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

The evolution of patient-specific precision biomarkers to guide personalized heart-transplant care

Introduction

In parallel to the clinical maturation of heart transplantation over the last 50 years, rejection testing has been revolutionized within the systems biology paradigm triggered by the Human Genome Project.

Areas Covered

We have co-developed the first FDA-cleared diagnostic and prognostic leukocyte gene expression profiling biomarker test in transplantation medicine that gained international evidence-based medicine guideline acceptance to rule out moderate/severe acute cellular cardiac allograft rejection without invasive endomyocardial biopsies. This work prompted molecular re-classification of intragraft biology, culminating in the identification of a pattern of intragraft myocyte injury, in addition to acute cellular rejection and antibody-mediated rejection. This insight stimulated research into non-invasive detection of myocardial allograft injury. The addition of a donor-organ specific myocardial injury marker based on donor-derived cell-free DNA further strengthens the non-invasive monitoring concept, combining the clinical use of two complementary non-invasive blood-based measures, host immune activity-related risk of acute rejection as well as cardiac allograft injury.

Expert Opinion

This novel complementary non-invasive heart transplant monitoring strategy based on leukocyte gene expression profiling and donor-derived cell-free DNA that incorporates longitudinal variability measures provides an exciting novel algorithm of heart transplant allograft monitoring. This algorithm's clinical utility will need to be tested in an appropriately designed randomized clinical trial which is in preparation.

Development and validation of routine clinical laboratory data derived marker-based nomograms for the prediction of 5-year graft survival in kidney transplant recipients

Background: To develop and validate predictive nomograms for 5-year graft survival in kidney transplant recipients (KTRs) with easily-available laboratory data derived markers and clinical variables within the first year post-transplant.

Methods: The clinical and routine laboratory data from within the first year post-transplant of 1289 KTRs was collected to generate candidate predictors. Univariate and multivariate Cox analyses and LASSO were conducted to select final predictors. X-tile analysis was applied to identify optimal cutoff values to transform potential continuous factors into category variables and stratify patients. C-index, calibration curve, dynamic time-dependent AUC, decision curve analysis, and Kaplan-Meier curves were used to evaluate models’ predictive accuracy and clinical utility.

Results: Two predictive nomograms were constructed by using 0–6- and 0–12- month laboratory data, and showed good predictive performance with C-indexes of 0.78 and 0.85, respectively, in the training cohort. Calibration curves showed that the prediction probabilities of 5-year graft survival were in concordance with actual observations. Additionally, KTRs could be successfully stratified into three risk groups by nomograms.

Conclusions: These predictive nomograms combining demographic and 0–6- or 0–12- month markers derived from post-transplant laboratory data could serve as useful tools for early identification of 5-year graft survival probability in individual KTRs.

Clinical impact of serum bilirubin levels on kidney transplant outcomes

Serum bilirubin, a potent endogenous antioxidant, has been associated with decreased risks of cardiovascular disease, diabetes, and kidney disease. However, the effects of serum bilirubin on kidney transplant outcomes remain undetermined. We analyzed 1628 patients who underwent kidney transplantations between 2003 and 2017. Patients were grouped into sex-specific quartiles according to mean serum bilirubin levels, 3–12 months post-transplantation. Median bilirubin levels were 0.66 mg/dL in males and 0.60 mg/dL in females. The intra-individual variability of serum bilirubin levels was low (9%). Serum bilirubin levels were inversely associated with graft loss, death-censored graft failure, and all-cause mortality, independent of renal function, donor status, and transplant characteristics. Multivariable analysis revealed that the lowest serum bilirubin quartile was associated with increased risk of graft loss (HR 2.64, 95% CI 1.67–4.18, P < 0.001), death-censored graft failure (HR 2.97, 95% CI 1.63–5.42, P < 0.001), and all-cause mortality (HR 2.07, 95% CI 1.01–4.22, P = 0.046). Patients with lower serum bilirubin were also at greater risk of rejection and exhibited consistently lower glomerular filtration rates than those with higher serum bilirubin. Serum bilirubin levels were significantly associated with transplantation outcomes, suggesting that bilirubin could represent a therapeutic target for improving long-term transplant outcomes.

In-silico performance, validation, and modeling of the Nanostring Banff Human Organ transplant gene panel using archival data from human kidney transplants

BACKGROUND

RNA gene expression of renal transplantation biopsies is commonly used to identify the immunological patterns of graft rejection. Mostly done with microarrays, seminal findings defined the patterns of gene sets associated with rejection and non-rejection kidney allograft diagnoses. To make gene expression more accessible, the Molecular Diagnostics Working Group of the Banff Foundation for Allograft Pathology and NanoString Technologies partnered to create the Banff Human Organ Transplant Panel (BHOT), a gene panel set of 770 genes as a surrogate for microarrays (~ 50,000 genes). The advantage of this platform is that gene expressions are quantifiable on formalin fixed and paraffin embedded archival tissue samples, making gene expression analyses more accessible. The purpose of this report is to test in silico the utility of the BHOT panel as a surrogate for microarrays on archival microarray data and test the performance of the modelled BHOT data.

METHODS

BHOT genes as a subset of genes from downloaded archival public microarray data on human renal allograft gene expression were analyzed and modelled by a variety of statistical methods.

RESULTS

Three methods of parsing genes verify that the BHOT panel readily identifies renal rejection and non-rejection diagnoses using in silico statistical analyses of seminal archival databases. Multiple modelling algorithms show a highly variable pattern of misclassifications per sample, either between differently constructed principal components or between modelling algorithms. The misclassifications are related to the gene expression heterogeneity within a given diagnosis because clustering the data into 9 groups modelled with fewer misclassifications.

CONCLUSION

This report supports using the Banff Human Organ Transplant Panel for gene expression of human renal allografts as a surrogate for microarrays on archival tissue. The data modelled satisfactorily with aggregate diagnoses although with limited per sample accuracy and, thereby, reflects and confirms the modelling complexity and the challenges of modelling gene expression as previously reported.

