the recipient. The important moments from the point of view of therapeutic strategy will be put into perspective. New connections between key players involved in pathophysiological mechanisms and implications for innate immunity and injury processes are among the avenues to explore. Technological developments to improve the quality of organs from these recipients will be analyzed, such as perfusion techniques with new modalities of temperatures and oxygenation. New molecules are being investigated for their potential role in protecting these organs and an analysis of potential prospects will be proposed. Finally, the important perspectives that seem to be favored will be discussed in order to reposition the use of deceased donors after circulatory arrest. The use of these organs has become a routine procedure and improving their quality and providing the means for their evaluation is absolutely inevitable.

Case Report: Long-term observations from the tacrolimus weaning randomized clinical trial depicts the challenging aspects for determination of low-immunological risk patients

Whilst calcineurin inhibitors (CNI) are the cornerstone of immunosuppressive maintenance therapy in kidney transplantation, several studies have investigated the safety of CNI withdrawal in order to avoid their numerous side effects. In this context, we performed several years ago a clinical randomized trial evaluating CNI weaning in stable kidney transplant recipients without anti-HLA immunization. The trial was interrupted prematurely due to a high number of de novo DSA (dnDSA) and biopsy proven acute rejection (BPAR) in patients who underwent tacrolimus weaning, resulting in treatment for rejection and resumption of tacrolimus. We report here the long-term outcomes of patients included in this clinical trial. Ten years after randomization, all patients are alive with a functional allograft. They all receive tacrolimus therapy except one with recurrent cutaneous neoplasia issues. Long-term eGFR was comparable between patients of the two randomized groups (46.4 ml/min vs 42.8 ml/min). All dnDSA that occurred during the study period became non-detectable and all rejections episodes were reversed. The retrospective assessment of HLA DQ single molecule epitope mismatching determined that a majority of patients who developed dnDSA after tacrolimus withdrawal would have been considered at high immunological risk. Minimization of immunosuppression remains a challenging objective, mainly because of the issues to properly select very low immunological risk patients. Valuable improvements have been made the last decade regarding evaluation of the allograft rejection notably through the determination of numerous at-risk biomarkers. However, even if the impact of such tools still need to be clarify in clinical routine, they may permit an improvement in patients' selection for immunosuppression minimization without increasing the risk of allograft rejection.

Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Individual dynamic prediction and prognostic analysis for long-term allograft survival after kidney transplantation

Background

Predicting allograft survival is vital for efficient transplant success. With dynamic changes in patient conditions, clinical indicators may change longitudinally, and doctors’ judgments may be highly variable. It is necessary to establish a dynamic model to precisely predict the individual risk/survival of new allografts.

Methods

The follow-up data of 407 patients were obtained from a renal allograft failure study. We introduced a landmarking-based dynamic Cox model that incorporated baseline values (age at transplantation, sex, weight) and longitudinal changes (glomerular filtration rate, proteinuria, hematocrit). Model performance was evaluated using Harrell’s C-index and the Brier score.

Results

Six predictors were included in our analysis. The Kaplan–Meier estimates of survival at baseline showed an overall 5-year survival rate of 87.2%. The dynamic Cox model showed the individual survival prediction with more accuracy at different time points (for the 5-year survival prediction, the C-index = 0.789 and Brier score = 0.065 for the average of all time points) than the static Cox model at baseline (C-index = 0.558, Brier score = 0.095). Longitudinal covariate prognostic analysis (with time-varying effects) was performed.

Conclusions

The dynamic Cox model can utilize clinical follow-up data, including longitudinal patient information. Dynamic prediction and prognostic analysis can be used to provide evidence and a reference to better guide clinical decision-making for applying early treatment to patients at high risk.

Data-Driven Chronic Allograft Phenotypes: A Novel and Validated Complement for Histologic Assessment of Kidney Transplant Biopsies

BACKGROUND

No validated system currently exists to realistically characterize the chronic pathology of kidney transplants that represents the dynamic disease process and spectrum of disease severity. We sought to develop and validate a tool to describe chronicity and severity of renal allograft disease and integrate it with the evaluation of disease activity.

METHODS

The training cohort included 3549 kidney transplant biopsies from an observational cohort of 937 recipients. We reweighted the chronic histologic lesions according to their time-dependent association with graft failure, and performed consensus k-means clustering analysis. Total chronicity was calculated as the sum of the weighted chronic lesion scores, scaled to the unit interval.

