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

Comparing the prognostic performance of iBOX and biopsy-proven acute rejection for long-term kidney graft survival

Biopsy-proven acute rejection (BPAR) occurs in approximately 10% of kidney transplant recipients in the first year, making superiority trials unfeasible. iBOX, a quantitative composite of estimated glomerular filtration rate, proteinuria, antihuman leukocyte antigen donor-specific antibody, and + full/- abbreviated kidney histopathology, is a new proposed surrogate endpoint. BPAR's prognostic ability was compared with iBOX in a pooled cohort of 1534 kidney transplant recipients from 4 data sets, including 2 prospective randomized controlled trials. Discrimination analyses showed mean c-statistic differences between both iBOX compared with BPAR of 0.25 (95% confidence interval: 0.17-0.32) for full iBOX and 0.24 (95% confidence interval: 0.16-0.32) for abbreviated iBOX, indicating statistically significantly higher c-statistic values for the iBOX prognosis of death-censored graft survival. Mean (± standard error) c-statistics were 0.81 ± 0.03 for full iBOX, 0.80 ± 0.03 for abbreviated iBOX, and 0.57 ± 0.03 for BPAR. In calibration analyses, predicted graft loss events from both iBOX models were not significantly different from those observed. However, for BPAR, the predicted events were significantly (P < .01) different (observed: 64; predicted: 70; full iBOX: 76; abbreviated iBOX: 173 BPAR). IBOX at 1-year posttransplant is superior to BPAR in the first year posttransplant in graft loss prognostic performance, providing valuable additional information and facilitating the demonstration of superiority of novel immunosuppressive regimens.

Early and Late Microvascular Inflammation Have Differing Etiological Causes and Clinical Expression

BACKGROUND

Microvascular inflammation (MVI) is an important pathological feature of antibody-mediated rejection (AMR). How posttransplant time affects its clinicopathological expression is little understood.

METHODS

This retrospective, single-center study screened 3398 kidney transplant biopsies and dichotomized 202 MVI ≥ 2 (Banff glomerulitis + peritubular capillaritis ≥ 2) samples by 9-mo median incidence time for comparison.

RESULTS

The prevalence of MVI ≥ 2 was 12.4% in transplant kidneys, which failed more frequently than propensity-matched normal controls (n = 202; P < 0.001). Epidemiological risk factors for early MVI ≥ 2 were delayed graft function, prior AMR, and circulating donor-specific antibodies (DSAs+). Prior recipient sensitization occurred in 72.3%. Early MVI ≥ 2 was classified AMR in 65.3% and cellular rejection in 34.7%, and demonstrated excellent functional recovery and graft survival comparable to normal control kidneys. Late MVI ≥ 2 was predicted by younger (18 = 29 y) age, female recipient, living-donation, prior methylprednisolone, cyclosporine (versus tacrolimus, levels <5 ng/mL), absent antiproliferative therapy, and DSA+ using multivariable epidemiological modeling. Nonadherence caused 49.5%, with iatrogenic minimization responsible for 47.5%, usually for recipient infection. Late MVI ≥ 2 was because of AMR in 93.1%, and characterized by greater interstitial fibrosis, tubular atrophy, complement degradation split-product 4d (C4d) staining of peritubular capillaries+, endothelial C4d staining of glomerular capillaries+, transplant glomerulopathy and vasculopathy scores, DSA strength, and graft failure than early MVI ≥ 2 or normal transplant kidneys. Death-censored graft survival in 149 unique MVI ≥ 2 kidneys was independently determined by nonadherence, serum creatinine, proteinuria, DSA+, Banff C4d staining of peritubular capillaries+, and chronic interstitial fibrosis scores. MVI score and time lost significance using multivariable Cox regression.

CONCLUSIONS

The changing expression of MVI ≥ 2 over time is best explained by differences in underimmunosuppression and microvascular injury from AMR impacting allograft function and survival.

Detection of Subclinical Rejection in Pediatric Kidney Transplantation: Current and Future Practices

INTRODUCTION

The successes in the field of pediatric kidney transplantation over the past 60 years have been extraordinary. Year over year, there have been significant improvements in short-term graft survival. However, improvements in longer-term outcomes have been much less apparent. One important contributor has been the phenomenon of low-level rejection in the absence of clinical manifestations-so-called subclinical rejection (SCR).

METHODS

Traditionally, rejection has been diagnosed by changes in clinical parameters, including but not limited to serum creatinine and proteinuria. This review examines the shortcomings of this approach, the effects of SCR on kidney allograft outcome, the benefits and drawbacks of surveillance biopsies to identify SCR, and new urine and blood biomarkers that define the presence or absence of SCR.

RESULTS

Serum creatinine is an unreliable index of SCR. Surveillance biopsies are the method most utilized to detect SCR. However, these have significant drawbacks. New biomarkers show promise. These biomarkers include blood gene expression profiles and donor derived-cell free DNA; urine gene expression profiles; urinary cytokines, chemokines, and metabolomics; and other promising blood and urine tests.

CONCLUSION

Specific emphasis is placed on studies carried out in pediatric kidney transplant recipients.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03719339.

Management of Failing Kidney and Pancreas Transplantations

Survival rates for allografts have improved over the last 2 decades, yet failing allografts remains a challenge in the field of transplant. The risks of mortality and morbidity associated with failed allografts are compounded by infectious complications and metabolic abnormalities, emphasizing the need for a standardized approach to management. Management of failing allografts lacks consensus, highlighting the need for unified protocols to guide treatment protocols and minimize risks with postdialysis initiation. The decision to wean off immunosuppression depends on various factors, including living donor availability and infectious risks, necessitating improved coordination of care and a standard guideline. Treatment of failed pancreas focuses on glycemic control, with insulin as the mainstay, while considering surgical interventions such as graft pancreatectomy in advanced symptomatic cases. Navigating the complexities of failed allograft management demands a multidisciplinary approach and standardized stepwise protocol. Addressing the gaps in management plans for failing allografts and employing a systematic approach to transplant decisions will enhance patient outcomes and facilitate informed decision-making.

IFTA Foci Density: An Unrecognized Highly Prognostic Measurement of Fibrosis in Kidney Transplant Biopsies

Key Points

Background

Contraction of interstitial fibrosis and tubular atrophy (IFTA) may cause %IFTA to under-represent the severity of nephron loss. Higher density of IFTA foci is an important predictor of progressive CKD in native kidneys independent of %IFTA.

Methods

We studied kidney transplant recipients transplanted between 2000 and 2013 who had a 5-year surveillance kidney biopsy and subsequent follow-up. Banff ci score (interstitial fibrosis) was obtained from the pathology reports. After digitizing the biopsies, we traced cortex area and each distinct IFTA focus on a single trichrome-stained section. Percent IFTA area and IFTA foci density (count of IFTA foci/cortex area) were calculated. Cox models assessed the risk of death-censored graft failure after the 5-year biopsy with Banff ci score, morphometric %IFTA, and IFTA foci density.

Results

There were 58 death-censored allograft failures among 835 kidney recipients during the 5 years of follow-up. Biopsies from grafts that failed had higher mean Banff ci score (1.5 versus 0.7, P < 0.0001), %IFTA (22.6% versus 7.0%, P < 0.0001), and IFTA foci density (1.3/mm2 versus 0.4/mm2, P < 0.0001). After adjusting for other Banff scores or clinical variables, Banff ci did not correlate with allograft failure, but both higher %IFTA (hazard ratio = 1.56, P < 0.0001) and higher IFTA foci density (hazard ratio = 2.34, P < 0.0001) did. All but four allograft failures by 10 years had biopsies in the top quartile of either %IFTA or IFTA foci density at 5 years. A model using just these two morphometric measures without clinical characteristics resulted in a c-statistic of 0.891 with respect to allograft failure.

