Diagnosis, Frailty, Kidney Transplant, Kidney Transplant; Complications, Kidney Transplant Rejection, Renal Transplant, Renal Transplant Failure, Transplant Dysfunction
Conditions
Keywords
kidney transplant, novel biomarkers, post-transplant antibodies, HLA-specific antibodies, donor specific antibodies, transplant in older age, machine learning, predictive models, rejection
Brief summary
The purpose of this study is to assess a new test to detect antibodies which may form following kidney transplant. These antibodies can be difficult to detect as they do not cause any symptoms but can lead to kidney damage. A new blood test will be performed alongside existing antibody tests to see how well the test functions in comparison and to see how well it is able to distinguish between inflammation caused by antibodies and other sorts of inflammation such as a urinary tract infection. The investigators also want to determine whether it is predictable whom will develop antibodies after a transplant and use these results to change the current way patients are monitored for antibodies after receiving a transplant. In addition to this, the investigators want to establish if patients over 60 years of age are relatively protected against immunological events such as rejection compared to patients who are under 60 years of age. The results could potentially lead to using a different immunosuppression regime based on which population age group patients belong to and lowering the risks associated with these drugs.
Detailed description
Donor-derived cell-free DNA (dd-cfDNA) Post-transplant monitoring for acute rejection in most centres, focuses on identification of a deterioration in graft function which may be totally asymptomatic. The current best practice to investigate a suspected rejection are renal graft biopsy with appropriate staining for complement component C4d and a serum single antigen bead (SAB) testing. This reactive approach to assessing and monitoring rejection, mainly driven by serial assessments of renal function to determine response to treatment, avoids the need for multiple invasive diagnostic tests such as biopsies, but it poses the risk of missing the early detection and treatment of rejection prior to an objective decline in function. A creeping creatinine, where there are small but sustained increases in creatinine at sequential visits is relatively common and by the time a deterioration consistent with rejection is observed there can already have been significant tissue damage. Donor-derived cell-free DNA (dd-cfDNA) has been described as a useful biomarker for graft injury secondary to rejection which can be evident in blood weeks to months prior to histological evidence of graft injury. dd-cfDNA levels are high in the immediate post-operative phase, although there is a sharp drop off to low baseline levels after a few days to two weeks making it a useful biomarker in all but the earliest rejection episode. Levels are much higher in antibody mediated rejection (ABMR) when compared to cellular rejection offering improvements in sensitivity when considering ABMR alone (85%) versus rejection of all aetiology (59%). The investigators believe it is important to correlate the findings of the dd-cfDNA sample with the other tests described, so that observations about the percentage dd-cfDNA found in different pathologies and the overall frequency of positive results can be made. Immunological Factors in Older Age Renal Transplant and Longitudinal Donor Specific Antibodies (DSA) study Older patients who undergo kidney transplant (KT) have better survival than those who remain on the waiting list. Nevertheless, outcomes are inferior to younger recipients and KT is often felt to be a predominantly quality of life intervention for older patients. Frailty may have a beneficial influence on the risk of post-transplant adverse immunological events and rejection episodes. If transplanted with good quality organs (all barring elderly deceased after circulatory death - DCD grafts), older kidney transplant recipients will have fewer rejection episodes than younger counterparts. The investigators conducted a retrospective cohort study of the outcomes for older age transplant recipients (\>60) locally, to compare results before and after the change in allocation system which coincides with the COVID-19 pandemic. In this study, the investigators observed less favourable HLA-mismatching, a higher rate of re-intervention (operative or interventional radiology), higher rates of tertiary centre readmissions and higher 1-year mortality rates. The previous post-transplant HLA-specific antibody work examined only the first positive sample post-transplant independent of pre-transplant sensitisation status. The investigators, thus, propose prospectively recruiting patients, irrespective of age, undergoing transplant to determine the overall frequency of immunological events and de novo HLA specific antibody formation. Given the importance of early detection and intervention of antibody mediated processes and optimising treatment protocols for frail patients, the investigators aim to expand the research interest in post-transplant antibody monitoring. The investigators, thus, propose prospectively recruiting patients, irrespective of age, undergoing transplant to determine the overall frequency of immunological events and de novo HLA specific antibody formation. Data on clinical outcomes will be collected and the investigators would look to compare the \<60s with those 60 or older patients undergoing kidney transplant. Determining Predictive Models for Post-transplant HLA-specific Antibody Formation As an adjunct to the proposed study looking at the influence of age on immunological events, the investigators aim to examine the cohort as a whole to determine the differential clinical outcomes for patients with and without de novo HLA-specific antibody over time. An important part of this arm of the study will be to determine predictive models using machine learning methodology to determine those most at risk for the development of de-novo HLA specific antibody post-transplant.