Three-month course of intragraft transcriptional changes in kidney allografts with early histological minimal injury - a cohort study

The tubulitis with/without interstitial inflammation not meeting criteria for T-cell-mediated rejection (minimal allograft injury) is the most frequent histological findings in early transplant biopsies. The course of transcriptional changes in sequential kidney graft biopsies has not been studied yet. Molecular phenotypes were analyzed using the Molecular Microscope^® Diagnostic System (MMDx) in 46 indication biopsies (median 13 postoperative days) diagnosed as minimal allograft injury and in corresponding follow-up biopsies at 3 months. All 46 patients with minimal injury in early biopsy received steroid pulses. MMDx interpreted indication biopsies as no-rejection in 34/46 (74%), T-cell-mediated rejection (TCMR) in 4/46 (9%), antibody-mediated rejection in 6/46 (13%), and mixed rejection in 2/46 (4%) cases. Follow-up biopsies were interpreted by MMDx in 37/46 (80%) cases as no-rejection, in 4/46 (9%) as TCMR, and in 5/46 (11%) as mixed rejection. Follow-up biopsies showed a decrease in MMDx-assessed acute kidney injury (P = 0.001) and an increase of atrophy-fibrosis (P = 0.002). The most significant predictor of MMDx rejection scores in follow-up biopsies was the tubulitis classifier score in initial biopsies (AUC = 0.84, P = 0.002), confirmed in multivariate binary regression (OR = 16, P = 0.016). Molecular tubulitis score at initial biopsy has the potential to discriminate patients at risk for molecular rejection score at follow-up biopsy.

A validation study of the 4-variable and 8-variable kidney failure risk equation in transplant recipients in the United Kingdom

Background

There is emerging evidence that the 4-variable Kidney Failure Risk Equation (KFRE) can be used for risk prediction of graft failure in transplant recipients. However, geographical validation of the 4-variable KFRE in transplant patients is lacking, as is whether the more extensive 8-variable KFRE improves predictive accuracy. This study aimed to validate the 4- and 8-variable KFRE predictions of the 5-year death-censored risk of graft failure in patients in the United Kingdom.

Methods

A retrospective cohort study involved 415 transplant recipients who had their first renal transplant between 2003 and 2015 and were under follow-up at Salford Royal NHS Foundation Trust. The KFRE risk scores were calculated on variables taken 1-year post-transplant. The area under the receiver operating characteristic curves (AUC) and calibration plots were evaluated to determine discrimination and calibration of the 4- and 8-variable KFREs in the whole cohort as well as in a subgroup analysis of living and deceased donor recipients and in patients with an eGFR< 45 ml/min/1.73m².

Results

There were 16 graft failure events (4%) in the whole cohort. The 4- and 8-variable KFREs showed good discrimination with AUC of 0.743 (95% confidence interval [CI] 0.610–0.876) and 0.751 (95% CI 0.629–0.872) respectively. In patients with an eGFR< 45 ml/min/1.73m², the 8-variable KFRE had good discrimination with an AUC of 0.785 (95% CI 0.558–0.982) but the 4-variable provided excellent discrimination in this group with an AUC of 0.817 (0.646–0.988). Calibration plots however showed poor calibration with risk scores tending to underestimate risk of graft failure in low-risk patients and overestimate risk in high-risk patients, which was seen in the primary and subgroup analyses.

Conclusions

Despite adequate discrimination, the 4- and 8-variable KFREs are imprecise in predicting graft failure in transplant recipients using data 1-year post-transplant. Larger, international studies involving diverse patient populations should be considered to corroborate these findings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02259-4.

Chronic transplant glomerulopathy: New insights into pathogenesis

There have been recent significant advances in short-term outcomes in renal transplantation, however, long-term allograft survival remains a challenge. With reported incidences as high of 74.5% of chronic graft loss in patients with biopsies showing transplant glomerulopathy (TG), this syndrome represents an important factor for chronic allograft complications. In this review we show an overview of the novel mechanistic insights into pathogenesis of TG, as well as a brief description of the pathology, diagnosis and newer prognostic indices within TG diagnosis. These data raise intriguing roles for cell-mediated immunity and podocyte stress in TG as well as reinforce previous associations of TG with ABMR. We also delve into management strategies for TG and report the paucity of existing clinical trial data for this prevalent condition in renal transplants.

Advances in personalized medicine and noninvasive diagnostics in solid organ transplantation

Personalized medicine has been a mainstay and in practice in transplant pharmacotherapy since the advent of the field. Decisions pertaining to the diagnosis, selection, and monitoring of transplant pharmacotherapy are aimed toward the individual, the allograft, and the overall immunologic needs of the patient. Recent advances in pharmacogenomics, noninvasive biomarkers, and artificial intelligence (AI) technologies have the promise of transforming the way we individualize treatment and monitor allograft function. Pharmacogenomic testing can provide clinicians with additional data that can minimize toxicity and maximize therapeutic dosing in high-risk patients, leading to more informed decisions that may decrease the risk of rejection and adverse outcomes related to immunosuppressive therapies. Development of noninvasive strategies to monitor allograft function may offer safer and more convenient methods to detect allograft injury. Cell free DNA and gene expression profiling offer the potential to serve as "liquid biopsies" minimizing the risk to patients and providing clinicians with useful molecular data that may help individualize immunosuppression and rejection treatment. Use of big data in transplant and novel AI platforms, such as the iBox, hold tremendous promise in providing clinicians a "glimpse into the future" thereby allowing for a more individualized approach to immunosuppressive therapy that may minimize future adverse outcomes. Advances in diagnostics, laboratory science, and AI have made the application of personalized medicine even more tailored for solid organ transplant recipients. In this perspective, we summarize the current and emerging tools available, literature supporting use, and the horizon for future personalization of transplantation.