Conclusions

Morphometric characterization of IFTA foci density is a strong predictor of death-censored allograft failure not captured in current Banff classification for grading of kidney fibrosis.

Proceedings of the 2024 Transplant AI Symposium

With recent advancements in deep learning (DL) techniques, the use of artificial intelligence (AI) has become increasingly prevalent in all fields. Currently valued at 9.01 billion USD, it is a rapidly growing market, projected to increase by 40% per annum. There has been great interest in how AI could transform the practice of medicine, with the potential to improve all healthcare spheres from workflow management, accessibility, and cost efficiency to enhanced diagnostics with improved prognostic accuracy, allowing the practice of precision medicine. The applicability of AI is particularly promising for transplant medicine, in which it can help navigate the complex interplay of a myriad of variables and improve patient care. However, caution must be exercised when developing DL models, ensuring they are trained with large, reliable, and diverse datasets to minimize bias and increase generalizability. There must be transparency in the methodology and extensive validation of the model, including randomized controlled trials to demonstrate performance and cultivate trust among physicians and patients. Furthermore, there is a need to regulate this rapidly evolving field, with updated policies for the governance of AI-based technologies. Taking this in consideration, we summarize the latest transplant AI developments from the Ajmera Transplant Center's inaugural symposium.

Chronic Rejection After Kidney Transplantation

In kidney transplantation, ongoing alloimmune processes-commonly triggered by HLA incompatibilities-can trigger chronic transplant rejection, affecting the microcirculation and the tubulointerstitium. Continuous inflammation may lead to progressive, irreversible graft injury, culminating in graft dysfunction and accelerated transplant failure. Numerous experimental and translational studies have delineated a complex interplay of different immune mechanisms driving rejection, with antibody-mediated rejection (AMR) being an extensively studied rejection variant. In microvascular inflammation, a hallmark lesion of AMR, natural killer (NK) cells have emerged as pivotal effector cells. Their essential role is supported by immunohistologic evidence, bulk and spatial transcriptomics, and functional genetics. Despite significant research efforts, a substantial unmet need for approved rejection therapies persists, with many trials yielding negative outcomes. However, several promising therapies are currently under investigation, including felzartamab, a monoclonal antibody targeting the surface molecule CD38, which is highly expressed in NK cells and antibody-producing plasma cells. In an exploratory phase 2 trial in late AMR, this compound has demonstrated potential in resolving molecular and morphologic rejection activity and injury, predominantly by targeting NK cell effector function. These findings inspire hope for effective treatments and emphasize the necessity of further pivotal trials focusing on chronic transplant rejection.

Predicting graft survival in paediatric kidney transplant recipients using machine learning

BACKGROUND

Identification of factors that affect graft survival in kidney transplantation can increase graft survival and reduce mortality. Artificial intelligence modelling enables impartial evaluation of clinician bias. This study aimed to examine factors that affect the survival of grafts in paediatric kidney transplantation through the use of machine learning.

METHODS

A retrospective review was conducted on records of paediatric patients who underwent kidney transplantation between 1994 and 2021 and had post-transplant follow-up > 12 months. The nearest neighbour method was used to impute missing fields from a total of 48 variables in the dataset. Models including Naive Bayes, logistic regression, support vector machine (SVM), multi-layer perceptron, and XGBoost were trained to predict graft survival. The study used 80% of the patients for training and the remaining 20% for testing. Modelling success was evaluated based on accuracy and F1 score metrics.

RESULTS

The study analysed 465 kidney transplant recipients. Of these, 56.7% were male. The mean age at transplantation was 12.08 ± 5.01 years. Of the kidney transplants, 73.1% (n = 339) were from living donors, 34.5% (n = 160) were pre-emptive transplants, and 2.2% (n = 10) were second-time transplants. The machine learning model identified several features associated with graft survival, including antibody-mediated rejection (+ 0.7), acute cellular rejection (+ 0.66), eGFR at 3 years (+ 0.43), eGFR at 5 years (+ 0.34), pre-transplant peritoneal dialysis (+ 0.2), and cadaveric donor (+ 0.2). The successes of the logistic regression and SVM models were similar. The F1 score was 91.9%, and accuracy was 96.5%.

CONCLUSION

Machine learning can be used to identify factors that affect graft survival in kidney transplant recipients. By expanding similar studies, risk maps can be created prior to transplantation.

Efficacy and Safety of Bleselumab in Preventing the Recurrence of Primary Focal Segmental Glomerulosclerosis in Kidney Transplant Recipients: A Phase 2a, Randomized, Multicenter Study

BACKGROUND

Focal segmental glomerulosclerosis (FSGS) is a common cause of end-stage kidney disease and frequently recurs after kidney transplantation. Recurrent FSGS (rFSGS) is associated with poor allograft and patient outcomes. Bleselumab, a fully human immunoglobulin G4 anti-CD40 antagonistic monoclonal antibody, disrupts CD40-related processes in FSGS, potentially preventing rFSGS.

METHODS

A phase 2a, randomized, multicenter, open-label study of adult recipients (aged ≥18 y) of a living or deceased donor kidney transplant with a history of biopsy-proven primary FSGS. The study assessed the efficacy of bleselumab combined with tacrolimus and corticosteroids as maintenance immunosuppression in the prevention of rFSGS >12 mo posttransplantation, versus standard of care (SOC) comprising tacrolimus, mycophenolate mofetil, and corticosteroids. All patients received basiliximab induction. The primary endpoint was rFSGS, defined as proteinuria (protein-creatinine ratio ≥3.0 g/g) with death, graft loss, or loss to follow-up imputed as rFSGS, through 3 mo posttransplant.

RESULTS

Sixty-three patients were followed for 12 mo posttransplantation. Relative decrease in rFSGS occurrence through 3 mo with bleselumab versus SOC was 40.7% (95% confidence interval, -89.8 to 26.8; P = 0.37; absolute decrease 12.7% [95% confidence interval, -34.5 to 9.0]). Central-blinded biopsy review found relative (absolute) decreases in rFSGS of 10.9% (3.9%), 17.0% (6.2%), and 20.5% (7.5%) at 3, 6, and 12 mo posttransplant, respectively; these differences were not statistically significant. Adverse events were similar for both treatments. No deaths occurred during the study.

CONCLUSIONS

In at-risk kidney transplant recipients, bleselumab numerically reduced proteinuria occurrence versus SOC, but no notable difference in occurrence of biopsy-proven rFSGS was observed.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

Unveiling systemic responses in kidney transplantation: interplay between the allograft transcriptome and serum proteins

Immunity, as defined by systems biology, encompasses a holistic response throughout the body, characterized by intricate connections with various tissues and compartments. However, this concept has been rarely explored in kidney transplantation. In this proof-of-concept study, we investigated a direct association between the allograft phenotype and serum protein signatures. Time-matched samples of graft biopsies and blood serum were collected in a heterogeneous cohort of kidney-transplanted patients (n = 15) for bulk RNA sequencing and proteomics, respectively. RNA transcripts exhibit distinct and reproducible, coregulated gene networks with specific functional profiles. We measured 159 serum proteins and investigated correlations with gene expression networks. Two opposing axes-one related to metabolism and the other to inflammation-were identified. They may represent a biological continuum between the allograft and the serum and correlate with allograft function, but not with interstitial fibrosis or proteinuria. For signature validation, we used two independent proteomic data sets (n = 21). Our findings establish a biological link between the allograft transcriptome and the blood serum proteome, highlighting systemic immune effects in kidney transplantation and offering a promising framework for developing allograft-linked biomarkers.