RESULTS

We identified four chronic clusters associated with graft outcome, based on the proportion of ambiguous clustering. The two clusters with the worst survival outcome were determined by interstitial fibrosis and tubular atrophy (IFTA) and by transplant glomerulopathy. The chronic clusters partially overlapped with the existing Banff IFTA classification (adjusted Rand index, 0.35) and were distributed independently of the acute lesions. Total chronicity strongly associated with graft failure (hazard ratio [HR], 8.33; 95% confidence interval [CI], 5.94 to 10.88; P<0.001), independent of the total activity scores (HR, 5.01; 95% CI, 2.83 to 7.00; P<0.001). These results were validated on an external cohort of 4031 biopsies from 2054 kidney transplant recipients.

CONCLUSIONS

The evaluation of total chronicity provides information on kidney transplant pathology that complements the estimation of disease activity from acute lesion scores. Use of the data-driven algorithm used in this study, called RejectClass, may provide a holistic and quantitative assessment of kidney transplant injury phenotypes and severity.

Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation

Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk constellations. Many machine learning models have been developed for detecting patient outcomes in the past years. However, performance metrics alone do not determine practical utility. We present a newly developed clinical decision support system (CDSS) for detection of patients at risk for rejection and death-censored graft failure. The CDSS is based on clinical routine data including 1,516 kidney transplant recipients and more than 100,000 data points. In a reader study we compare the performance of physicians at a nephrology department with and without the CDSS. Internal validation shows AUC-ROC scores of 0.83 for rejection, and 0.95 for graft failure. The reader study shows that predictions by physicians converge toward the CDSS. However, performance does not improve (AUC-ROC; 0.6413 vs. 0.6314 for rejection; 0.8072 vs. 0.7778 for graft failure). Finally, the study shows that the CDSS detects partially different patients at risk compared to physicians. This indicates that the combination of both, medical professionals and a CDSS might help detect more patients at risk for graft failure. However, the question of how to integrate such a system efficiently into clinical practice remains open.

Progression of histological lesions after ABO incompatible kidney transplantation

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%, p<0.0001, patients with a ct >0 raised from 29% to 78%, p<0.0001) was observed in ABOi LDKT without pDSAs. Histological patterns of evolution were comparable to those observed in ABOc kidney transplant patients. Microvascular inflammation was lower in ABOi LDKT without pDSAs compared to those with pDSAs (ABOi or ABOc). At last follow-up, 28 months, IQR (15-48) post-transplantation, 29 patients (36%) had a severe graft dysfunction (defined by a CKD-epi eGFR < 30 mL/min/1.73m²). The donor age was a predictive factor for the development of severe kidney allograft dysfunction at last follow-up (HR= 1.05, 95% CI [1.05-1.10], p= 0.03).

Hence, 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.

DSA in solid organ transplantation: is it a matter of specificity, amount, or functional characteristics?

PURPOSE OF REVIEW

The present review describes the clinical relevance of human leukocyte antigen (HLA) donor-specific antibodies (HLA-DSAs) as biomarkers of alloimmunity and summarizes recent improvements in their characterization that provide insights into immune risk assessment, precision diagnosis, and prognostication in transplantation.

RECENT FINDINGS

Recent studies have addressed the clinical utility of HLA-DSAs as biomarkers for immune risk assessment in pretransplant and peritransplant, diagnosis and treatment evaluation of antibody-mediated rejection, immune monitoring posttransplant, and risk stratification.

SUMMARY

HLA-DSAs have proved to be the most advanced immune biomarkers in solid organ transplantation in terms of analytical validity, clinical validity and clinical utility. Recent studies are integrating multiple HLA-DSA characteristics including antibody specificity, HLA class, quantity, immunoglobulin G subclass, and complement-binding capacity to improve risk assessment peritransplant, diagnosis and treatment evaluation of antibody-mediated rejection, immune monitoring posttransplant, and transplant prognosis evaluation. In addition, integration of HLA-DSAs to clinical, functional and histological transplant parameters has further consolidated the utility of HLA-DSAs as robust biomarkers and allows to build new tools for monitoring, precision diagnosis, and risk stratification for individual patients. However, prospective and randomized-controlled studies addressing the clinical benefit and cost-effectiveness of HLA-DSA-based monitoring and patient management strategies are required to demonstrate that the use of HLA-DSAs as biomarkers can improve current clinical practice and transplant outcomes.