New concepts in chronic antibody-mediated kidney allograft rejection: prevention and treatment

PURPOSE OF REVIEW

Chronic antibody-mediated rejection (AMR) is a cardinal cause of transplant failure, with currently no proven effective prevention or treatment. The present review will focus on new therapeutic concepts currently under clinical evaluation.

RECENT FINDINGS

One interesting treatment approach may be interference with interleukin-6 (IL-6) signaling to modulate B-cell immunity and donor-specific antibody (DSA) production. Currently, a large phase III randomized controlled trial is underway to clarify the safety and efficacy of clazakizumab, a high-affinity anti-IL-6 antibody, in chronic AMR. A prevention/treatment strategy may be costimulation blockade using belatacept to interfere with germinal center responses and DSA formation. In a recent uncontrolled study, belatacept conversion was shown to stabilize renal function and dampen AMR activity. Moreover, preliminary clinical results suggest efficacy of CD38 antibodies to deplete plasma and natural killer cells to treat AMR, with anecdotal reports demonstrating at least transient resolution of active rejection.

SUMMARY

There are promising concepts on the horizon for the prevention and treatment of chronic AMR. The design of adequately powered placebo-controlled trials to clarify the safety and efficacy of such new therapies, however, remains a big challenge, and will rely on the definition of precise surrogate endpoints predicting long-term allograft survival. Mapping the natural history of AMR would greatly help the understanding of who would derive benefits from treatment.

Statistical performance of 16 posttransplant risk scores in a contemporary cohort of heart transplant recipients

Accurate risk stratification of early heart transplant failure is required to avoid futile transplants and rationalize donor selection. We aimed to evaluate the statistical performance of existing risk scores on a contemporary cohort of heart transplant recipients. After an exhaustive search, we identified 16 relevant risk scores. From the UNOS database, we selected all first noncombined adult heart transplants performed between 2014 and 2017 for validation. The primary endpoint was death or retransplant during the first year posttransplant. For all scores, we analyzed their association with outcomes, sensitivity, specificity, likelihood ratios, and discrimination (concordance index and overlap of individual scores). The cohort included 9396 patients. All scores were significantly associated with the primary outcome (P < .001 for all scores). Their likelihood ratios, both negative and positive, were poor. The discriminative performance of all scores was limited, with concordance index ranging from 0.544 to 0.646 (median 0.594) and an important overlap of individual scores between patients with or without the primary endpoint. Subgroup analyses revealed important variation in discrimination according to donor age, recipient age, and the type of assist device used at transplant. Our findings raise concerns about the use of currently available scores in the clinical field.

External Validation of the DynPG for Kidney Transplant Recipients

BACKGROUND

In kidney transplantation, dynamic prediction of patient and kidney graft survival (DynPG) may help to promote therapeutic alliance by delivering personalized evidence-based information about long-term graft survival for kidney transplant recipients. The objective of the current study is to externally validate the DynPG.

METHODS

Based on 6 baseline variables, the DynPG can be updated with any new serum creatinine measure available during the follow-up. From an external validation sample of 1637 kidney recipients with a functioning graft at 1-year posttransplantation from 2 European transplantation centers, we assessed the prognostic performance of the DynPG.

RESULTS

As one can expect from an external validation sample, differences in several recipient, donor, and transplantation characteristics compared with the learning sample were observed. Patients were mainly transplanted from deceased donors (91.6% versus 84.8%; P < 0.01), were less immunized against HLA class I (18.4% versus 32.7%; P < 0.01) and presented less comorbidities (62.2% for hypertension versus 82.7%, P < 0.01; 25.1% for cardiovascular disease versus 33.9%, P < 0.01). Despite these noteworthy differences, the area under the ROC curve varied from 0.70 (95% confidence interval [CI], 0.64-0.76) to 0.76 (95% CI, 0.64-0.88) for prediction times at 1 and 6 years posttransplantation respectively, and calibration plots revealed reasonably accurate predictions.

CONCLUSIONS

We validated the prognostic capacities of the DynPG in terms of both discrimination and calibration. Our study showed the robustness of the DynPG for informing both the patient and the physician, and its transportability for a cohort presenting different features than the one used for the DynPG development.

Cellular therapies in organ transplantation

Cellular therapy is a promising tool for improving the outcome of organ transplantation. Various cell types with different immunoregulatory and regenerative properties may find application for specific transplant rejection or injury-related indications. The current era is crucial for the development of cellular therapies. Preclinical models have demonstrated the feasibility of efficacious cell therapy in transplantation, early clinical trials have shown safety of several of these therapies, and the first steps towards efficacy studies in humans have been made. In this review, we address the current state of the art of cellular therapies in clinical transplantation and discuss monitoring tools and endpoints for these studies.

A Changing Paradigm in Heart Transplantation: An Integrative Approach for Invasive and Non-Invasive Allograft Rejection Monitoring

Cardiac allograft rejection following heart transplantation is challenging to diagnose. Tissue biopsies are the gold standard in monitoring the different types of rejection. The last decade has seen an increased emphasis on identifying non-invasive methods to improve rejection diagnosis and overcome tissue biopsy invasiveness. Liquid biopsy, as an efficient non-invasive diagnostic and prognostic oncological monitoring tool, seems to be applicable in heart transplant follow-ups. Moreover, molecular techniques applied on blood can be translated to tissue samples to provide novel perspectives on tissue and reveal new diagnostic and prognostic biomarkers. This review aims to provide a comprehensive overview of the state-of-the-art of the new methodologies in cardiac allograft rejection monitoring and investigate the future perspectives on invasive and non-invasive rejection biomarkers identification. We reviewed literature from the most used scientific databases, such as PubMed, Google Scholar, and Scopus. We extracted 192 papers and, after a selection and exclusion process, we included in the review 81 papers. The described limitations notwithstanding, this review show how molecular biology techniques and omics science could be deployed complementarily to the histopathological rejection diagnosis on tissue biopsies, thus representing an integrated approach for heart transplant patients monitoring.