Application of spatial-omics to the classification of kidney biopsy samples in transplantation

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

Recent Insights in Noninvasive Diagnostic for the Assessment of Kidney and Cardiovascular Outcome in Kidney Transplant Recipients

Kidney transplantation improves quality of life and prolongs survival of patients with end-stage kidney disease. However, kidney transplant recipients present a higher risk for cardiovascular events compared to the general population. Risk assessment for graft failure as well as cardiovascular events is still based on invasive procedures. Biomarkers in blood and urine, but also new diagnostic approaches like genetic or molecular testing, can be useful tools to monitor graft function and to identify patients of high cardiovascular risk. Many biomarkers have been introduced, whereas most of these biomarkers have not been implemented in clinical routine. Here, we discuss recent developments in biomarkers and diagnostic models in kidney transplant recipients. Because many factors impact graft function and cardiovascular risk, it is most likely that no biomarker will meet the highest demands and standards. We advocate to shift focus to the identification of patients benefitting from molecular and genetic testing as well as from analysis of more specific biomarkers instead of finding one biomarker fitting to all patients.

Challenges and opportunities for designing clinical trials for antibody mediated rejection

Significant progress has been made in kidney transplantation, with 1-year graft survival nearing 95%. However, long-term allograft survival remains suboptimal, with a 10-year overall graft survival rate of only 53.6% for deceased donor transplant recipients. Chronic active antibody-mediated rejection (ABMR) is a leading cause of death-censored graft loss, yet no therapy has demonstrated efficacy in large, randomized trials, despite substantial investment from pharmaceutical companies. Several clinical trials aimed to treat chronic ABMR in the past decade have yielded disappointing results or were prematurely terminated, attributed to factors including incomplete understanding of disease mechanisms, heterogeneous patient populations with comorbidities, slow disease progression, and limited patient numbers. This review aims to discuss opportunities for improving retrospective and prospective studies of ABMR, focusing on addressing heterogeneity, outcome measurement, and strategies to enhance patient enrollment to inform study design, data collection, and reporting.

Preoperative Risk Assessment of Early Kidney Graft Loss

Background

A large proportion of potential organ donors are not utilized for kidney transplantation out of risk of early allograft loss because of donor-related characteristics. These can be summarized using kidney donor profile index (KDPI). Because KDPI affects the choice of the recipient, the predictive ability of KDPI is tied to recipient attributes. These have been questioned to explain most of the predictive ability of KDPI. This study aims to quantify the effect of the donor on early graft loss (EGL) by accounting for nonrandom allocation.

Methods

This study included patients undergoing kidney transplantation from deceased donors between 2014 and 2020 from the Scientific Registry of Transplantation Recipients. EGL, defined as a return to dialysis or retransplantation during the first posttransplant year, was the primary endpoint. Nonrandom allocation and donor-recipient matching by KDPI necessitated the use of inverse probability treatment weighting, which served to assess the effect of KDPI and mitigate selection bias in a weighted Cox regression model.

Results

The study comprised 89 290 transplantations in 88 720 individual patients. Inverse probability treatment weighting resulted in a good balance of recipient covariates across values of continuous KDPI. Weighted analysis showed KDPI to be a significant predictor for short-term outcomes. A comparable (in terms of age, time on dialysis, previous transplants, gender, diabetes status, computed panel-reactive antibodies, and HLA mismatches) average recipient, receiving a kidney from a donor with KDPI 40-60 had a 3.5% risk of EGL increased to a risk of 7.5% if received a kidney from a KDPI >95 donor (hazard ratio, 2.3; 95% confidence interval, 1.9-2.7). However, for all-cause survival KDPI was less influential.

Conclusions

The predictive ability of KDPI does not stem from recipient confounding alone. In this large sample-sized study, modeling methods accounting for nonindependence of recipient selection verify graft quality to effectively predict short-term transplantation outcomes.

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation

Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many years. Nevertheless, despite the question of whether AI models add value to traditional modeling approaches, such as regression models, their "black box" nature is one of the factors that have hindered the translation from research to clinical practice. Several techniques that make such models understandable to humans were developed with the promise of increasing transparency in the support of medical decision-making. These techniques should help AI to close the gap between theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulations. But is this also happening in the field of kidney transplantation? This review reports the use and explanation of "black box" models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other related outcomes after kidney transplantation. In particular, we emphasize the discussion on the need (or not) to explain ML models for biological discovery and clinical implementation in kidney transplantation. We also discuss promising future research paths for these computational tools.

Very Low Frequency of Pathological Findings in One-year Protocol Biopsies of Uneventful Standard Risk Kidney Transplant Recipients: Results From the Nordic Protocol Biopsy Study

Background

The clinical significance of kidney transplant protocol biopsies has been debated. We studied the frequency of borderline changes and T cell-mediated rejection (TCMR) in 1-y protocol biopsies in standard risk kidney transplant recipients.

Methods

Consecutive non-HLA-sensitized recipients of kidney transplants between 2006 and 2017, who underwent a protocol biopsy at 1 y in 2 national transplant centers were studied retrospectively (N = 1546). Donor-specific HLA antibodies (DSAs), graft function (plasma creatinine), and proteinuria were measured at the time of 1-y protocol biopsy. The occurrence of subclinical acute TCMR (i2t2v0 or higher) or borderline changes suspicious of TCMR (i1t1v0 or higher) in the protocol biopsy was studied, together with frequency of findings causing changes in the composite score iBox.

Results

Subclinical acute TCMR was detected in 30 of 1546 (1.9%) of the protocol biopsies, and borderline or TCMR in 179 of 1546 (12%). Among patients with no history of acute rejection, and no proteinuria or DSA, TCMR was detected in only 1 of 974 (0.1%) and borderline or TCMR in only 48 of 974 (4.9%) patients at 1 y. In the absence of proteinuria (<30 mg/g, or equivalent as measured with a negative dipstick proteinuria) or DSA, or history of acute rejection, only 50 of 974 (5.1%) biopsies showed any lesions significant for the iBox score.

Conclusions

The likelihood of pathological findings in 1-y protocol biopsies in non-HLA-sensitized patients without previous immunological events is low. Clinical usefulness of protocol biopsies seems limited in these patients.

Long-Term Outcomes after Conversion to a Belatacept-Based Immunosuppression in Kidney Transplant Recipients

BACKGROUND

Conversion to a belatacept-based immunosuppression is currently used as a calcineurin inhibitor (CNI) avoidance strategy when the CNI-based standard-of-care immunosuppression is not tolerated after kidney transplantation. However, there is a lack of evidence on the long-term benefit and safety after conversion to belatacept.

METHODS

We prospectively enrolled 311 kidney transplant recipients from 2007 to 2020 from two referral centers, converted from CNI to belatacept after transplant according to a prespecified protocol. Patients were matched at the time of conversion to patients maintained with CNIs, using optimal matching. The primary end point was death-censored allograft survival at 7 years. The secondary end points were patient survival, eGFR, and safety outcomes, including serious viral infections, immune-related complications, antibody-mediated rejection, T-cell-mediated rejection, de novo anti-HLA donor-specific antibody, de novo diabetes, cardiovascular events, and oncologic complications.

RESULTS

A total of 243 patients converted to belatacept (belatacept group) were matched to 243 patients maintained on CNIs (CNI control group). All recipient, transplant, functional, histologic, and immunologic parameters were well balanced between the two groups with a standardized mean difference below 0.05. At 7 years post-conversion to belatacept, allograft survival was 78% compared with 63% in the CNI control group ( P < 0.001 for log-rank test). The safety outcomes showed a similar rate of patient death (28% in the belatacept group versus 36% in the CNI control group), active antibody-mediated rejection (6% versus 7%), T-cell-mediated rejection (4% versus 4%), major adverse cardiovascular events, and cancer occurrence (9% versus 11%). A significantly higher rate of de novo proteinuria was observed in the belatacept group as compared with the CNI control group (37% versus 21%, P < 0.001).

