Pathology of the kidney allograft

Exploring the potential of Fourier transform-infrared spectroscopy of urine for non-invasive monitoring of inflammation associated with a kidney transplant

The global rise of end-stage renal disease is leading to an increase in kidney transplants. Graft survival is dependent on the occurrence of inflammation which can lead to cases of rejection. Traditional laboratory analyses often lack accuracy, and graft biopsies - the current gold standard - are considered invasive and risky. This highlights an unmet need for innovative diagnostic and monitoring methods of graft rejection and inflammation. This study explores the potential of Fourier-transform infrared spectroscopy of fresh urine for diagnosing kidney transplant inflammation. Urine samples were collected from kidney transplant patients who were under regular surveillance. An unsupervised method of spectral data analysis, especially Uniform Manifold Approximation and Projection (UMAP), was initially employed. However, it was unable to reveal a clear distinction between control and pathological conditions. Subsequently, two machine learning models - SVM and gradient boosting - were employed to categorise participants into pathologic or control groups, achieving a diagnostic accuracy of 77.78%. This study also evaluated other factors that could affect model performance, including urine biochemical composition, type of inflammation, and patient's medication history. The inherent variability of urine, attributed to factors such as diet and medications, poses challenges to identifying robust spectroscopic markers. Nevertheless, mid-infrared spectroscopy offers a promising, non-invasive approach for diagnosing kidney transplant disorders. Further research is essential to provide more advanced prediction models and meet the criteria for potential clinical deployment.

Expanding role of antibodies in kidney transplantation

The role of antibodies in kidney transplant (KT) has evolved significantly over the past few decades. This role of antibodies in KT is multifaceted, encompassing both the challenges they pose in terms of antibody-mediated rejection (AMR) and the opportunities for improving transplant outcomes through better detection, prevention, and treatment strategies. As our understanding of the immunological mechanisms continues to evolve, so too will the approaches to managing and harnessing the power of antibodies in KT, ultimately leading to improved patient and graft survival. This narrative review explores the multifaceted roles of antibodies in KT, including their involvement in rejection mechanisms, advancements in desensitization protocols, AMR treatments, and their potential role in monitoring and improving graft survival.

The transformative potential of artificial intelligence in solid organ transplantation

Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

Evolution of human kidney allograft pathology diagnostics through 30 years of the Banff classification process

The second half of the previous century witnessed a tremendous rise in the number of clinical kidney transplants worldwide. This activity was, however, accompanied by many issues and challenges. An accurate diagnosis and appropriate management of causes of graft dysfunction were and still are, a big challenge. Kidney allograft biopsy played a vital role in addressing the above challenge. However, its interpretation was not standardized for many years until, in 1991, the Banff process was started to fill this void. Thereafter, regular Banff meetings took place every 2 years for the past 30 years. Marked changes have taken place in the interpretation of kidney allograft biopsies, diagnosis, and classification of rejection and other non-rejection pathologies from the original Banff 93 classification. This review attempts to summarize those changes for increasing the awareness and understanding of kidney allograft pathology through the eyes of the Banff process. It will interest the transplant surgeons, physicians, pathologists, and allied professionals associated with the care of kidney transplant patients.

Discrepancy Analysis between Histology and Molecular Diagnoses in Kidney Allograft Biopsies: A Single-Center Experience

Histology diagnosis is essential for the monitoring and management of kidney transplant patients. Nowadays, the accuracy and reproducibility of histology have been criticized when compared with molecular microscopy diagnostic system (MMDx). Our cohort included 95 renal allograft biopsies with both histology and molecular diagnoses. Discrepancies between histology and molecular diagnosis were assessed for each biopsy. Among the 95 kidney allograft biopsies, a total of 6 cases (6%) showed clear (n = 4) or borderline (n = 2) discrepancies between histology and molecular diagnoses. Four out of the six (67%) were cases with pathologically and clinically confirmed active infections that were diagnosed as mild to moderate T-cell-mediated rejection (TCMR) with MMDx. Two cases showed pathological changes that were not sufficient to make a definitive diagnosis of active rejection via histology, while MMDx results showed antibody-mediated rejection (ABMR). In addition, there were six cases with recurrent or de novo glomerular diseases diagnosed only via histology. All other biopsy results were in an agreement. Our results indicate that histology diagnosis of kidney allograft biopsy is superior to molecular diagnosis in the setting of infections and glomerular diseases; however, MMDx can provide helpful information to confirm the diagnosis of active ABMR.