Interventions
DIAGNOSTIC_TESTcf-DNA
In addition to the standard of care tests, participants will have an additional blood sample (dd-cf DNA). A cohort study patients who have undergone high immunological risk kidney transplant at our centre defined as a re-transplant, where the cRF is \>20% or where there is a level 4 HLA-mismatch. We will take a single plasma sample for dd-cfDNA testing at 6-12 months post-transplant and pair this with an assessment of renal function (creatinine, eGFR), MSU, BK and CMV PCR, single antigen bead (SAB) monitoring of HLA-specific antibodies and allograft USS.
DIAGNOSTIC_TESTImmunological Events following renal transplant in older age
Determine the overall frequency of immunological events and de novo HLA specific antibody formation in the \<60 and \>60 age population. Standard of care test taken at the different time intervals for routine storage. We will use those samples for HLA testing (either screening alone or screening and single antigen bead testing if screening yields a positive result).
OTHERDetermining Predictive Models for Post-transplant HLA-specific Antibody Formation
A machine learning model will be developed in Python using a range of pre- and post-transplant variables to determine a predictive model for de novo HLA-specific antibody following renal transplant.
Sponsors
Kidney Research United Kingdom
Liverpool University Hospitals NHS Foundation Trust
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. cf-DNA arm: * Adult patients transplanted within 6-12 months (retrospective recruitment) * Patients admitted for renal transplant or within the first 6 months following transplant (prospective recruitment) * Patients must have capacity to provide informed consent * Patients must have received a high-risk transplant defined as level 4 mismatch, cRF \>20, second or subsequent transplant, ABO or HLA incompatible 2. Older Age Immunological Events: \- Any adult patient with capacity undergoing, or within 72 hours of, a renal transplant 3. Predictive models: * Any adult patient with capacity undergoing, or within 72 hours of, a renal transplant * Unsensitized pre-transplant
Exclusion criteria
1. cf-DNA arm: * Transplanted for longer than 12 months; * Low risk transplants; * Patients lacking capacity; 2. Older Age Immunological Events: * Patients lacking capacity * Patients transplanted longer than 2 weeks 3. Predictive models: * Sensitised patients * Patients lacking capacity * Patients transplanted longer than 2 weeks
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| dd-cf DNA | 6-12 months post-transplant | Occurrence of a positive cell free DNA test in high-risk post-transplant patients at 6-12 months |
| Immunological events in older age | through study completion, an average of 1 year | Frequency of the development of immunological events in the over 60s versus \<60 cohorts |
| Longitudinal DSA monitoring | through study completion, an average of 1 year | Frequency of development of de novo HLA specific antibody in the 1st year following transplantation in patients previously unsensitised. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Comparison of renal function, viral reactivation, readmission and reoperation rates between the 2 age groups (over 60s and <60) | through study completion, an average of 1 year | as above |
| Occurrence of UTIs, viral reactivation and structural transplant abnormalities in high-risk post-transplant patients at 6-12 months | 6-12 months | as above |
| Comparison of post-transplant graft and patient survival between patients developing de novo HLA specific antibody and those who do not, using machine learning models | through study completion, an average of 1 year | as above |
| Comparison of post-transplant complications (utilising the Clavien-Dindo classification of surgical complications) between patients developing de novo HLA specific antibody and those who do not, using machine learning models | through study completion, an average of 1 year | as above |
| Association of cell free DNA result and any identified pathology (DSA, UTI, viral infection) | through study completion, an average of 1 year | as above |
| Comparison of graft and patient survival to 12 months in the over 60s and <60 | through study completion, an average of 1 year | as above |
Countries
United Kingdom
Contacts
Primary ContactGeorge E Nita, MBChB MSc MRCSEd
Backup ContactPetra M Goldsmith, MBBChir PhD FRCS
Outcome results
None listed