CONCLUSIONS

This real-world evidence study shows that conversion to belatacept post-transplant was associated with lower risk of graft failure and acceptable safety outcomes compared with patients maintained on CNIs.

CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER

Long-term Outcomes after Conversion to Belatacept, NCT04733131 .

The status and influencing factors of fatigue in kidney transplant recipients based on the theory of unpleasant symptoms: A cross-sectional study in China

AIMS

This study describes the incidence of fatigue in kidney transplant recipients and analyses the relationship between physiological factors, psychological factors, situational factors and fatigue in kidney transplant recipients.

BACKGROUND

Fatigue, as a common symptom after kidney transplantation, is affected by many factors, but the influence of some factors on the fatigue of kidney transplant recipients is still controversial.

DESIGN

This cross-sectional study was designed based on the theory of unpleasant symptoms.

METHODS

Our survey involved 307 participants attending the kidney transplant outpatient clinic of a tertiary Class A hospital (Changsha, Hunan, China). Data were collected between February and April 2021 using a structured questionnaire and electronic medical records. Data were analysed using IBM SPSS 25.0 (SPSS Inc.) RESULTS: It was found that the incidence of fatigue in kidney transplant recipients was 53.1%. According to the binary logistic regression analysis, sleep quality, hypokalemia, anxiety, depression and education level were independent risk factors for fatigue in kidney transplant recipients.

CONCLUSION

The incidence of fatigue in kidney transplant recipients was high and was influenced by physical, psychological and situational factors. Clinical nurses should assess fatigue levels in a timely and multidimensional manner in clinical practice and provide effective and scientific guidance about fatigue self-coping and symptom management for kidney transplant recipients.

Investigating the Impact of AI on Shared Decision-Making in Post-Kidney Transplant Care (PRIMA-AI): Protocol for a Randomized Controlled Trial

BACKGROUND

Patients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or retransplantation. Choosing the right kidney replacement therapy after graft loss is an important preference-sensitive decision for kidney transplant recipients. However, the rate of conversations about treatment options after kidney graft loss has been shown to be as low as 13% in previous studies. It is unknown whether the implementation of artificial intelligence (AI)-based risk prediction models can increase the number of conversations about treatment options after graft loss and how this might influence the associated shared decision-making (SDM).

OBJECTIVE

This study aims to explore the impact of AI-based risk prediction for the risk of graft loss on the frequency of conversations about the treatment options after graft loss, as well as the associated SDM process.

METHODS

This is a 2-year, prospective, randomized, 2-armed, parallel-group, single-center trial in a German kidney transplant center. All patients will receive the same routine post-kidney transplant care that usually includes follow-up visits every 3 months at the kidney transplant center. For patients in the intervention arm, physicians will be assisted by a validated and previously published AI-based risk prediction system that estimates the risk for graft loss in the next year, starting from 3 months after randomization until 24 months after randomization. The study population will consist of 122 kidney transplant recipients >12 months after transplantation, who are at least 18 years of age, are able to communicate in German, and have an estimated glomerular filtration rate <30 mL/min/1.73 m2. Patients with multi-organ transplantation, or who are not able to communicate in German, as well as underage patients, cannot participate. For the primary end point, the proportion of patients who have had a conversation about their treatment options after graft loss is compared at 12 months after randomization. Additionally, 2 different assessment tools for SDM, the CollaboRATE mean score and the Control Preference Scale, are compared between the 2 groups at 12 months and 24 months after randomization. Furthermore, recordings of patient-physician conversations, as well as semistructured interviews with patients, support persons, and physicians, are performed to support the quantitative results.

RESULTS

The enrollment for the study is ongoing. The first results are expected to be submitted for publication in 2025.

CONCLUSIONS

This is the first study to examine the influence of AI-based risk prediction on physician-patient interaction in the context of kidney transplantation. We use a mixed methods approach by combining a randomized design with a simple quantitative end point (frequency of conversations), different quantitative measurements for SDM, and several qualitative research methods (eg, records of physician-patient conversations and semistructured interviews) to examine the implementation of AI-based risk prediction in the clinic.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06056518; https://clinicaltrials.gov/study/NCT06056518.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54857.

Tocilizumab in chronic active antibody-mediated rejection: rationale and protocol of an in-progress randomized controlled open-label multi-center trial (INTERCEPT study)

BACKGROUND

Chronic active antibody-mediated rejection (caAMR) in kidney transplants is associated with irreversible tissue damage and a leading cause of graft loss in the long-term. However, the treatment for caAMR remains a challenge to date. Recently, tocilizumab, a recombinant humanized monoclonal antibody directed against the human interleukin-6 (IL-6) receptor, has shown promise in the treatment of caAMR. However, it has not been systematically investigated so far underscoring the need for randomized controlled studies in this area.

METHODS

The INTERCEPT study is an investigator-driven randomized controlled open-label multi-center trial in kidney transplant recipients to assess the efficacy of tocilizumab in the treatment of biopsy-proven caAMR. A total of 50 recipients with biopsy-proven caAMR at least 12 months after transplantation will be randomized to receive either tocilizumab (n = 25) added to our standard of care (SOC) maintenance treatment or SOC alone (n = 25) for a period of 24 months. Patients will be followed for an additional 12 months after cessation of study medication. After the inclusion biopsies at baseline, protocol kidney graft biopsies will be performed at 12 and 24 months. The sample size calculation assumed a difference of 5 ml/year in slope of estimated glomerular filtration rate (eGFR) between the two groups for 80% power at an alpha of 0.05. The primary endpoint is the slope of eGFR at 24 months after start of treatment. The secondary endpoints include assessment of the following at 12, 24, and 36 months: composite risk score iBox, safety, evolution and characteristics of donor-specific antibodies (DSA), graft histology, proteinuria, kidney function assessed by measured GFR (mGFR), patient- and death-censored graft survival, and patient-reported outcomes that include transplant-specific well-being, adherence to immunosuppressive medications and perceived threat of the risk of graft rejection.

DISCUSSION

No effective treatment exists for caAMR at present. Based on the hypothesis that inhibition of IL-6 receptor by tocilizumab will reduce antibody production and reduce antibody-mediated damage, our randomized trial has a potential to provide evidence for a novel treatment strategy for caAMR, therewith slowing the decline in graft function in the long-term.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04561986. Registered on September 24, 2020.

The Banff 2022 Kidney Meeting Work Plan: Data-driven refinement of the Banff Classification for renal allografts

The XVIth Banff Meeting for Allograft Pathology was held in Banff, Alberta, Canada, from September 19 to 23, 2022, as a joint meeting with the Canadian Society of Transplantation. In addition to a key focus on the impact of microvascular inflammation and biopsy-based transcript analysis on the Banff Classification, further sessions were devoted to other aspects of kidney transplant pathology, in particular T cell-mediated rejection, activity and chronicity indices, digital pathology, xenotransplantation, clinical trials, and surrogate endpoints. Although the output of these sessions has not led to any changes in the classification, the key role of Banff Working Groups in phrasing unanswered questions, and coordinating and disseminating results of investigations addressing these unanswered questions was emphasized. This paper summarizes the key Banff Meeting 2022 sessions not covered in the Banff Kidney Meeting 2022 Report paper and also provides an update on other Banff Working Group activities relevant to kidney allografts.

