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

Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure

Background

Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate end points and for patient management as clinical decision support tools. However, the effect of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes.

Methods

We included 11,046 kidney transplant recipients enrolled in ten countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine–Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modeling and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance.

Results

Among 11,046 recipients in the derivation and validation cohorts, 1497 (14%) lost their graft and 1003 (9%) died with a functioning graft after a median follow-up postrisk evaluation of 4.7 years (interquartile range, 2.7–7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan–Meier and Aalen–Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138, and 0.0135 for Cox, Fine–Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors older than 65 years), the findings suggest a trend toward moderately improved calibration when using a competing risk approach.

Conclusions

Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

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.

Addressing the need for standardization of symptomatic medication documentation in Parkinson's disease clinical research: A call to action

People with Parkinson's disease (PD) are prescribed a variety of medications to mitigate symptoms and improve their quality of life. These symptomatic therapies cover a range of pharmacological classes, including classical dopaminergic treatments, other antiparkinsonian agents, and pharmacotherapies for non-PD conditions. Often, medications are prescribed for concomitant use and in increasing doses, particularly as the disease progresses. Documentation of these interventions in clinical trials is necessary to accurately capture medication usage, compare medication utilization across different studies, understand factors contributing to experimental therapeutic response, and analyze clinical trial data in a precise manner. At the present time, there is no current international standard for how these medications are documented within clinical trials. As a case example, we will highlight medication use documentation in a large international multicenter observational study commonly used as a reference for design of clinical trials. This review aims to raise awareness within the scientific community of the importance of proper medication documentation and the need for standardization to harmonize prescriptive practices, improve treatment interpretability, and perform consistently robust analyses from clinical trials data.

High-dimensional analysis of NK cells in kidney transplantation uncovers subsets associated with antibody-independent graft dysfunction

Natural killer (NK) cells respond to diseased and allogeneic cells through NKG2A/HLA-E or killer cell immunoglobulin-like receptor (KIR)/HLA-ABC interactions. Correlations between HLA/KIR disparities and kidney transplant pathology suggest an antibody-independent pathogenic role for NK cells in transplantation, but the mechanisms remain unclear. Using CyTOF to characterize recipient peripheral NK cell phenotypes and function, we observed diverse NK cell subsets among participants who responded heterogeneously to allo-stimulators. NKG2A+KIR+ NK cells responded more vigorously than other subsets, and this heightened response persisted after kidney transplantation despite immunosuppression. In test and validation sets from 2 clinical trials, pretransplant donor-induced release of cytotoxicity mediator Ksp37 by NKG2A+ NK cells correlated with reduced long-term allograft function. Separate analyses showed that Ksp37 gene expression in allograft biopsies lacking histological rejection correlated with death-censored graft loss. Our findings support an antibody-independent role for NK cells in transplant injury and support further testing of pretransplant, donor-reactive, NK cell-produced Ksp37 as a risk-assessing, transplantation biomarker.

How artificial intelligence is transforming nephrology

Current research in nephrology is increasingly focused on elucidating the complexity inherent in tightly interwoven molecular systems and their correlation with pathology and related therapeutics, including dialysis and renal transplantation. Rapid advances in the omics sciences, medical device sensorization, and networked digital medical devices have made such research increasingly data centered. Data-centric science requires the support of computationally powerful and sophisticated tools able to handle the overflow of novel biomarkers and therapeutic targets. This is a context in which artificial intelligence (AI) and, more specifically, machine learning (ML) can provide a clear analytical advantage, given the rapid advances in their ability to harness multimodal data, from genomic information to signal, image and even heterogeneous electronic health records (EHR). However, paradoxically, only a small fraction of ML-based medical decision support systems undergo validation and demonstrate clinical usefulness. To effectively translate all this new knowledge into clinical practice, the development of clinically compliant support systems based on interpretable and explainable ML-based methods and clear analytical strategies for personalized medicine are imperative. Intelligent nephrology, that is, the design and development of AI-based strategies for a data-centric approach to nephrology, is just taking its first steps and is by no means yet close to its coming of age. These first steps are not even homogeneously taken, as a digital divide in access to technology has become evident between developed and developing countries, also affecting underrepresented minorities. With all this in mind, this editorial aim to provide a selective overview of the current use of AI technologies in nephrology and heralds the "Artificial Intelligence in Nephrology" special issue launched by BMC Nephrology.

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.

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 .

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.