Development and external validation study combining existing models and recent data into an up-to-date prediction model for evaluating kidneys from older deceased donors for transplantation

With a rising demand for kidney transplantation, reliable pre-transplant assessment of organ quality becomes top priority. In clinical practice, physicians are regularly in doubt whether suboptimal kidney offers from older donors should be accepted. Here, we externally validate existing prediction models in a European population of older deceased donors, and subsequently developed and externally validated an adverse outcome prediction tool. Recipients of kidney grafts from deceased donors 50 years of age and older were included from the Netherlands Organ Transplant Registry (NOTR) and United States organ transplant registry from 2006-2018. The predicted adverse outcome was a composite of graft failure, death or chronic kidney disease stage 4 plus within one year after transplantation, modelled using logistic regression. Discrimination and calibration were assessed in internal, temporal and external validation. Seven existing models were validated with the same cohorts. The NOTR development cohort contained 2510 patients and 823 events. The temporal validation within NOTR had 837 patients and the external validation used 31987 patients in the United States organ transplant registry. Discrimination of our full adverse outcome model was moderate in external validation (C-statistic 0.63), though somewhat better than discrimination of the seven existing prediction models (average C-statistic 0.57). The model's calibration was highly accurate. Thus, since existing adverse outcome kidney graft survival models performed poorly in a population of older deceased donors, novel models were developed and externally validated, with maximum achievable performance in a population of older deceased kidney donors. These models could assist transplant clinicians in deciding whether to accept a kidney from an older donor.

Analysis of Factors Influencing Kidney Function of Recipients After Renal Transplantation in Southwestern China: A Retrospective Study

Background: Factors influencing the kidney function of patients after renal transplantation include both recipient-related factors and donor-related factors. To gain a better understanding of these factors and to improve clinical decision-making, we performed a retrospective study of southwestern Chinese people receiving kidney transplantation.

Methods: In this retrospective analysis, a total of 2,462 recipients receiving allogeneic kidney transplantation in West China Hospital of Sichuan University from December 13, 2008 to January 10, 2018 were included. Data on recipient and donor characteristics were extracted from the Transplant Center Database and stratified by discrete time points after kidney transplantation. Univariate and multivariate logistic regression analyses were carried out on the study variables, and kidney function of postoperative patients was monitored using cystatin C (CysC) as the outcome indicator.

Results: From the univariate analysis, several factors showed statistically significant short-term impact on kidney function based on CysC after kidney transplantation, including age, ethnicity, body mass index (BMI), and HLA A-B-DR-DQ loci mismatch. Gender of recipients and gender-consistency between donors and recipients revealed both short-term and long-term influence. Younger donors had significantly better medium-and-long-term influence on kidney function. From the multivariate logistic regression analysis, recipient gender, ethnicity, BMI, and donor age were independent factors affecting postoperative CysC recovery at discrete time points.

Conclusion: Several factors of recipients related to renal function after kidney transplantation, such as gender, ethnicity, BMI and donor's age should be paid more attention to. Moreover, female and non-Han recipients decreased the risk of poor outcome during postoperative kidney function recovery while large BMI of recipients and higher donor age increased the risk. It is useful to predict the postoperative renal function earlier according to corresponding factors, and improve the patient's quality of life.

The Kidney Failure Risk Equation for Prediction of Allograft Loss in Kidney Transplant Recipients

Rationale & Objective

The Kidney Failure Risk Equation (KFRE) is a simple widely validated prediction model using age, sex, estimated glomerular filtration rate, and urinary albumin-creatinine ratio to predict the risk for end-stage kidney disease. Data are limited for its applicability to kidney transplant recipients.

Study Design

Validation study of the KFRE as a post hoc analysis of the Folic Acid for Vascular Outcomes Reduction in Transplantation (FAVORIT) Trial.

Setting & Participants

Adult kidney transplant recipients with functioning kidney allografts at least 6 months posttransplantation from 30 centers in the United States, Canada, and Brazil. Participants with estimated glomerular filtration rates < 60 mL/min/1.73 m² at study entry were included.

Predictor

2- and 5-year kidney failure risk predicted by the KFRE using variables at study entry.

Outcome

Graft loss, defined by initiation of dialysis.

Analytical Approach

Discrimination of the KFRE was assessed using C statistics; calibration was assessed by plotting predicted risk against observed cumulative incidence of graft loss.

Results

2,889 participants were included. Within 2 years, 98 participants developed graft loss, 107 participants died with a functioning graft, and 129 participants were lost to follow-up, and by 5 years, 252 had developed graft loss, 265 died with a functioning graft, and 1,543 were lost to follow-up. The KFRE demonstrated accurate calibration and discrimination (C statistic, 0.85 [95% CI, 0.81-0.88] at 2 years and 0.81 [95% CI, 0.78-0.84] at 5 years); performance was similar regardless of donor type (living vs deceased) and graft vintage, with the noted exception of poorer calibration for graft vintage less than 2 years.

Limitations

Unavailable cause of graft loss.

Conclusions

The KFRE accurately predicted the risk for graft loss among adult kidney transplant recipients with graft vintage longer than 2 years and may be a useful prognostic tool for nephrologists caring for kidney transplant recipients.

Organ allocation in the age of the algorithm: avoiding futile transplantation - utility in allocation

PURPOSE OF REVIEW

This review describes and questions the evolution of allocation systems from local team decisions in the 20th century to patient-oriented allocation using complex algorithm predicting transplant benefit.

RECENT FINDINGS

The opening years of the 2000s have seen the implementation of prioritization scores aiming at increasing transparency and reducing waitlist mortality. The 2010s have underlined the necessity of drawing the upper limits of how sick a patient can be while still ensuring acceptable survival. More complex algorithms evaluating transplant benefit have been implemented in allocation systems to take this issue into account.