Prognostic Biomarkers in Kidney Transplantation: A Systematic Review and Critical Appraisal

SIGNIFICANCE STATEMENT

Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events. Less than 5% of the studies performed an external validation. The authors also showed the poor transparency reporting and identified a data beautification phenomenon. These findings suggest that there is much wasted research effort in transplant biomarker medical research and highlight the need to produce more rigorous studies so that more biomarkers may be validated and successfully implemented in clinical practice.

BACKGROUND

Despite the increasing number of biomarker studies published in the transplant literature over the past 20 years, demonstrations of their clinical benefit and their implementation in routine clinical practice are lacking. We hypothesized that suboptimal design, data, methodology, and reporting might contribute to this phenomenon.

METHODS

We formed a consortium of experts in systematic reviews, nephrologists, methodologists, and epidemiologists. A systematic literature search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library between January 1, 2005, and November 12, 2022 (PROSPERO ID: CRD42020154747). All English language, original studies investigating the association between a biomarker and kidney allograft outcome were included. The final set of publications was assessed by expert reviewers. After data collection, two independent reviewers randomly evaluated the inconsistencies for 30% of the references for each reviewer. If more than 5% of inconsistencies were observed for one given reviewer, a re-evaluation was conducted for all the references of the reviewer. The biomarkers were categorized according to their type and the biological milieu from which they were measured. The study characteristics related to the design, methods, results, and their interpretation were assessed, as well as reproducible research practices and transparency indicators.

RESULTS

A total of 7372 publications were screened and 804 studies met the inclusion criteria. A total of 1143 biomarkers were assessed among the included studies from blood ( n =821, 71.8%), intragraft ( n =169, 14.8%), or urine ( n =81, 7.1%) compartments. The number of studies significantly increased, with a median, yearly number of 31.5 studies (interquartile range [IQR], 23.8-35.5) between 2005 and 2012 and 57.5 (IQR, 53.3-59.8) between 2013 and 2022 ( P < 0.001). A total of 655 studies (81.5%) were retrospective, while 595 (74.0%) used data from a single center. The median number of patients included was 232 (IQR, 96-629) with a median follow-up post-transplant of 4.8 years (IQR, 3.0-6.2). Only 4.7% of studies were externally validated. A total of 346 studies (43.0%) did not adjust their biomarker for key prognostic factors, while only 3.1% of studies adjusted the biomarker for standard-of-care patient monitoring factors. Data sharing, code sharing, and registration occurred in 8.8%, 1.1%, and 4.6% of studies, respectively. A total of 158 studies (20.0%) emphasized the clinical relevance of the biomarker, despite the reported nonsignificant association of the biomarker with the outcome measure. A total of 288 studies assessed rejection as an outcome. We showed that these rejection studies shared the same characteristics as other studies.

CONCLUSIONS

Biomarker studies in kidney transplantation lack validation, rigorous design and methodology, accurate interpretation, and transparency. Higher standards are needed in biomarker research to prove the clinical utility and support clinical use.

Stratification of Kidney Transplant Recipients Into Five Subgroups Based on Temporal Disease Trajectories

Background

Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Considerable clinical research has focused on improving graft survival and an increasing number of kidney recipients die with a functioning graft. There is a need to improve patient survival and to better understand the individualized risk of comorbidities and complications. Here, we developed a method to stratify recipients into similar subgroups based on previous comorbidities and subsequently identify complications and for a subpopulation, laboratory test values associated with survival.

Methods

First, we identified significant disease patterns based on all hospital diagnoses from the Danish National Patient Registry for 5752 kidney transplant recipients from 1977 to 2018. Using hierarchical clustering, these longitudinal patterns of diseases segregate into 3 main clusters of glomerulonephritis, hypertension, and diabetes. As some recipients are diagnosed with diseases from >1 cluster, recipients are further stratified into 5 more fine-grained trajectory subgroups for which survival, stratified complication patterns as well as laboratory test values are analyzed.

Results

The study replicated known associations indicating that diabetes and low levels of albumin are associated with worse survival when investigating all recipients. However, stratification of recipients by trajectory subgroup showed additional associations. For recipients with glomerulonephritis, higher levels of basophils are significantly associated with poor survival, and these patients are more often diagnosed with bacterial infections. Additional associations were also found.

Conclusions

This study demonstrates that disease trajectories can confirm known comorbidities and furthermore stratify kidney transplant recipients into clinical subgroups in which we can characterize stratified risk factors. We hope to motivate future studies to stratify recipients into more fine-grained, homogenous subgroups to better discover associations relevant for the individual patient and thereby enable more personalized disease-management and improve long-term outcomes and survival.

Creatinine-cystatin C ratio and death with a functioning graft in kidney transplant recipients

Death with a functioning graft is important cause of graft loss after kidney transplantation. However, little is known about factors predicting death with a functioning graft among kidney transplant recipients. In this study, we evaluated the association between post-transplant creatinine-cystatin C ratio and death with a functioning graft in 1592 kidney transplant recipients. We divided the patients into tertiles based on sex-specific creatinine-cystatin C ratio. Among the 1592 recipients, 39.5% were female, and 86.1% underwent living-donor kidney transplantation. The cut-off value for the lowest creatinine-cystatin C ratio tertile was 0.86 in males and 0.73 in females. The lowest tertile had a significantly lower 5-year patient survival rate and was independently associated with death with a functioning graft (adjusted hazard ratio 2.574, 95% confidence interval 1.339-4.950, P < 0.001). Infection was the most common cause of death in the lowest tertile group, accounting for 62% of deaths. A low creatinine-cystatin C ratio was significantly associated with an increased risk of death with a functioning graft after kidney transplantation.

The power of partnership: Exploring collaboration dynamics in U.S. transplant research

INTRODUCTION

Collaboration is one of the hallmarks of academic research. This study analyzes collaboration patterns in U.S. transplant research, examining publication trends, productive institutions, co-authorship networks, and citation patterns in high-impact transplant journals.

METHODS

4,265 articles published between 2012 and 2021 were analyzed using scientometric tools, logistic regression, VantagePoint software, and Gephi software for network visualization.

RESULTS

16,003 authors from 1,011 institutions and 59 countries were identified, with Harvard, Johns Hopkins, and University of Pennsylvania contributing the most papers. Odds of international collaboration significantly increased over time (OR 1.03; p ​= ​0.040), while odds of citation in single-institution collaborations decreased (OR 0.99; p ​= ​0.016). Five major scientific communities and central institutions (Harvard University and University of Pittsburgh) connecting them were identified, revealing interconnected research clusters.

CONCLUSIONS

Collaboration enhances knowledge exchange and research productivity, with an increasing trend of institutional and international collaboration in U.S. transplant research. Understanding this community is essential for promoting research impact and forming strategic partnerships.

Clinical Decision Support Systems Used in Transplantation: Are They Tools for Success or an Unnecessary Gadget? A Systematic Review

Although clinical decision support systems (CDSSs) have been used since the 1970s for a wide variety of clinical tasks including optimization of medication orders, improved documentation, and improved patient adherence, to date, no systematic reviews have been carried out to assess their utilization and efficacy in transplant medicine. The aim of this study is to systematically review studies that utilized a CDSS and assess impact on patient outcomes. A total of 48 articles were identified as meeting the author-derived inclusion criteria, including tools for posttransplant monitoring, pretransplant risk assessment, waiting list management, immunosuppressant management, and interpretation of histopathology. Studies included 15 984 transplant recipients. Tools aimed at helping with transplant patient immunosuppressant management were the most common (19 studies). Thirty-four studies (85%) found an overall clinical benefit following the implementation of a CDSS in clinical practice. Although there are limitations to the existing literature, current evidence suggests that implementing CDSS in transplant clinical settings may improve outcomes for patients. Limited evidence was found using more advanced technologies such as artificial intelligence in transplantation, and future studies should investigate the role of these emerging technologies.

NephroCAGE-German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study

BACKGROUND

Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures.

