Kidney transplantation in highly sensitized recipients

Pre-transplant crossmatch-negative donor-specific anti-HLA antibody predicts acute antibody-mediated rejection but not long-term outcomes in kidney transplantation: an analysis of the Korean Organ Transplantation Registry

Background

Pre-transplant donor-specific anti-human leukocyte antigen antibody (HLA-DSA) is a recognized risk factor for acute antibody-mediated rejection (ABMR) and allograft failure. However, the clinical relevance of pre-transplant crossmatch (XM)-negative HLA-DSA remains unclear.

Methods

We investigated the effect of XM-negative HLA-DSA on post-transplant clinical outcomes using data from the Korean Organ Transplantation Registry (KOTRY). This study included 2019 living donor kidney transplant recipients from 40 transplant centers in South Korea: 237 with HLA-DSA and 1782 without HLA-DSA.

Results

ABMR developed more frequently in patients with HLA-DSA than in those without (5.5% vs. 1.5%, p<0.0001). Multivariable analysis identified HLA-DSA as a significant risk factor for ABMR (odds ratio = 3.912, 95% confidence interval = 1.831-8.360; p<0.0001). Furthermore, the presence of multiple HLA-DSAs, carrying both class I and II HLA-DSAs, or having strong HLA-DSA were associated with an increased incidence of ABMR. However, HLA-DSA did not affect long-term clinical outcomes, such as allograft function and allograft survival, patient survival, and infection-free survival.

Conclusion

Pre-transplant XM-negative HLA-DSA increased the risk of ABMR but did not affect long-term allograft outcomes. HLA-incompatible kidney transplantation in the context of XM-negative HLA-DSA appears to be feasible with careful monitoring and ensuring appropriate management of any occurrence of ABMR. Furthermore, considering the characteristics of pre-transplant XM-negative HLA-DSA, the development of a more detailed and standardized desensitization protocol is warranted.

Donor-derived cell-free DNA predicted allograft rejection and severe microvascular inflammation in kidney transplant recipients

Introduction

The aim of this study is to investigate the clinical validity of donor-derived cell-free DNA (dd-cfDNA) in comparison with that of donor specific anti-HLA antibody (DSA) for predicting biopsy-proven rejection (BPR)and severe microvascular inflammation (severe MVI) in kidney transplant recipients (KTRs).

Methods

In this prospective observational investigation, 64 KTRs who underwent the indicated biopsies were included. Blood samples collected prior to biopsy were tested for dd-cfDNA and DSA. Biopsy specimens were classified by a renal pathologist according to the Banff classification. The predictive performance of dd-cfDNA and DSA for histological allograft diagnosis was assessed.

Results

KTRs were categorized into the high and low dd-cfDNA groups based on a level of 0.4%. Eighteen patients (28.1%) had positive DSA at biopsy, exhibiting higher dd-cfDNA levels than the DSA-negative patients. BPR and severe MVI incidences were elevated in the high dd-cfDNA group (BPR: 42.9% vs. 3.4%, P <0.001; severe MVI: 37.1% vs. 3.4%, P = 0.001). Also, elevated glomerulitis and MVI scores were observed in the high dd-cfDNA group. DSA showed the highest predictive value for BPR (AUC = 0.880), whereas dd-cfDNA alone excelled in predicting severe MVI (AUC = 0.855). Combination of DSA and dd-cfDNA (>0.4%) yielded sensitivities of 80.0% and 50.0% with specificities of 90.7% and 88.0% for antibody-mediated rejection and severe MVI detection, respectively.

Conclusion

The dd-cfDNA test is a predictive tool for BPR and severe MVI, and it can improve the performance, especially when combined with DSA for BPR.

Epidemiology, Clinical Manifestations, and Outcome of Mucormycosis in Solid Organ Transplant Recipients: A Systematic Review of Reported Cases

Mucormycosis is an emerging disease primarily affecting the immunocompromised host, but scarce evidence is available for solid organ transplant recipients (SOTRs). We systematically reviewed 183 cases occurring in SOTRs, exploring epidemiology, clinical characteristics, causative pathogens, therapeutic approaches, and outcomes. Kidney transplants accounted for half of the cases, followed by heart (18.6%), liver (16.9%), and lung (10.4%). Diagnosis showed a dichotomous distribution, with 63.7% of cases reported within 100 days of transplantation and 20.6% occurring at least 1 year after transplant. The 90-day and 1-year mortality rates were 36.3% and 63.4%, respectively. Disseminated disease had the highest mortality at both time points (75% and 93%). Treatment with >3 immunosuppressive drugs showed a significant impact on 90-day mortality (odds ratio [OR], 2.33; 95% CI, 1.02-5.66; P = .0493), as did a disseminated disease manifestation (OR, 8.23; 95% CI, 2.20-36.71; P = .0027) and the presence of diabetes (OR, 2.35; 95% CI, 1.01-5.65; P = .0497). Notably, prophylaxis was administered to 12 cases with amphotericin B. Further investigations are needed to validate these findings and to evaluate the potential implementation of prophylactic regimens in SOTRs at high risk.