SUMMARY

Allocation algorithms are becoming more and more complex, integrating numerous parameters from both donor and recipient to achieve optimal matching. The limitations of implementing these complex algorithms are represented by the evermoving waiting list demography, geographic disparities between recipients and donors, team policy adaptation to rule changes, and implicit biases within the transplant community. Survival as the only metric by which to define benefit may be seen as restrictive; quality of life may be a fruitful measure for better defining benefit in organ transplantation in the future.

Prediction scores for risk of allograft loss in patients receiving kidney transplants: nil satis nisi optimum

Long-term graft survival is the main concern of kidney transplantation. Some strategies have been tested to predict graft survival using estimated glomerular filtration rate or proteinuria at different time points, histologic assessment, non-invasive biomarkers or even machine-learning methods. However, the 'magical formulae' for allograft survival prediction does not exist yet.

Sensitization in transplantation: Assessment of risk (STAR) 2019 Working Group Meeting Report

The purpose of the STAR 2019 Working Group was to build on findings from the initial STAR report to further clarify the expectations, limitations, perceptions, and utility of alloimmune assays that are currently in use or in development for risk assessment in the setting of organ transplantation. The goal was to determine the precision and clinical feasibility/utility of such assays in evaluating both memory and primary alloimmune risks. The process included a critical review of biologically driven, state-of-the-art, clinical diagnostics literature by experts in the field and an open public forum in a face-to-face meeting to promote broader engagement of the American Society of Transplantation and American Society of Histocompatibility and Immunogenetics membership. This report summarizes the literature review and the workshop discussions. Specifically, it highlights (1) available assays to evaluate the attributes of HLA antibodies and their utility both as clinical diagnostics and as research tools to evaluate the effector mechanisms driving rejection; (2) potential assays to assess the presence of alloimmune T and B cell memory; and (3) progress in the development of HLA molecular mismatch computational scores as a potential prognostic biomarker for primary alloimmunity and its application in research trial design.

KidneyCare Guided Immuno-Optimization in Renal Allografts: The KIRA Protocol

Immunosuppressant agents are essential in every transplant recipient's care yet walking the fine line of over- or under-immunosuppression is a constant struggle for both patients and transplant providers alike. Optimization and personalization of immunosuppression has been limited by the need for non-invasive graft surveillance methods that are specific enough to identify organ injury in real time. With this in mind, we propose a pilot study protocol utilizing both donor derived cell free DNA (dd-cfDNA, gene expression profiling (GEP), and machine learning (iBox), called KidneyCare, to assess the feasibility and safety in reducing immunosuppressant exposure without increasing the risk of clinical rejection, graft injury, or allograft loss. Patients randomized to the immunominimization arm will be enrolled in one of two protocols designed to eliminate one immunosuppressant and optimize the dose of the Calcineurin Inhibitors (CNIs) using the KidneyCare platform. All patients will be maintained on dual therapy of either steroids and a low dose CNI, or mycophenolate mofetil (MMF) and low dose CNI. Their outcomes will be compared to patients who have their immunosuppressants managed using standard clinical assessment and treatment protocols to determine the impact of immuno-optimization on graft function, complications, and patient reported outcomes.

Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot

Background

Renal replacement therapy (RRT) is a public health problem worldwide. Kidney transplantation (KT) is the best treatment for elderly patients' longevity and quality of life.

Objectives

The primary endpoint was to compare elderly versus younger KT recipients by analyzing the risk covariables involved in worsening renal function, proteinuria, graft loss, and death one year after KT. The secondary endpoint was to create a robot based on logistic regression capable of predicting the likelihood that elderly recipients will develop worse renal function one year after KT.

Method

Unicentric retrospective analysis of a cohort was performed with individuals aged ≥60 and <60 years old. We analysed medical records of KT recipients from January to December 2017, with a follow-up time of one year after KT. We used multivariable logistic regression to estimate odds ratios for elderly vs younger recipients, controlled for demographic, clinical, laboratory, data pre- and post-KT, and death.

Results

18 elderly and 100 younger KT recipients were included. Pretransplant immune variables were similar between two groups. No significant differences (P > 0.05) between groups were observed after KT on laboratory data means and for the prevalences of diabetes mellitus, hypertension, acute rejection, cytomegalovirus, polyomavirus, and urinary infections. One year after KT, the creatinine clearance was higher (P = 0.006) in youngers (70.9 ± 25.2 mL/min/1.73 m²) versus elderlies (53.3 ± 21.1 mL/min/1.73 m²). There was no difference in death outcome comparison. Multivariable analysis among covariables predisposing chronic kidney disease epidemiology collaboration (CKD-EPI) equation <60 mL/min/1.73 m² presented a statistical significance for age ≥60 years (P = 0.01) and reduction in serum haemoglobin (P = 0.03). The model presented goodness-fit in the evaluation of artificial intelligence metrics (precision: 90%; sensitivity: 71%; and F ₁ score: 0.79).

Conclusion

Renal function in elderly KT recipients was lower than in younger KT recipients. However, patients aged ≥60 years maintained enough renal function to remain off dialysis. Moreover, a learning machine application built a robot (Elderly KTbot) to predict in the elderly populations the likelihood of worse renal function one year after KT.

Impact of belatacept conversion on kidney transplant function, histology, and gene expression - a single-center study

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 = <0.001) from baseline = 31±15 to 40.2 ± 17.6 ml/min/1.73m² by 6 months postconversion, but then stayed stable. This improvement was also observed (P < 0.001) in comparison with a propensity matched control cohort on CNI, where eGFR stayed stable (mean ~ 32ml/min/1.72m² ) over 2-year follow-up. Patients converted < 6 months post-transplant were more likely to have a long-term improvement in kidney function. Paired gene expression analysis of 30 (of 53) consecutive pre- and postconversion surveillance biopsies did not reveal changes in inflammation/acute injury; although atrophy-fibrosis score worsened (mean = 0.28 to 0.44; P = 0.005). Thus, improvement in renal function with belatacept conversion occurred early and then sustained in comparison with controls where renal function remained unchanged overtime. We were unable to show molecular signals that could be related to CNI administration and regressed after withdrawal.