OBJECTIVE

The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models.

METHODS

To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients.

RESULTS

We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023.

CONCLUSIONS

For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48892.

Early kinetics of donor-derived cell-free DNA after transplantation predicts renal graft recovery and long-term function

BACKGROUND

Ischemia-reperfusion injury (IRI) upon transplantation is one of the most impactful events that the kidney graft suffers during its life. Its clinical manifestation in the recipient, delayed graft function (DGF), has serious prognostic consequences. However, the different definitions of DGF are subject to physicians' choices and centers' policies, and a more objective tool to quantify IRI is needed. Here, we propose the use of donor-derived cell-free DNA (ddcfDNA) for this scope.

METHODS

ddcfDNA was assessed in 61 kidney transplant recipients of either living or deceased donors at 24 h, and 7, 14 and 30 days after transplantation using the AlloSeq cfDNA Kit (CareDx, San Francisco, CA, USA). Patients were followed-up for 6 months and 7-year graft survival was estimated through the complete and functional iBox tool.

RESULTS

Twenty-four-hour ddcfDNA was associated with functional DGF [7.20% (2.35%-15.50%) in patients with functional DGF versus 2.70% (1.55%-4.05%) in patients without it, P = .023] and 6-month estimated glomerular filtration rate (r = -0.311, P = .023). At Day 7 after transplantation, ddcfDNA was associated with dialysis duration in DGF patients (r = 0.612, P = .005) and worse 7-year iBox-estimated graft survival probability (β -0.42, P = .001) at multivariable analysis. Patients with early normalization of ddcfDNA (<0.5% at 1 week) had improved functional iBox-estimated probability of graft survival (79.5 ± 16.8%) in comparison with patients with 7-day ddcfDNA ≥0.5% (67.7 ± 24.1%) (P = .047).

CONCLUSIONS

ddcfDNA early kinetics after transplantation reflect recovery from IRI and are associated with short-, medium- and long-term graft outcome. This may provide a more objective estimate of IRI severity in comparison with the clinical-based definitions of DGF.

Predictors of graft failure after first detection of de novo donor-specific HLA antibodies in kidney transplant recipients

BACKGROUND

De novo donor-specific antibodies (dnDSAs) may cause antibody-mediated rejection and graft dysfunction. Little is known about the clinical course after first detection of dnDSAs during screening in asymptomatic patients. We aimed to assess the value of estimated glomerular filtration rate (eGFR) and proteinuria to predict graft failure in patients with dnDSAs and their potential utility as surrogate endpoints.

METHODS

All 400 kidney transplant recipients with dnDSAs at our centre (1 March 2000-31 May 2021) were included in this retrospective study. The dates of graft loss, rejection, doubling of creatinine, ≥30% eGFR decline, proteinuria ≥500 mg/g and ≥1000 mg/g were registered from the first dnDSA appearance.

RESULTS

During 8.3 years of follow-up, graft failure occurred in 33.3% of patients. Baseline eGFR and proteinuria correlated with 5-year graft loss (area under the receiver operating characteristics curve 0.75 and 0.80, P < .001). Creatinine doubled after a median of 2.8 years [interquartile range (IQR) 1.5-5.0] from dnDSA and the time from doubling creatinine to graft failure was 1.0 year (IQR 0.4-2.9). Analysing eGFR reduction ≥30% as a surrogate endpoint (148/400), the time from dnDSA to this event was 2.0 years (IQR 0.6-4.2), with a positive predictive value (PPV) of 45.9% to predict graft loss, which occurred after 2.0 years (IQR 0.8-3.2). The median time from proteinuria ≥500 mg/g and ≥1000 mg/g to graft failure was identical, 1.8 years, with a PPV of 43.8% and 49.0%, respectively. Composite endpoints did not improve PPV. Multivariable analysis showed that rejection was the most important independent risk factor for all renal endpoints and graft loss.

CONCLUSIONS

Renal function, proteinuria and rejection are strongly associated with graft failure in patients with dnDSA and may serve as surrogate endpoints.

Complement and Non-Complement Binding Anti-HLA Antibodies Are Differentially Detected with Different Antigen Bead Assays in Renal Transplant Recipients

Two semi-quantitative, Luminex-based, single-antigen bead (SAB) assays are available to detect anti-HLA antibodies and evaluate their reactivity with complement binding. Sera from 97 patients with positive panel reactive antibody tests (>5%) were analyzed with two SAB tests, Immucor (IC) and One-Lambda (OL), for anti-HLA antibody detection and the evaluation of their complement-binding capacity. IC detected 1608/8148 (mean fluorescent intensity (MFI) 4195 (1995-11,272)) and 1136/7275 (MFI 6706 (2647-13,184)) positive anti-HLA class I and II specificities, respectively. Accordingly, OL detected 1942/8148 (MFI 6185 (2855-12,099)) and 1247/7275 (MFI 9498 (3630-17,702)) positive anti-HLA class I and II specificities, respectively. For the IC assay, 428/1608 (MFI 13,900 (9540-17,999)) and 409/1136 (MFI 11,832 (7128-16,531)) positive class I and II specificities bound C3d, respectively. Similarly, OL detected 485/1942 (MFI 15,452 (9369-23,095)) and 298/1247 (MFI18,852 (14,415-24,707)) C1q-binding class I and II specificities. OL was more sensitive in detecting class I and II anti-HLA antibodies than IC was, although there was no significant difference in the number of class II specificities per case. MFI was higher for complement vs. non-complement-binding anti-HLA antibodies in both assays. Both methods were equal in detecting complement-binding anti-HLA class I antibodies, whereas the C3d assay was more sensitive in detecting complement-binding anti-HLA class II antibodies.

Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence

The ability to accurately predict long-term kidney transplant survival can assist nephrologists in making therapeutic decisions. However, predicting kidney transplantation (KT) outcomes is challenging due to the complexity of the factors involved. Artificial intelligence (AI) has become an increasingly important tool in the prediction of medical outcomes. Our goal was to utilize both conventional and AI-based methods to predict long-term kidney transplant survival. Our study included 407 KTs divided into two groups (group A: with a graft lifespan greater than 5 years and group B: with poor graft survival). We first performed a traditional statistical analysis and then developed predictive models using machine learning (ML) techniques. Donors in group A were significantly younger. The use of Mycophenolate Mofetil (MMF) was the only immunosuppressive drug that was significantly associated with improved graft survival. The average estimated glomerular filtration rate (eGFR) in the 3rd month post-KT was significantly higher in group A. The number of hospital readmissions during the 1st year post-KT was a predictor of graft survival. In terms of early post-transplant complications, delayed graft function (DGF), acute kidney injury (AKI), and acute rejection (AR) were significantly associated with poor graft survival. Among the 35 AI models developed, the best model had an AUC of 89.7% (Se: 91.9%; Sp: 87.5%). It was based on ten variables selected by an ML algorithm, with the most important being hypertension and a history of red-blood-cell transfusion. The use of AI provided us with a robust model enabling fast and precise prediction of 5-year graft survival using early and easily collectible variables. Our model can be used as a decision-support tool to early detect graft status.

Multi-phasic eGFR trajectory during follow up and long-term graft failure after kidney transplantation

BACKGROUND

The prevailing assumption is that following kidney transplantation the pattern of kidney function decline is consistent. Nevertheless, numerous factors leading to graft loss may emerge, altering the trajectory of kidney function. In this study, we aim to assess alterations in estimated glomerular filtration rate (eGFR) trajectory over an extended period of follow-up and examine its correlation with graft survival.

METHODS

We calculated eGFR using all creatinine values available from 1-year post transplantation to the end of follow-up. For pattern analysis, we used a piecewise linear model.