Antibody-mediated rejection: prevention, monitoring and treatment dilemmas

PURPOSE OF REVIEW

Antibody-mediated rejection (AMR) has emerged as the leading cause of late graft loss in kidney transplant recipients. Donor-specific antibodies are an independent risk factor for AMR and graft loss. However, not all donor-specific antibodies are pathogenic. AMR treatment is heterogeneous due to the lack of robust trials to support clinical decisions. This review provides an overview and comments on practical but relevant dilemmas physicians experience in managing kidney transplant recipients with AMR.

RECENT FINDINGS

Active AMR with donor-specific antibodies may be treated with plasmapheresis, intravenous immunoglobulin and corticosteroids with additional therapies considered on a case-by-case basis. On the contrary, no treatment has been shown to be effective against chronic active AMR. Various biomarkers and prediction models to assess the individual risk of graft failure and response to rejection treatment show promise.

SUMMARY

The ability to personalize management for a given kidney transplant recipient and identify treatments that will improve their long-term outcome remains a critical unmet need. Earlier identification of AMR with noninvasive biomarkers and prediction models to assess the individual risk of graft failure should be considered. Enrolling patients with AMR in clinical trials to assess novel therapeutic agents is highly encouraged.

Investigational drugs for the treatment of kidney transplant rejection

INTRODUCTION

Kidney transplant rejection remains an important clinical problem despite the development of effective immunosuppressive drug combination therapy. Two major types of rejection are recognized, namely T-cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR), which have a different pathophysiology and are treated differently. Unfortunately, long-term outcomes of both TCMR and ABMR remain unsatisfactory despite current therapy. Hence, alternative therapeutic drugs are urgently needed.

AREAS COVERED

This review covers novel and investigational drugs for the pharmacological treatment of kidney transplant rejection. Potential therapeutic strategies and future directions are discussed.

EXPERT OPINION

The development of alternative pharmacologic treatment of rejection has focused mostly on ABMR, since this is the leading cause of kidney allograft loss and currently lacks an effective, evidence-based therapy. At present, there is insufficient high-quality evidence for any of the covered investigational drugs to support their use in ABMR. However, with the emergence of targeted therapies, this potential arises for individualized treatment strategies. In order to generate more high-quality evidence for such strategies and overcome the obstacles of classic, randomized, controlled trials, we advocate the implementation of adaptive trial designs and surrogate clinical endpoints. We believe such adaptive trial designs could help to understand the risks and benefits of promising drugs such as tocilizumab, clazakizumab, belimumab, and imlifidase.

Identifying Factors Predicting Kidney Graft Survival in Chile Using Elastic-Net-Regularized Cox’s Regression

Background and Objectives: We developed a predictive statistical model to identify donor–recipient characteristics related to kidney graft survival in the Chilean population. Given the large number of potential predictors relative to the sample size, we implemented an automated variable selection mechanism that could be revised in future studies as more national data is collected. Materials and Methods: A retrospective multicenter study was conducted to analyze data from 822 adult kidney transplant recipients from adult donors between 1998 and 2018. To the best of our knowledge, this is the largest kidney transplant database to date in Chile. A procedure based on a cross-validated regularized Cox regression using the Elastic Net penalty was applied to objectively identify predictors of death-censored graft failure. Hazard ratios were estimated by adjusting a multivariate Cox regression with the selected predictors. Results: Seven variables were associated with the risk of death-censored graft failure; four from the donor: age (HR = 1.02, 95% CI: 1.00–1.03), male sex (HR = 0.64, 95% CI: 0.46–0.90), history of hypertension (HR = 1.49, 95% CI: 0.98–2.28), and history of diabetes (HR = 2.04, 95% CI: 0.97–4.29); two from the recipient: years on dialysis log-transformation (HR = 1.29, 95% CI: 0.99–1.67) and history of previous solid organ transplantation (HR = 2.02, 95% CI: 1.18–3.47); and one from the transplant: number of HLA mismatches (HR = 1.13, 95% CI: 0.99–1.28). Only the latter is considered for patient prioritization in deceased kidney allocation in Chile. Conclusions: A risk model for kidney graft failure was developed and trained for the Chilean population, providing objective criteria which can be used to improve efficiency in deceased kidney allocation.

Magnetic Resonance Elastography as Surrogate Marker of Interstitial Fibrosis in Kidney Transplantation: A Prospective Study

Background

Fibrosis progression is a major prognosis factor in kidney transplantation. Its assessment requires an allograft biopsy, which remains an invasive procedure at risk of complications.