The Banff 2019 Kidney Meeting Report (I): Updates on and clarification of criteria for T cell- and antibody-mediated rejection

The XV. Banff conference for allograft pathology was held in conjunction with the annual meeting of the American Society for Histocompatibility and Immunogenetics in Pittsburgh, PA (USA) and focused on refining recent updates to the classification, advances from the Banff working groups, and standardization of molecular diagnostics. This report on kidney transplant pathology details clarifications and refinements to the criteria for chronic active (CA) T cell-mediated rejection (TCMR), borderline, and antibody-mediated rejection (ABMR). The main focus of kidney sessions was on how to address biopsies meeting criteria for CA TCMR plus borderline or acute TCMR. Recent studies on the clinical impact of borderline infiltrates were also presented to clarify whether the threshold for interstitial inflammation in diagnosis of borderline should be i0 or i1. Sessions on ABMR focused on biopsies showing microvascular inflammation in the absence of C4d staining or detectable donor-specific antibodies; the potential value of molecular diagnostics in such cases and recommendations for use of the latter in the setting of solid organ transplantation are presented in the accompanying meeting report. Finally, several speakers discussed the capabilities of artificial intelligence and the potential for use of machine learning algorithms in diagnosis and personalized therapeutics in solid organ transplantation.

Evidence for the alloimmune basis and prognostic significance of Borderline T cell-mediated rejection

Prognostic biomarkers of T cell-mediated rejection (TCMR) have not been adequately studied in the modern era. We evaluated 803 renal transplant recipients and correlated HLA-DR/DQ molecular mismatch alloimmune risk categories (low, intermediate, high) with the severity, frequency, and persistence of TCMR. Allograft survival was reduced in recipients with Banff Borderline (hazard ratio [HR] 2.4, P = .003) and Banff ≥ IA TCMR (HR 4.3, P < .0001) including a subset who never developed de novo donor-specific antibodies (P = .002). HLA-DR/DQ molecular mismatch alloimmune risk categories were multivariate correlates of Banff Borderline and Banff ≥ IA TCMR and correlated with the severity and frequency of rejection episodes. Recipient age, HLA-DR/DQ molecular mismatch category, and cyclosporin vs tacrolimus immunosuppression were independent correlates of Banff Borderline and Banff ≥ IA TCMR. In the subset treated with tacrolimus (720/803) recipient age, HLA-DR/DQ molecular mismatch category, and tacrolimus coefficient of variation were independent correlates of TCMR. The correlation of HLA-DR/DQ molecular mismatch category with TCMR, including Borderline, provides evidence for their alloimmune basis. HLA-DR/DQ molecular mismatch may represent a precise prognostic biomarker that can be applied to tailor immunosuppression or design clinical trials based on individual patient risk.

Banff Digital Pathology Working Group: Going digital in transplant pathology

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.

Banff 2019 Meeting Report: Molecular diagnostics in solid organ transplantation-Consensus for the Banff Human Organ Transplant (B-HOT) gene panel and open source multicenter validation

This meeting report from the XV Banff conference describes the creation of a multiorgan transplant gene panel by the Banff Molecular Diagnostics Working Group (MDWG). This Banff Human Organ Transplant (B-HOT) panel is the culmination of previous work by the MDWG to identify a broadly useful gene panel based on whole transcriptome technology. A data-driven process distilled a gene list from peer-reviewed comprehensive microarray studies that discovered and validated their use in kidney, liver, heart, and lung transplant biopsies. These were supplemented by genes that define relevant cellular pathways and cell types plus 12 reference genes used for normalization. The 770 gene B-HOT panel includes the most pertinent genes related to rejection, tolerance, viral infections, and innate and adaptive immune responses. This commercially available panel uses the NanoString platform, which can quantitate transcripts from formalin-fixed paraffin-embedded samples. The B-HOT panel will facilitate multicenter collaborative clinical research using archival samples and permit the development of an open source large database of standardized analyses, thereby expediting clinical validation studies. The MDWG believes that a pathogenesis and pathway based molecular approach will be valuable for investigators and promote therapeutic decision-making and clinical trials.

Trajectories of glomerular filtration rate and progression to end stage kidney disease after kidney transplantation

Although the gold standard of monitoring kidney transplant function relies on glomerular filtration rate (GFR), little is known about GFR trajectories after transplantation, their determinants, and their association with outcomes. To evaluate these parameters we examined kidney transplant recipients receiving care at 15 academic centers. Patients underwent prospective monitoring of estimated GFR (eGFR) measurements, with assessment of clinical, functional, histological and immunological parameters. Additional validation took place in seven randomized controlled trials that included a total of 14,132 patients with 403,497 eGFR measurements. After a median follow-up of 6.5 years, 1,688 patients developed end-stage kidney disease. Using unsupervised latent class mixed models, we identified eight distinct eGFR trajectories. Multinomial regression models identified seven significant determinants of eGFR trajectories including donor age, eGFR, proteinuria, and several significant histological features: graft scarring, graft interstitial inflammation and tubulitis, microcirculation inflammation, and circulating anti-HLA donor specific antibodies. The eGFR trajectories were associated with progression to end stage kidney disease. These trajectories, their determinants and respective associations with end stage kidney disease were similar across cohorts, as well as in diverse clinical scenarios, therapeutic eras and in the seven randomized control trials. Thus, our results provide the basis for a trajectory-based assessment of kidney transplant patients for risk stratification and monitoring.

Lack of Histological and Molecular Signature Response to Tocilizumab in Kidney Transplants with Chronic Active Antibody Mediated Rejection: A Case Series

Background

Traditional therapies for caAbMR have unclear efficacy with significant side effects in recipients of kidney transplants (KTs). A recent single-center case series suggested tocilizumab (TCZ) could stabilize renal function and improve microvascular inflammation. Here we report our findings of the use of TCZ in patients with caAbMR.