RESULTS

Nine hundred eighty-eight patients were included in the study. After a median follow-up of 5.2 years, 297 (30.1%) patients had a multi-phasic eGFR trajectory. Change in eGFR trajectory was associated with increased risk for graft failure (HR 7.15, 95% CI 5.17-9.89, p < .001), longer follow-up time, younger age, longer cold ischemia time, high prevalence of acute rejection, longer hospitalization and a lower initial eGFR. Of the 988 patients included in the study, 494 (50.0%) had a mono-phasic stable trajectory, 197 (19.9%) had a mono-phasic decreasing trajectory, 184 (18.6%) had bi-phasic decreasing trajectory (initial stability and then decline, 46(4.7%) had a bi-phasic stabilized (initial decline and then stabilization) and 67(6.8%) had a more complex trajectory (tri-phasic). Out of the total 144 patients who experienced graft loss, the predominant pattern was a bi-phasic decline characterized by a bi-linear trajectory (66 events, 45.8%).

CONCLUSIONS

Changes in eGFR trajectory during long-term follow-up can serve as a valuable tool for assessing the underlying mechanisms contributing to graft loss.

Challenges in the management of the kidney allograft: from decline to failure: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference

In March 2022, Kidney Disease: Improving Global Outcomes (KDIGO) held a virtual Controversies Conference to address the important but rarely examined phase during which the kidney transplant is failing or has failed. In addition to discussing the definition of a failing allograft, 4 broad areas were considered in the context of a declining functioning graft: prognosis and kidney failure trajectory; immunosuppression strategies; management of medical and psychological complications, and patient factors; and choice of kidney replacement therapy or supportive care following graft loss. Identifying and paying special attention to individuals with failing allografts was felt to be important in order to prepare patients psychologically, manage immunosuppression, address complications, prepare for dialysis and/or retransplantation, and transition to supportive care. Accurate prognostication tools, although not yet widely available, were embraced as necessary to define allograft survival trajectories and the likelihood of allograft failure. The decision of whether to withdraw or continue immunosuppression after allograft failure was deemed to be based most appropriately on risk-benefit analysis and likelihood of retransplantation within a few months. Psychological preparation and support was identified as a critical factor in patient adjustment to graft failure, as was early communication. Several models of care were noted that enabled a medically supportive transition back to dialysis or retransplantation. Emphasis was placed on the importance of dialysis-access readiness before initiation of dialysis, in order to avoid use of central venous catheters. The centrality of the patient to all management decisions and discussions was deemed to be paramount. Patient "activation," which can be defined as engaged agency, was seen as the most effective way to achieve success. Unresolved controversies, gaps in knowledge, and areas for research were also stressed in the conference deliberations.

Plasma protein signatures reflect systemic immunity and allograft function in kidney transplantation

Kidney transplantation causes large perturbations of the immune system. While many studies focus on the allograft, insights into systemic effects are largely missing. Here, we analyzed the systemic immune response in 3 cohorts of kidney transplanted patients. Using serum proteomics, laboratory values, mass cytometry, histological and clinical parameters, inter-patient heterogeneity was leveraged for multi-omic co-variation analysis. We identified circulating immune modules (CIM) that describe extra-renal signatures of co-regulated plasma proteins. CIM are present in nontransplanted controls, in transplant conditions and during rejection. They are enriched in pathways linked to kidney function, extracellular matrix, signaling, and cellular activation. A complex leukocyte response in the blood during allograft quiescence and rejection is associated with CIM activity and CIM-specific cytokines. CIM activity correlates with kidney function including a 2-month prediction. Together, the data suggest a systemic and multi-layered response of transplant immunity that might be insightful for understanding allograft dysfunction and developing translational biomarkers.

Artificial intelligence: a new field of knowledge for nephrologists?

Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.

Immune monitoring of allograft status in kidney transplant recipients

Kidney transplant patients require careful management of immunosuppression to avoid rejection while minimizing the risk of infection and malignancy for the best long-term outcome. The gold standard for monitoring allograft status and immunosuppression adequacy is a kidney biopsy, but this is invasive and costly. Conventional methods of allograft monitoring, such as serum creatinine level, are non-specific. Although they alert physicians to the need to evaluate graft dysfunction, by the time there is a clinical abnormality, allograft damage may have already occurred. The development of novel and non-invasive methods of evaluating allograft status are important to improving graft outcomes. This review summarizes the available conventional and novel methods for monitoring allograft status after kidney transplant. Novel and less invasive methods include gene expression, cell-free DNA, urinary biomarkers, and the use of artificial intelligence. The optimal method to manage patients after kidney transplant is still being investigated. The development of less invasive methods to assess allograft function has the potential to improve patient outcomes and allow for a more personalized approach to immunosuppression management.

Donor-Derived Cell-Free DNA (dd-cfDNA) in Kidney Transplant Recipients With Indication Biopsy-Results of a Prospective Single-Center Trial

Donor-derived cell-free DNA (dd-cfDNA) identifies allograft injury and discriminates active rejection from no rejection. In this prospective study, 106 kidney transplant recipients with 108 clinically indicated biopsies were enrolled at Heidelberg University Hospital between November 2020 and December 2022 to validate the clinical value of dd-cfDNA in a cohort of German patients. dd-cfDNA was quantified at biopsy and correlated to histopathology. Additionally, dd-cfDNA was determined on days 7, 30, and 90 post-biopsy and analyzed for potential use to monitor response to anti-rejection treatment. dd-cfDNA levels were with a median (IQR) % of 2.00 (0.48-3.20) highest in patients with ABMR, followed by 0.92 (0.19-11.25) in patients with TCMR, 0.44 (0.20-1.10) in patients with borderline changes and 0.20 (0.11-0.53) in patients with no signs of rejection. The AUC for dd-cfDNA to discriminate any type of rejection including borderline changes from no rejection was at 0.72 (95% CI 0.62-0.83). In patients receiving anti-rejection treatment, dd-cfDNA levels significantly decreased during the 7, 30, and 90 days follow-up compared to levels at the time of biopsy (p = 0.006, p = 0.002, and p < 0.001, respectively). In conclusion, dd-cfDNA significantly discriminates active rejection from no rejection. Decreasing dd-cfDNA following anti-rejection treatment may indicate response to therapy. Clinical Trial Registration: https://drks.de/search/de/trial/DRKS00023604, identifier DRKS00023604.

Systemic inflammation early after kidney transplantation is associated with long-term graft loss: a cohort study

Background

Early graft loss following kidney transplantation is mainly a result of acute rejection or surgical complications, while long-term kidney allograft loss is more complex. We examined the association between systemic inflammation early after kidney transplantation and long-term graft loss, as well as correlations between systemic inflammation scores and inflammatory findings in biopsies 6 weeks and 1 year after kidney transplantation.

Methods

We measured 21 inflammatory biomarkers 10 weeks after transplantation in 699 patients who were transplanted between 2009 and 2012 at Oslo University Hospital, Rikshospitalet, Norway. Low-grade inflammation was assessed with predefined inflammation scores based on specific biomarkers: one overall inflammation score and five pathway-specific scores. Surveillance or indication biopsies were performed in all patients 6 weeks after transplantation. The scores were tested in Cox regression models.

Results

Median follow-up time was 9.1 years (interquartile range 7.6-10.7 years). During the study period, there were 84 (12.2%) death-censored graft losses. The overall inflammation score was associated with long-term kidney graft loss both when assessed as a continuous variable (hazard ratio 1.03, 95% CI 1.01-1.06, P = 0.005) and as a categorical variable (4th quartile: hazard ratio 3.19, 95% CI 1.43-7.10, P = 0.005). In the pathway-specific analyses, fibrogenesis activity and vascular inflammation stood out. The vascular inflammation score was associated with inflammation in biopsies 6 weeks and 1 year after transplantation, while the fibrinogenesis score was associated with interstitial fibrosis and tubular atrophy.

