Expanding the Scope of Post-transplant HLA-specific Antibody Detection and Monitoring in Renal Transplant Recipients (HLA-AB)

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Frailty; Kidney Transplant Rejection; Renal Transplant; Diagnosis; Kidney Transplant; Complications; Kidney Transplant; Renal Transplant Failure; Transplant Dysfunction
• Intervention / Treatment: Diagnostic test: cf-DNA; Diagnostic test: Immunological Events following renal transplant in older age; Other: Determining Predictive Models for Post-transplant HLA-specific Antibody Formation

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.

• Study Type: Observational
• Enrollment (Estimated): 282

Contacts and Locations

• Study Contact: Name: George E Nita, MD MSc MRCSEd; Phone Number: 01517062000; Email: georgeemilian.nita@liverpoolft.nhs.uk
• Study Contact Backup: Name: Petra M Goldsmith, MBBChir PhD FRCS; Phone Number: 01517055550; Email: petra.goldsmith@liverpoolft.nhs.uk

Study Locations

• United Kingdom
  • Merseyside
    • Liverpool, Merseyside, United Kingdom, L7 8XP
      • Liverpool University Hospitals NHS Foundation Trust
      • Contact: George E Nita, MD MSc MRCSEd; Phone Number: 01517062000; Email: georgeemilian.nita@liverpoolft.nhs.uk
      • Contact: Petra M Goldsmith, MBBChir PhD FRCS; Phone Number: 01517065550; Email: petra.goldsmith@liverpoolft.nhs.uk
      • Sub-Investigator: George E Nita, MD MSc MRCSEd
      • Principal Investigator: Petra M Goldsmith, MBBChir PhD FRCS

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

1. cf-DNA arm: Patients who have undergone a "high risk" renal transplant in our unit within the last 6-12 months will be retrospectively recruited to the study.
2. Older Age Immunological Events: Post-transplant patients prospectively recruited, irrespective of age, undergoing transplant to determine the overall frequency of immunological events and de novo HLA specific antibody formation. We will collect data on clinical outcomes and would look to compare the <60s with those 60 or older looking for a difference in immunological events beteween the 2 groups.
3. Predictive models: A subset of the cohort of recruits to the immunological factors in older age study will be used to determine machine learning algorithms of predictive factors for the development of de novo donor specific antibody.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
cf-DNA arm; Participants will be recruited on the basis of having received a renal transplant within the last 6-12 months which has been deemed high risk. They will be identified from a database currently held within the renal transplant unit by members of the direct care team. They will be approached at a routine outpatient appointment for inclusion into the study and testing can be performed at their time of routine post-transplant testing where they will undergo blood (dd-cf DNA NGS assay), standard of care tests: blood count, renal profile, donor specific antibodies (DSA) sample, BK virus PCR, CMV PCR, urine testing and an ultrasound of the graft.	Diagnostic test: cf-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.; Other Names: donor derived cell-free DNA (dd-cf DNA)
Immunological Events following renal transplant in older age; The renal transplant population will be divided on the basis of age into two cohort: ≥60 and <60. All renal transplant patients are regularly followed up in the outpatient clinic where blood and urine tests are collected, and clinical evaluations are performed. It is not foreseen that this study will necessitate any additional hospital visits or testing above and beyond the usual standard of care. Serum samples are taken at the time intervals indicated for routine storage and we will simply use those samples for HLA testing (either screening alone or screening and single antigen bead testing if screening yields a positive result).	Diagnostic test: Immunological 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).; Other Names: Longitudinal DSA monitoring
Determining Predictive Models for Post-transplant HLA-specific Antibody Formation; A subset of the cohort of recruits to the immunological factors in older age study will be used to determine machine learning algorithms of predictive factors for the development of de novo donor specific antibody. Only patients who were unsensitised prior to the kidney transplant will be included into the study because prior sensitisation makes determining de novo specificities much harder. The follow up period will be set at 1 year to synchronise with the older age study.	Other: Determining 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.; Other Names: Machine learning algorithms

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
dd-cf DNA	Occurrence of a positive cell free DNA test in high-risk post-transplant patients at 6-12 months	6-12 months post-transplant
Immunological events in older age	Frequency of the development of immunological events in the over 60s versus <60 cohorts	through study completion, an average of 1 year
Longitudinal DSA monitoring	Frequency of development of de novo HLA specific antibody in the 1st year following transplantation in patients previously unsensitised.	through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Occurrence of UTIs, viral reactivation and structural transplant abnormalities in high-risk post-transplant patients at 6-12 months	as above	6-12 months
Association of cell free DNA result and any identified pathology (DSA, UTI, viral infection)	as above	through study completion, an average of 1 year
Comparison of graft and patient survival to 12 months in the over 60s and <60	as above	through study completion, an average of 1 year
Comparison of renal function, viral reactivation, readmission and reoperation rates between the 2 age groups (over 60s and <60)	as above	through study completion, an average of 1 year
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	as above	through study completion, an average of 1 year
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	as above	through study completion, an average of 1 year

Collaborators and Investigators

• Sponsor: Liverpool University Hospitals NHS Foundation Trust
• Collaborators: Kidney Research United Kingdom
• Investigators: Principal Investigator: Petra M Goldsmith, MBBChir PhD FRCS, Liverpool University Hospitals NHS Foundation Trust

Study record dates

Study Major Dates

• Study Start (Estimated): September 1, 2023
• Primary Completion (Estimated): September 1, 2025
• Study Completion (Estimated): September 1, 2026

Study Registration Dates

• First Submitted: August 22, 2023
• First Submitted That Met QC Criteria: August 29, 2023
• First Posted (Actual): September 6, 2023

Study Record Updates

• Last Update Posted (Actual): September 6, 2023
• Last Update Submitted That Met QC Criteria: August 29, 2023
• Last Verified: August 1, 2023

More Information

Terms related to this study

• Keywords: machine learning; rejection; kidney transplant; predictive models; donor specific antibodies; novel biomarkers; post-transplant antibodies; HLA-specific antibodies; transplant in older age
• Additional Relevant MeSH Terms: Pathologic Processes; Frailty; Physiological Effects of Drugs; Immunologic Factors; Antibodies
• Other Study ID Numbers: LHS0037

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No