Methods

We assessed renal stiffness by magnetic resonance elastography (MRE) as a surrogate marker of fibrosis in a prospective cohort of kidney transplant recipients compared with the histologic gold standard. Interstitial fibrosis was evaluated by three methods: the semi-quantitative Banff ci score, a visual quantitative evaluation by a pathologist, and a computer-assisted quantitative evaluation. MRE-derived stiffness was assessed at the superior, median, and inferior poles of the allograft.

Results

We initially enrolled 73 patients, but only 55 had measurements of their allograft stiffness by MRE before an allograft biopsy. There was no significant correlation between MRE-derived stiffness at the biopsy site and the ci score (ρ=-0.25, P=0.06) or with the two quantitative assessments (pathologist: ρ=-0.25, P=0.07; computer assisted: ρ=-0.21, P=0.12). We observed negative correlations between the stiffness of both the biopsy site and the whole allograft, with either the glomerulosclerosis percentage (ρ=-0.32, P=0.02 and ρ=-0.31, P=0.02, respectively) and the overall nephron fibrosis percentage, defined as the mean of the percentages of glomerulosclerosis and interstitial fibrosis (ρ=-0.30, P=0.02 and ρ=-0.28, P=0.04, respectively). At patient level, mean MRE-derived stiffness was similar across the three poles of the allograft (±0.25 kPa). However, a high variability of mean stiffness was found between patients, suggesting a strong influence of confounding factors. Finally, no significant correlation was found between mean MRE-derived stiffness and the slope of eGFR (P=0.08).

Conclusions

MRE-derived stiffness does not directly reflect the extent of fibrosis in kidney transplantation.

Shifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication

The measurement of outcomes in kidney transplantation has been more accurately documented than almost any other surgical procedure result in recent decades. With significant improvements in short- and long-term outcomes related to optimized immunosuppression, outcomes have gradually shifted away from conventional clinical endpoints (ie, patient and graft survival) to surrogate and composite endpoints. This article reviews how outcomes measurements have evolved in the past 2 decades in the setting of increased data collection and summarizes recent advances in outcomes measurements pertaining to clinical, histopathological, and immune outcomes. Finally, we discuss the use of composite endpoints and Bayesian concepts, specifically focusing on the integrative box risk prediction score, in conjunction with machine learning to refine prognostication.

Pretransplant characteristics of kidney transplant recipients that predict posttransplant outcome

Better characterization of the potential kidney transplant recipient using novel biomarkers, for example, pretransplant plasma endotrophin, will lead to improved outcome after transplantation. This mini-review will focus on current knowledge about pretransplant recipients’ characteristics, biomarkers, and immunology. Clinical characteristics of recipients including age, obesity, blood pressure, comorbidities, and estimated survival scores have been introduced for prediction of recipient and allograft survival. The pretransplant immunologic risk assessment include histocompatibility leukocyte antigens (HLAs), anti-HLA donor-specific antibodies, HLA-DQ mismatch, and non-HLA antibodies. Recently, there has been the hope that pretransplant determination of markers can further improve the prediction of posttransplant complications, both short-term and long-term outcomes including rejections, allograft loss, and mortality. Higher pretransplant plasma endotrophin levels were independently associated with posttransplant acute allograft injury in three prospective European cohorts. Elevated numbers of non-synonymous single-nucleotide polymorphism mismatch have been associated with increased allograft loss in a multivariable analysis. It is concluded that there is a need for integration of clinical characteristics and novel molecular and immunological markers to improve future transplant medicine to reach better diagnostic decisions tailored to the individual patient.

Histopathological prognostic factors in ANCA-associated glomerulonephritis

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) are a group of multisystemic autoimmune diseases characterized by necrotizing inflammation of small vessels. Kidney involvement is frequent in granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), and accounts for a significant proportion of the morbidity and mortality related to these diseases. Despite improvement in therapeutic management of ANCA-glomerulonephritis (ANCA-GN), end-stage kidney disease (ESKD) still occurs in up to 30% of affected patients within 5 years following diagnosis. Thus, identifying patients for whom aggressive immunosuppressive therapy will be more beneficial than deleterious is of great importance. Several clinical, biological and histological factors have been proposed as predictors of ESKD. The kidney biopsy is essential not only for the diagnosis, but also for evaluating renal prognosis. In this review, we discuss the prognostic value of renal lesions at the diagnosis of ANCA-GN by analyzing each compartment of the nephron. We also review existing ESKD risk classification in ANCA-GN and finally propose an example of a standardized pathology report that could be used in routine practice.