Conclusion

In conclusion, a systemic inflammatory environment early after kidney transplantation was associated with biopsy-confirmed kidney graft pathology and long-term kidney graft loss. The systemic vascular inflammation score correlated with inflammatory findings in biopsies 6 weeks and 1 year after transplantation.

Validation of a prediction system for risk of kidney allograft failure in pediatric kidney transplant recipients: An international observational study

Predicting long-term kidney allograft failure is an unmet need for clinical care and clinical trial optimization in children. We aimed to validate a kidney allograft failure risk prediction system in a large international cohort of pediatric kidney transplant recipients. Patients from 20 centers in Europe and the United States, transplanted between 2004 and 2017, were included. Allograft assessment included estimated glomerular filtration rate, urine protein-to-creatinine ratio, circulating antihuman leukocyte antigen donor-specific antibody, and kidney allograft histology. Individual predictions of allograft failure were calculated using the integrative box (iBox) system. Prediction performances were assessed using discrimination and calibration. The allograft evaluations were performed in 706 kidney transplant recipients at a median time of 9.1 (interquartile range, 3.3-19.2) months posttransplant; mean estimated glomerular filtration rate was 68.7 ± 28.1 mL/min/1.73 m2, and median urine protein-to-creatinine ratio was 0.1 (0.0-0.4) g/g, and 134 (19.0%) patients had antihuman leukocyte antigen donor-specific antibodies. The iBox exhibited accurate calibration and discrimination for predicting the outcomes up to 10 years after evaluation, with a C-index of 0.81 (95% confidence interval, 0.75-0.87). This study confirms the generalizability of the iBox to predict long-term kidney allograft failure in children, with performances similar to those reported in adults. These results support the use of the iBox to improve patient monitoring and facilitate clinical trials in children.

Qualifying a novel clinical trial endpoint (iBOX) predictive of long-term kidney transplant outcomes

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

Qualifying a Novel Clinical Trial Endpoint (iBOX) Predictive of Long-Term Kidney Transplant Outcomes

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

A Multi-Step Precision Pathway for Predicting Allograft Survival in Heterogeneous Cohorts of Kidney Transplant Recipients

Accurate prediction of allograft survival after kidney transplantation allows early identification of at-risk recipients for adverse outcomes and initiation of preventive interventions to optimize post-transplant care. Many prediction algorithms do not model cohort heterogeneity and may lead to inaccurate assessment of longer-term graft outcomes among minority groups. Using data from a national Australian kidney transplant cohort (2008-2017) as the derivation set, we developed P-Cube, a multi-step precision prediction pathway model for predicting overall graft survival in three ethnic subgroups: European Australians, Asian Australians and Aboriginal and Torres Strait Islander Peoples. The concordance index for the European Australians, Asian Australians, and Aboriginal and Torres Strait Islander Peoples subpopulations were 0.99 (0.98-0.99), 0.93 (0.92-0.94) and 0.92 (0.91-0.93), respectively. Similar findings were observed when validating P-cube using an external dataset [Scientific Registry of Transplant Recipient Registry (2006-2020)]. Six sub-categories of recipients with distinct risk factor profiles were identified. Some factors such as blood group compatibility were considered important across the entire transplant population. Other factors such as human leukocyte antigen (HLA)-DR mismatches were unique to older recipients. The P-cube model identifies allograft survival specific risk factors within a heterogenous population and offers personalized survival predictions in a diverse cohort.

Longitudinal estimated glomerular filtration rate (eGFR) modeling in long-term renal function to inform clinical trial design in kidney transplantation

Kidney transplantation is the preferred treatment for individuals with end-stage kidney disease. From a modeling perspective, our understanding of kidney function trajectories after transplantation remains limited. Current modeling of kidney function post-transplantation is focused on linear slopes or percent decline and often excludes the highly variable early timepoints post-transplantation, where kidney function recovers and then stabilizes. Using estimated glomerular filtration rate (eGFR), a well-known biomarker of kidney function, from an aggregated dataset of 4904 kidney transplant patients including both observational studies and clinical trials, we developed a longitudinal model of kidney function trajectories from time of transplant to 6 years post-transplant. Our model is a nonlinear, mixed-effects model built in NONMEM that captured both the recovery phase after kidney transplantation, where the graft recovers function, and the long-term phase of stabilization and slow decline. Model fit was assessed using diagnostic plots and individual fits. Model performance, assessed via visual predictive checks, suggests accurate model predictions of eGFR at the median and lower 95% quantiles of eGFR, ranges which are of critical clinical importance for assessing loss of kidney function. Various clinically relevant covariates were also explored and found to improve the model. For example, transplant recipients of deceased donors recover function more slowly after transplantation and calcineurin inhibitor use promotes faster long-term decay. Our work provides a generalizable, nonlinear model of kidney allograft function that will be useful for estimating eGFR up to 6 years post-transplant in various clinically relevant populations.

Molecular immune monitoring in kidney transplant rejection: a state-of-the-art review

Although current regimens of immunosuppressive drugs are effective in renal transplant recipients, long-term renal allograft outcomes remain suboptimal. For many years, the diagnosis of renal allograft rejection and of several causes of renal allograft dysfunction, such as chronic subclinical inflammation and infection, was mostly based on renal allograft biopsy, which is not only invasive but also possibly performed too late for proper management. In addition, certain allograft dysfunctions are difficult to differentiate from renal histology due to their similar pathogenesis and immune responses. As such, non-invasive assays and biomarkers may be more beneficial than conventional renal biopsy for enhancing graft survival and optimizing immunosuppressive drug regimens during long-term care. This paper discusses recent biomarker candidates, including donor-derived cell-free DNA, transcriptomics, microRNAs, exosomes (or other extracellular vesicles), urine chemokines, and nucleosomes, that show high potential for clinical use in determining the prognosis of long-term outcomes of kidney transplantation, along with their limitations.

Regulatory T Cell Biomarkers Identify Patients at Risk of Developing Acute Cellular Rejection in the First Year Following Heart Transplantation

BACKGROUND

Acute cellular rejection (ACR), an alloimmune response involving CD4+ and CD8+ T cells, occurs in up to 20% of patients within the first year following heart transplantation. The balance between a conventional versus regulatory CD4+ T cell alloimmune response is believed to contribute to developing ACR. Therefore, tracking these cells may elucidate whether changes in these cell populations could signal ACR risk.

METHODS

We used a CD4+ T cell gene signature (TGS) panel that tracks CD4+ conventional T cells (Tconv) and regulatory T cells (Treg) on longitudinal samples from 94 adult heart transplant recipients. We evaluated combined diagnostic performance of the TGS panel with a previously developed biomarker panel for ACR diagnosis, HEARTBiT, while also investigating TGS' prognostic utility.

RESULTS

Compared with nonrejection samples, rejection samples showed decreased Treg- and increased Tconv-gene expression. The TGS panel was able to discriminate between ACR and nonrejection samples and, when combined with HEARTBiT, showed improved specificity compared with either model alone. Furthermore, the increased risk of ACR in the TGS model was associated with lower expression of Treg genes in patients who later developed ACR. Reduced Treg gene expression was positively associated with younger recipient age and higher intrapatient tacrolimus variability.

CONCLUSIONS

We demonstrated that expression of genes associated with CD4+ Tconv and Treg could identify patients at risk of ACR. In our post hoc analysis, complementing HEARTBiT with TGS resulted in an improved classification of ACR. Our study suggests that HEARTBiT and TGS may serve as useful tools for further research and test development.