Role of Complement-dependent Cytotoxicity Crossmatch and HLA Typing in Solid Organ Transplant

BACKGROUND

Solid organ transplantation is a life-saving medical operation that has progressed greatly because of developments in diagnostic tools and histocompatibility tests. Crossmatching for complement-dependent cytotoxicity (CDC) and human leukocyte antigen (HLA) typing are two important methods for checking graft compatibility and reducing the risk of graft rejection. HLA typing and CDC crossmatching are critical in kidney, heart, lung, liver, pancreas, intestine, and multi-organ transplantation.

METHODS

A systematic literature search was conducted on the internet, using PubMed, Scopus, and Google Scholar databases, to identify peer-reviewed publications about solid organ transplants, HLA typing, and CDC crossmatching.

CONCLUSION

Recent advances in HLA typing have allowed for high-resolution evaluation, epitope matching, and personalized therapy methods. Genomic profiling, next-generation sequencing, and artificial intelligence have improved HLA typing precision, resulting in better patient outcomes. Artificial intelligence (AI) driven virtual crossmatching and predictive algorithms have eliminated the requirement for physical crossmatching in the context of CDC crossmatching, boosting organ allocation and transplant efficiency. This review elaborates on the importance of HLA typing and CDC crossmatching in solid organ transplantation.

Uremic Pericarditis with Cardiac Tamponade in a Patient on Hemodialysis

Uremic pericardial effusion and pericarditis in end-stage kidney disease patients remain one of the causes responsible for high rates of morbidity and occasional mortality. While clinical presentation could be variable, clinicians should have a high index of suspicion for uremic pericarditis especially in patients who miss their dialysis sessions. We present a 77-year-old man with end-stage renal disease on dialysis diagnosed with pericarditis and large pericardial effusion complicated by cardiac tamponade and shock. He underwent urgent pericardiocentesis with clinical improvement. The course of the disease can be complicated by shock with multiorgan failure, particularly the liver. The presentation is relatively acute requiring a high level of suspicion, urgent diagnosis, and management to reduce mortality. As the geriatric population increases with associated comorbid conditions, it would be expected that patients undergoing dialysis would increase. Given the uncommon nature of the disease and how these patients have been managed by multiple specialties and care providers, it is important to consider dialysis-related complications in all patients with end-stage renal disease presenting with dyspnea.

Impact of Induction Immunosuppressants on T Lymphocyte Subsets after Kidney Transplantation: A Prospective Observational Study with Focus on Anti-Thymocyte Globulin and Basiliximab Induction Therapies

Induction immunosuppressive therapy for kidney transplant recipients (KTRs) primarily includes interleukin-2 receptor antagonists, such as basiliximab (BXM) or lymphocyte-depleting agents, and anti-thymocyte globulin (ATG). This study aimed to investigate their effects on T cell dynamics during the early post-transplantation period. This prospective observational study included 157 KTRs. Peripheral blood samples were collected from each patient within 5 days before and 4 and 12 weeks after transplantation. Flow cytometric analysis was performed to assess various T cell subsets whose changes were then analyzed. In the ATG group, CD4+ T cell expression decreased significantly compared with that in the BXM group. However, CD4+CD161+ and CD4+CD25+CD127low T cell expression levels increased significantly. In the CD8+ T cell subset, a decrease in CD8+CD28nullCD57+ and CD8+CCR7+ T cell expression was observed in the ATG group. However, among patients diagnosed with biopsy-proven acute rejection, T cell subset expression did not significantly differ relative to non-rejection cases. In conclusion, ATG induction therapy resulted in more pronounced changes in T lymphocyte subsets than BXM induction, with increased CD4+CD161+ and CD4+CD25+CD127low T cells and an early decrease in CD8+CD28nullCD57+ and CD8+CCR7+ T cells, some of which are associated with acute rejection.

Impact of Low-Level Donor-Specific Anti-HLA Antibody on Posttransplant Clinical Outcomes in Kidney Transplant Recipients

Background

The clinical significance of low-level donor-specific anti-HLA antibody (low-DSA) remains controversial. We investigated the impact of low-DSA on posttransplant clinical outcomes in kidney transplant (KT) recipients.