Artificial intelligence-enabled decision support in nephrology

Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems - which use algorithms based on learned examples - may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.

Prediction model for the risk of ESKD in patients with primary FSGS

The purpose of this study is to build a prediction model for accurate assessment of the risk of end-stage kidney disease (ESKD) in individuals with primary focal segmental glomerulosclerosis (FSGS) by integrating clinical and pathological features at biopsy. The prediction model was created based on a retrospective study of 99 patients with biopsy-proven primary FSGS diagnosed at our hospital between December 2012 and December 2019. We assessed discriminative ability and predictive accuracy of the model by C-index and calibration plot. Internal validation of the prediction model was performed with 1000-bootstrap procedure. Eight patients (8.1%) progressed to ESKD before 31 March 2021. Univariate analysis revealed that disease duration before biopsy, hematuria, hemoglobin, eGFR, and percentages of sclerosis and global sclerosis were associated with renal outcome. In multivariate analysis, three predictors were included in final prediction model: eGFR, hematuria, and percentage of sclerosis. The C-index of the model was 0.811 and 5-year calibration plot showed good agreement between predicted renal survival probability and actual observation. A nomogram and an online risk calculator were built on the basis of the prediction model. In conclusion, we constructed and internally validated the first prediction model for risk of ESKD in primary FSGS, which showed good discriminative ability and calibration performance. The prediction model provides an accurate and simple strategy to predict renal prognosis which may help to identify patients at high risk of ESKD and guide the management for patients with primary FSGS in clinical practice.

Using Information Available at the Time of Donor Offer to Predict Kidney Transplant Survival Outcomes: A Systematic Review of Prediction Models

Statistical models that can predict graft and patient survival outcomes following kidney transplantation could be of great clinical utility. We sought to appraise existing clinical prediction models for kidney transplant survival outcomes that could guide kidney donor acceptance decision-making. We searched for clinical prediction models for survival outcomes in adult recipients with single kidney-only transplants. Models that require information anticipated to become available only after the time of transplantation were excluded as, by that time, the kidney donor acceptance decision would have already been made. The outcomes of interest were all-cause and death-censored graft failure, and death. We summarised the methodological characteristics of the prediction models, predictive performance and risk of bias. We retrieved 4,026 citations from which 23 articles describing 74 models met the inclusion criteria. Discrimination was moderate for all-cause graft failure (C-statistic: 0.570–0.652; Harrell’s C: 0.580–0.660; AUC: 0.530–0.742), death-censored graft failure (C-statistic: 0.540–0.660; Harrell’s C: 0.590–0.700; AUC: 0.450–0.810) and death (C-statistic: 0.637–0.770; Harrell’s C: 0.570–0.735). Calibration was seldom reported. Risk of bias was high in 49 of the 74 models, primarily due to methods for handling missing data. The currently available prediction models using pre-transplantation information show moderate discrimination and varied calibration. Further model development is needed to improve predictions for the purpose of clinical decision-making.

Systematic Review Registration:https://osf.io/c3ehp/l.

Emerging drugs for antibody-mediated rejection after kidney transplantation: a focus on phase II & III trials

INTRODUCTION

Antibody-mediated rejection (ABMR) is a leading cause of kidney allograft failure. Its therapy continues to be challenge, and no treatment has been approved for the market thus far.

AREAS COVERED

In this article, we discuss the pathophysiology and phenotypic presentation of ABMR, the current level of evidence to support the use of available therapeutic strategies, and the emergence of tailored drugs now being evaluated in systematic clinical trials. We searched PubMed, Clinicaltrials.gov and Citeline's Pharmaprojects for pertinent information on emerging anti-rejection strategies, laying a focus on phase II and III trials.

EXPERT OPINION

Currently, we rely on the use of apheresis for alloantibody depletion and intravenous immunoglobulin (referred to as standard of care), preferentially in early active ABMR. Recent systematic trials have questioned the benefits of using the CD20 antibody rituximab or the proteasome inhibitor bortezomib. However, there are now several promising treatment approaches in the pipeline, which are being trialed in phase II and III studies. These include interleukin-6 antagonism, CD38-targeting antibodies, and selective inhibitors of complement. On the basis of the information that has emerged so far, it seems that innovative treatment strategies for clinical use in ABMR may be available within the next 5-10 years.