Novel Histopathologic and Ultrastructural Findings in Renal Allografts From Patients With Size and Age Mismatches: A Case Series

While reports on the long-term pathology in mismatched allografts have been focused on the donor and recipient body surface area, evidence is emerging to support donor-recipient age difference as an additional prognostic factor. Most reports are based on pediatric recipients receiving older/bigger allografts. Here, we describe three cases with age mismatch including two cases of adult patients receiving pediatric allografts and a third case of a younger patient receiving an allograft from an older donor exhibiting findings not described in extant literature. Each of these cases exhibits unique changes seen in mismatched donor-recipient size/age post-transplant pathology. These non-rejection changes should be suspected in cases of donor-recipient size/age mismatch. In cases of allograft function decline, a full biopsy workup, including electron microscopy, should be considered.

Risk factors for graft loss and death among kidney transplant recipients: A competing risk analysis

INTRODUCTION

Kidney transplantation is the best therapeutical option for CKD patients. Graft loss risk factors are usually estimated with the cox method. Competing risk analysis could be useful to determine the impact of different events affecting graft survival, the occurrence of an outcome of interest can be precluded by another. We aimed to determine the risk factors for graft loss in the presence of mortality as a competing event.

METHODS

A retrospective cohort of 1454 kidney transplant recipients who were transplanted between July 1, 2008, to May 31, 2019, in Colombiana de Trasplantes, were analyzed to determine risk factors of graft loss and mortality at 5 years post-transplantation. Kidney and patient survival probabilities were estimated by the competing risk analysis. The Fine and Gray method was used to fit a multivariable model for each outcome. Three variable selection methods were compared, and the bootstrapping technique was used for internal validation as split method for resample. The performance of the final model was assessed calculating the prediction error, brier score, c-index and calibration plot.

RESULTS

Graft loss occurred in 169 patients (11.6%) and death in 137 (9.4%). Cumulative incidence for graft loss and death was 15.8% and 13.8% respectively. In a multivariable analysis, we found that BKV nephropathy, serum creatinine and increased number of renal biopsies were significant risk factors for graft loss. On the other hand, recipient age, acute cellular rejection, CMV disease were risk factors for death, and recipients with living donor had better survival compared to deceased-donor transplant and coronary stent. The c-index were 0.6 and 0.72 for graft loss and death model respectively.

CONCLUSION

We developed two prediction models for graft loss and death 5 years post-transplantation by a unique transplant program in Colombia. Using a competing risk multivariable analysis, we were able to identify 3 significant risk factors for graft loss and 5 significant risk factors for death. This contributes to have a better understanding of risk factors for graft loss in a Latin-American population. The predictive performance of the models was mild.

A Three-Gene Peripheral Blood Potential Diagnosis Signature for Acute Rejection in Renal Transplantation

Background: Acute rejection (AR) remains a major issue that negatively impacts long-term allograft survival in renal transplantation. The current study aims to apply machine learning methods to develop a non-invasive diagnostic test for AR based on gene signature in peripheral blood. Methods: We collected blood gene expression profiles of 251 renal transplant patients with biopsy-proven renal status from three independent cohorts in the Gene Expression Omnibus database. After differential expression analysis and machine learning algorithms, selected biomarkers were applied to the least absolute shrinkage and selection operator (LASSO) logistic regression to construct a diagnostic model in the training cohort. The diagnostic ability of the model was further tested in validation cohorts. Gene set enrichment analysis and immune cell assessment were also conducted for further investigation. Results: A novel diagnostic model based on three genes (TSEN15, CAPRIN1 and PRR34-AS1) was constructed in the training cohort (AUC = 0.968) and successfully verified in the validation cohort (AUC = 0.925) with high accuracy. Moreover, the diagnostic model also showed a promising value in discriminating T cell-mediated rejection (TCMR) (AUC = 0.786). Functional enrichment analysis and immune cell evaluation demonstrated that the AR model was significantly correlated with adaptive immunity, especially T cell subsets and dendritic cells. Conclusion: We identified and validated a novel three-gene diagnostic model with high accuracy for AR in renal transplant patients, and the model also performed well in distinguishing TCMR. The current study provided a promising tool to be used as a precise and cost-effective non-invasive test in clinical practice.