Methods

We retrospectively reviewed 1,027 KT recipients, namely, 629 living donor KT (LDKT) recipients and 398 deceased donor KT (DDKT) recipients, in Seoul St. Mary's Hospital (Seoul, Korea) between 2010 and 2018. Low-DSA was defined as a positive anti-HLA-DSA result in the Luminex single antigen assay (LABScreen single antigen HLA class I - combi and class II - group 1 kits; One Lambda, Canoga Park, CA, USA) but a negative result in a crossmatch test. We compared the incidence of biopsy-proven allograft rejection (BPAR), changes in allograft function, allograft survival, patient survival, and posttransplant infections between subgroups according to pretransplant low-DSA.

Results

The incidence of overall BPAR and T cell-mediated rejection did not differ between the subgroups. However, antibody-mediated rejection (ABMR) developed more frequently in patients with low-DSA than in those without low-DSA in the total cohort and the LDKT and DDKT subgroups. In multivariate analysis, low-DSA was identified as a risk factor for ABMR development. Its impact was more pronounced in DDKT (odds ratio [OR]: 9.60, 95% confidence interval [CI]: 1.79-51.56) than in LDKT (OR: 3.76, 95% CI: 0.99-14.26) recipients. There were no significant differences in other outcomes according to pretransplant low-DSA.

Conclusions

Pretransplant low-DSA has a significant impact on the development of ABMR, and more so in DDKT recipients than in LDKT recipients, but not on long-term outcomes.

Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering

Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients.

Combined impact of the inter and intra-patient variability of tacrolimus blood level on allograft outcomes in kidney transplantation

Introduction

Tacrolimus (TAC) has been widely used as an immunosuppressant after kidney transplantation (KT); however, the combined effects of intra-patient variability (IPV) and inter-patient variability of TAC-trough level (C0) in blood remain controversial. This study aimed to determine the combined impact of TAC-IPV and TAC inter-patient variability on allograft outcomes of KT.

Methods

In total, 1,080 immunologically low-risk patients who were not sensitized to donor human leukocyte antigen (HLA) were enrolled. TAC-IPV was calculated using the time-weighted coefficient variation (TWCV) of TAC-C0, and values > 30% were classified as high IPV. Concentration-to-dose ratio (CDR) was used for calculating TAC inter-patient variability, and CDR < 1.05 ng•mg/mL was classified as rapid metabolizers (RM). TWCV was calculated based on TAC-C0 up to 1 year after KT, and CDR was calculated based on TAC-C0 up to 3 months after KT. Patients were classified into four groups according to TWCV and CDR: low IPV/non-rapid metabolizer (NRM), high IPV/NRM, low IPV/RM, and high IPV/RM. Subgroup analysis was performed for pre-transplant panel reactive antibody (PRA)-positive and -negative patients (presence or absence of non-donor-specific HLA-antibodies). Allograft outcomes, including deathcensored graft loss (DCGL) and biopsy-proven allograft rejection (BPAR), were compared.

Results

The incidences of DCGL, BPAR, and overall graft loss were the highest in the high-IPV/RM group. In addition, a high IPV/RM was identified as an independent risk factor for DCGL. The hazard ratio of high IPV/RM for DCGL and the incidence of active antibody-mediated rejection were considerably increased in the PRA-positive subgroup.

Discussion

High IPV combined with RM (inter-patient variability) was closely related to adverse allograft outcomes, and hence, more attention must be given to pre-transplant PRA-positive patients.

Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection

Kidney transplantation is the preferred treatment for patients with end-stage kidney disease, because it prolongs survival and improves quality of life. Allograft biopsy is the gold standard for diagnosing allograft rejection. However, it is invasive and reactive, and continuous monitoring is unrealistic. Various biomarkers for diagnosing allograft rejection have been developed over the last two decades based on omics technologies to overcome these limitations. Omics technologies are based on a holistic view of the molecules that constitute an individual. They include genomics, transcriptomics, proteomics, and metabolomics. The omics approach has dramatically accelerated biomarker discovery and enhanced our understanding of multifactorial biological processes in the field of transplantation. However, clinical application of omics-based biomarkers is limited by several issues. First, no large-scale prospective randomized controlled trial has been conducted to compare omics-based biomarkers with traditional biomarkers for rejection. Second, given the variety and complexity of injuries that a kidney allograft may experience, it is likely that no single omics approach will suffice to predict rejection or outcome. Therefore, integrated methods using multiomics technologies are needed. Herein, we introduce omics technologies and review the latest literature on omics biomarkers predictive of allograft rejection in kidney transplant recipients.