Harnessing the B Cell Response in Kidney Transplantation - Current State and Future Directions

Despite dramatic improvement in kidney transplantation outcomes over the last decades due to advent of modern immunosuppressive agents, long-term outcomes remain poor. Antibody-mediated rejection (ABMR), a B cell driven process, accounts for the majority of chronic graft failures. There are currently no FDA-approved regimens for ABMR; however, several clinical trials are currently on-going. In this review, we present current mechanisms of B cell response in kidney transplantation, the clinical impact of sensitization and ABMR, the B cell response under current immunosuppressive regimens, and ongoing clinical trials for ABMR and desensitization treatment.

Molecular Markers of Kidney Transplantation Outcome: Current Omics Tools and Future Developments

Purpose of review: The emerging field of molecular predictive medicine is aiming to change the traditional medical approach in renal transplantation. Many studies have explored potential biomarker molecules with predictive properties in renal transplantation, issued from omics research. Herein, we review the biomarker molecules of four technologies (i.e., Genomics, Transcriptomics, Proteomics, and Metabolomics) associated with favorable kidney transplant outcomes. Recent findings: Several panels of molecules have been associated with the outcome that the majority of markers are related to inflammation and immune response; although. other molecular ontologies are also represented, such as proteasome, growth, regeneration, and drug metabolism. Throughout this review, we highlight the lack of properly validated statistical demonstration. Indeed, the most preeminent molecular panels either remain at the limited size study stage or are not confirmed during large-scale studies. At the core of this problem, we identify the methodological shortcomings and propose a comprehensive workflow for discovery and validation of molecular biomarkers that aims to improve the relevance of these tools in the future. Summary: Overall, adopting a patient management through omics approach could bring remarkable improvement to transplantation success. An increased effort and investment between scientists, medical biologists, and clinicians seem to be the path toward a proper solution.

Dickkopf 3-A New Indicator for the Deterioration of Allograft Function After Kidney Transplantation

Evidence of tubular atrophy and interstitial fibrosis is prognostically unfavorable and associated with a premature graft loss after kidney transplantation. Recently, Dickkopf 3 (DKK3), a profibrotic glycoprotein released by stressed tubular epithelial cells, has been identified to cause IF/TA by regulating the Wnt/β-catenin signaling and seems to engage a T-cell response. The aim of our study was to determine if a correlation between DKK3 and graft function exists and if DKK3 could be a new indicator to identify patients at risk for a deterioration in graft function. Patients, transplanted between 2016 and 2018, were analyzed with regard to DKK3 in the urine and graft function (creatinine, eGFR, albuminuria). Multivariable analyzes were used including known factors influencing graft function (PRA, donor age) to stress robustness of DKK3. The 3 and 12 month DKK3 values were significant predictors for subsequent graft function up to 36 months. An increase of DKK3 from month 3 to 12 of ≥ 25% showed a higher risk of an impaired graft function, with, e.g., a reduction in eGFR of about 9–10 ml/min in contrast to patients without intensified DKK3 increase. Induction therapy has an influence on DKK3 as patients induced with a T-cell depleting therapy showed a trend toward lower DKK3 values. In summary, our study is the first investigation of DKK3 in kidney transplant recipients and was able to show that DKK3 could forecast graft function. It is recommended to investigate the potential of DKK3 as a predictor of kidney function after transplantation in further studies.

Costs in the Year Following Deceased Donor Kidney Transplantation: Relationships With Renal Function and Graft Failure

Relationships between renal function and medical costs for deceased donor kidney transplant recipients are not fully quantified post-transplant. We describe these relationships with renal function measured by estimated glomerular filtration rate (eGFR) and graft failure. The United States Renal Data System identified adults receiving single-organ deceased donor kidneys 2012–2015. Inpatient, outpatient, other facility costs and eGFRs at discharge, 6 and 12 months were included. A time-history of costs was constructed for graft failures and monthly costs in the first year post-transplant were compared to those without failure. The cohort of 24,021 deceased donor recipients had a 2.4% graft failure rate in the first year. Total medical costs exhibit strong trends with eGFR. Recipients with 6-month eGFRs of 30–59 ml/min/1.73m² have total costs 48% lower than those <30 ml/min/1.73m². For recipients with graft failure monthly costs begin to rise 3–4 months prior to failure, with incremental costs of over $38,000 during the month of failure. Mean annual total incremental costs of graft failure are over $150,000. Total costs post-transplant are strongly correlated with eGFR. Graft failure in the first year is an expensive, months-long process. Further reductions in early graft failures could yield significant human and economic benefits.