Methods

Ten adult recipients of KTs with biopsy-proven caAbMR were treated with TCZ at 8 mg/kg per month. Patients were monitored for adverse events, and therapy was interrupted in the setting of serious infections. Six patients (60%) underwent post-treatment biopsies.

Results

Patients (mean age of 43 years) were initiated on TCZ at a median of 36 months post-KT. A majority of patients were black (70%), underwent regrafts (40%), and were sensitized (mean cPRA=41%). Patients received a median of six doses of TCZ (range=3-10). At a median follow-up of 12 months (range=8-24 months), renal function did not show improvement (mean eGFR, 42±18 ml/min per 1.73 m² to 37±24 ml/min per 1.73 m²; P=0.27). The slope of decline in eGFR remained unchanged (-0.14±0.9 to -0.33±1.1; P=0.25). There was no improvement in mean MVI (g+ptc) (4.8±1.4 to 4.2±2.0; P=0.39) scores or Molecular Microscope Diagnostic System (MMDx) AbMR scores (0.79±0.17 to 0.78±0.26; P=0.86). There was a numeric worsening of chronicity (ci+ct) scores (2.5±0.8 to 3.3±1.7; P=0.38) and MMDx atrophy fibrosis scores (0.36±0.24 to 0.58±0.15; P=0.21). Patient survival was 90%, with one patient death due to complications from a hip infection. Overall death-censored graft survival was 80%, with two graft losses in patients who had recurrent infections requiring hospitalization.

Conclusions

In this early experience, we report a lack of efficacy and toxicity with the use of TCZ for caAbMR. Prospective clinical trials are needed to clarify the role of IL-6 blockade and the possibility of increased incidence of infections in patients with caAbMR who are treated with TCZ.

Donor-derived Cell-free DNA Combined With Histology Improves Prediction of Estimated Glomerular Filtration Rate Over Time in Kidney Transplant Recipients Compared With Histology Alone

Higher Banff inflammation and chronicity scores on kidney transplant biopsies are associated with poorer graft survival, although histology alone has limitations in predicting outcomes. We investigated if integrating donor-derived cell-free DNA (dd-cfDNA, Allosure; CareDx, Inc.) with Banff biopsy scores into a predictive model for estimated glomerular filtration rate over time can improve prognostic assessment versus histology alone.

Methods

We identified 180 kidney transplant patients with dd-cfDNA assessed within 1 mo of biopsy. Using linear mixed-effects models, a prediction model of Banff histology scores and dd-cfDNA on estimated glomerular filtration rate over time was derived. Nested models were compared using the likelihood-ratio test, Akaike Information Criterion, and Bayesian Information Criterion to assess if inclusion of dd-cfDNA into a model consisting of Banff biopsy scores would improve model fit.

Results

Univariate models identified significant covariate-by-time interactions for cg = 3 versus <3 (coefficient: -1.3 mL/min/1.73 m²/mo; 95% confidence interval [CI], -2.4 to -0.2; P = 0.02) and ci + ct ≥ 3 versus <3 (coefficient: -0.7 mL/min/1.73 m²/mo; 95% CI, -1.3 to -0.1; P = 0.03) and a trend toward significant covariate-by-time interaction for dd-cfDNA (coefficient: -0.5 mL/min/1.73 m²/mo; 95% CI, -1.0 to 0.1; P = 0.08). Addition of acute inflammation (i, t, and v), microvascular inflammation (g and ptc), and inflammation in area of interstitial fibrosis and tubular atrophy scores to chronicity scores (cg ≥ 3 and ci + ct ≥ 3) did not improve model fit. However, a model including dd-cfDNA with cg and ci + ct with covariate-by-time interactions had a better model fit compared with cg and ci + ct alone (likelihood-ratio test statistic = 21.1; df = 2; P < 0.001).

Conclusions

Addition of dd-cfDNA to Banff biopsy scores provided better prognostic assessment over biopsy characteristics alone.

Recurrent antibody-mediated rejection in renal allograft: physician's dilemma

Antibody-mediated rejection is the primary cause of renal allograft failure, and the molecular mechanistic is relatively less known in comparison to cell-mediated rejection. With the advent of new immunological agents, the short-term graft survival outcomes have improved, but the long-term outcomes are still unchanged. We present a case of 47-year-old with end-stage renal disease due to presumed lupus status post deceased donor renal transplantation. The patient developed recurrent (a total of five) antibody-mediated rejections (donor-specific antibody and C4d staining negative) spanning within a year of transplant despite the standard of care therapies. The present case draws attention to the importance of non-HLA antibodies in antibody-mediated rejection and diagnostic tools we can rely on when the histology is inconclusive and the role of new immunological agents.

What have we learned about how to prevent and treat antibody-mediated rejection in kidney transplantation?

Antibody-mediated rejection (ABMR) in kidney transplantation is a major cause of late graft loss, and despite all efforts to date the "standard of care" remains plasmapheresis, IVIg, and steroids, which itself is based on low quality evidence. This review focuses on the risk factors leading to memory and de novo donor-specific antibody (DSA)-associated ABMR, the optimal prevention strategies for ABMR, and advances in  adjunctive and emerging therapies for ABMR. Because new agents require regulatory approval via a Phase 3 randomized control trial (RCT), an overview of progress in innovative trial design for ABMR is provided. Finally, based on the insights gained in the biology of ABMR, current knowledge gaps are identified for future research that could significantly affect our understanding of how to optimally treat ABMR.

Use of biomarkers to improve immunosuppressive drug development and outcomes in renal organ transplantation: A meeting report

On September 27-28, 2018 the Food and Drug Administration (FDA) and the Critical Path Institute's Transplant Therapeutics Consortium convened a public workshop titled "Evidence-Based Treatment Decisions in Transplantation: The Right Dose & Regimen for the Right Patient/Individualized Treatment." The workshop facilitated cooperative engagement of transplant community stakeholders, including pharmaceutical industry, academic researchers, clinicians, patients, and regulators to discuss methods to advance the development of novel immunosuppressive drugs for use in solid organ transplantation. Day 1 focused on the utility of biomarkers in drug development_, with considerations for seeking regulatory endorsement for use in clinical trials. Biomarkers add value to drug development by improving patient selection criteria, safety monitoring, endpoint selection, and more. Regulatory endorsement through the FDA Biomarker Qualification Program encourages the use of biomarkers in drug development by instilling confidence and consistency in biomarker interpretation across trials. Public-private partnerships or consortia allow stakeholders to share expertise, resources, and data in pursuit of biomarker qualification. Biomarkers relevant to pretransplant risk assessment, early posttransplant care, and assessment of immune response, immunosuppressive drug efficacy, and graft function as discussed on day 1 of the workshop are described.

Validation of the Kidney Failure Risk Equation in Kidney Transplant Recipients

Background:

Predicting allograft failure in kidney transplant recipients can help plan renal replacement therapy and guide patient-provider communication. The kidney failure risk equation (KFRE) accurately predicts the need for dialysis in patients with chronic kidney disease (CKD), but has not been validated in kidney transplant recipients.

Objective:

We sought to validate the 4-variable KFRE (age, sex, estimated glomerular filtration rate [eGFR], and urine albumin-to-creatinine ratio [ACR]) for prediction of 2- and 5-year death-censored allograft failure.

Design:

Retrospective cohort study.

Setting:

Four independent North American Cohorts from Ontario, Canada; Alberta, Canada; Manitoba, Canada; and Wisconsin, United States, between January 1999 and December 2017.

Patients:

Adult kidney transplant patients at 1-year posttransplantation.

Measurements:

Kidney failure risk as measured by the KFRE (eGFR, urine ACR, age, and sex).

Methods:

We included all adult patients who had at least 1 serum creatinine and at least 1 urine ACR measurement approximately 1 year following kidney transplantation. The performance of the KFRE was evaluated using the area under the receiver operating characteristic curve (C-statistic). C-statistics from the 4 cohorts were meta-analyzed using random-effects models.

Results:

A total of 3659 patients were included. Pooled C-statistics were good in the entire population, at 0.81 (95% confidence interval: 0.72-0.91) for the 2-year KFRE and 0.73 (0.67-0.80) for the 5-year KFRE. Discrimination improved among patients with poorer kidney function (eGFR < 45 mL/min/1.73 m²), with a C-statistic of 0.88 (0.78-0.98) for the 2-year KFRE and 0.83 (0.74-0.91) for the 5-year KFRE.

Limitations:

The KFRE does not predict episodes of acute rejection and there was heterogeneity between cohorts.

Conclusions:

The KFRE accurately predicts kidney failure in kidney transplant recipients at 1-year posttransplantation. Further validation in larger cohorts with longer follow-up times can strengthen the case for clinical implementation.

Prognostic value for long-term graft survival of estimated glomerular filtration rate and proteinuria quantified at 3 months after kidney transplantation

Background

The estimated glomerular filtration rate (eGFR) measured at 1 year is the usual benchmark applied in kidney transplantation (KT). However, acting on earlier eGFR values could help in managing KT during the first post-operative year. We aimed to assess the prognostic value for long-term graft survival of the early (3 months) quantification of eGFR and proteinuria following KT.

Methods

The 3-, 6- and 12-month eGFR using the Modified Diet in Renal Disease equation (eGFR_MDRD) was determined and proteinuria was measured in 754 patients who underwent their first KT between 2000 and 2010 (with a mean follow-up of 8.3 years) in our centre. Adjusted associations with graft survival were estimated using a multivariable Cox model. The predictive accuracy was estimated using the C-index and net reclassification index. These same analyses were measured in a multicentre validation cohort of 1936 patients.

Results

Both 3-month eGFR_MDRD and proteinuria were independent predictors of return to dialysis (all P < 0.05) and there was a strong correlation between eGFR at 3 and 12 months (Spearman’s ρ = 0.76). The predictive accuracy of the 3-month eGFR was within a similar range and did not differ significantly from the 12-month eGFR in either the derivation cohort [C-index 62.6 (range 57.2–68.1) versus 66.0 (range 60.1–71.9), P = 0.41] or the validation cohort [C-index 69.3 (range 66.4–72.1) versus 71.7 (range 68.7–74.6), P = 0.25].

Conclusion

The 3-month eGFR was a valuable predictor of the long-term return to dialysis whose predictive accuracy was not significantly less than that of the 12-month eGFR in multicentre cohorts totalling >2500 patients. Three-month outcomes may be useful in randomized controlled trials targeting early therapeutic interventions.

Artificial intelligence and machine learning in nephropathology

Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.

Emerging biomarkers in chronic lung allograft dysfunction

Introduction: Lung transplantation remains an important treatment for patients with end stage lung disease. Chronic lung allograft dysfunction (CLAD) remains the greatest limiting factor for long term survival. As the diagnosis of CLAD is based on pulmonary function tests, significant lung injury is required before a diagnosis is feasible, likely when irreversible damage has already occurred. Therefore, research is ongoing for early CLAD recognition, with biomarkers making up a substantial amount of this research.Areas covered: The purpose of this review is to describe available biomarkers, focusing on those which aid in predicting CLAD and distinguishing between different CLAD phenotypes. We describe biomarkers presenting in bronchial alveolar lavage (BAL) as well as circulating in peripheral blood, both of which offer an appealing alternative to lung biopsy.Expert opinion: Development of CLAD involves complex, multiple immune and nonimmune mechanisms. Therefore, evaluation of potential CLAD biomarkers serves a dual purpose: clinically, the goal remains early detection and identification of patients at increased risk. Simultaneously, biomarkers offer insight into the different mechanisms involved in the pathophysiology of CLAD, leading to the development of possible interventions. The ultimate goal is the development of both preventive and early intervention strategies for CLAD to improve the overall survival of our lung transplant recipients.