xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT06025240</url>
    </required_header>
    <id_info>
      <org_study_id>LHS0037</org_study_id>
      <nct_id>NCT06025240</nct_id>
    </id_info>
    <brief_title>Expanding the Scope of Post-transplant HLA-specific Antibody Detection and Monitoring in Renal Transplant Recipients</brief_title>
    <acronym>HLA-AB</acronym>
    <official_title>Expanding the Scope of Post-transplant HLA-specific Antibody Detection and Monitoring in Renal Transplant Recipients</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Liverpool University Hospitals NHS Foundation Trust</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Kidney Research United Kingdom</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Liverpool University Hospitals NHS Foundation Trust</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The purpose of this study is to assess a new test to detect antibodies which may form&#xD;
        following kidney transplant. These antibodies can be difficult to detect as they do not cause&#xD;
        any symptoms but can lead to kidney damage. A new blood test will be performed alongside&#xD;
        existing antibody tests to see how well the test functions in comparison and to see how well&#xD;
        it is able to distinguish between inflammation caused by antibodies and other sorts of&#xD;
        inflammation such as a urinary tract infection. The investigators also want to determine&#xD;
        whether it is predictable whom will develop antibodies after a transplant and use these&#xD;
        results to change the current way patients are monitored for antibodies after receiving a&#xD;
        transplant. In addition to this, the investigators want to establish if patients over 60&#xD;
        years of age are relatively protected against immunological events such as rejection compared&#xD;
        to patients who are under 60 years of age. The results could potentially lead to using a&#xD;
        different immunosuppression regime based on which population age group patients belong to and&#xD;
        lowering the risks associated with these drugs.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Donor-derived cell-free DNA (dd-cfDNA)&#xD;
  &#xD;
        Post-transplant monitoring for acute rejection in most centres, focuses on identification of&#xD;
        a deterioration in graft function which may be totally asymptomatic. The current best&#xD;
        practice to investigate a suspected rejection are renal graft biopsy with appropriate&#xD;
        staining for complement component C4d and a serum single antigen bead (SAB) testing. This&#xD;
        reactive approach to assessing and monitoring rejection, mainly driven by serial assessments&#xD;
        of renal function to determine response to treatment, avoids the need for multiple invasive&#xD;
        diagnostic tests such as biopsies, but it poses the risk of missing the early detection and&#xD;
        treatment of rejection prior to an objective decline in function. A &quot;creeping creatinine&quot;,&#xD;
        where there are small but sustained increases in creatinine at sequential visits is&#xD;
        relatively common and by the time a deterioration consistent with rejection is observed there&#xD;
        can already have been significant tissue damage.&#xD;
  &#xD;
        Donor-derived cell-free DNA (dd-cfDNA) has been described as a useful biomarker for graft&#xD;
        injury secondary to rejection which can be evident in blood weeks to months prior to&#xD;
        histological evidence of graft injury. dd-cfDNA levels are high in the immediate&#xD;
        post-operative phase, although there is a sharp drop off to low baseline levels after a few&#xD;
        days to two weeks making it a useful biomarker in all but the earliest rejection episode.&#xD;
        Levels are much higher in antibody mediated rejection (ABMR) when compared to cellular&#xD;
        rejection offering improvements in sensitivity when considering ABMR alone (85%) versus&#xD;
        rejection of all aetiology (59%). The investigators believe it is important to correlate the&#xD;
        findings of the dd-cfDNA sample with the other tests described, so that observations about&#xD;
        the percentage dd-cfDNA found in different pathologies and the overall frequency of positive&#xD;
        results can be made.&#xD;
  &#xD;
        Immunological Factors in Older Age Renal Transplant and Longitudinal Donor Specific&#xD;
        Antibodies (DSA) study&#xD;
  &#xD;
        Older patients who undergo kidney transplant (KT) have better survival than those who remain&#xD;
        on the waiting list. Nevertheless, outcomes are inferior to younger recipients and KT is&#xD;
        often felt to be a predominantly quality of life intervention for older patients. Frailty may&#xD;
        have a beneficial influence on the risk of post-transplant adverse immunological events and&#xD;
        rejection episodes. If transplanted with good quality organs (all barring elderly deceased&#xD;
        after circulatory death - DCD grafts), older kidney transplant recipients will have fewer&#xD;
        rejection episodes than younger counterparts. The investigators conducted a retrospective&#xD;
        cohort study of the outcomes for older age transplant recipients (&gt;60) locally, to compare&#xD;
        results before and after the change in allocation system which coincides with the COVID-19&#xD;
        pandemic. In this study, the investigators observed less favourable HLA-mismatching, a higher&#xD;
        rate of re-intervention (operative or interventional radiology), higher rates of tertiary&#xD;
        centre readmissions and higher 1-year mortality rates.&#xD;
  &#xD;
        The previous post-transplant HLA-specific antibody work examined only the first positive&#xD;
        sample post-transplant independent of pre-transplant sensitisation status. The investigators,&#xD;
        thus, propose prospectively recruiting patients, irrespective of age, undergoing transplant&#xD;
        to determine the overall frequency of immunological events and de novo HLA specific antibody&#xD;
        formation. Given the importance of early detection and intervention of antibody mediated&#xD;
        processes and optimising treatment protocols for frail patients, the investigators aim to&#xD;
        expand the research interest in post-transplant antibody monitoring. The investigators, thus,&#xD;
        propose prospectively recruiting patients, irrespective of age, undergoing transplant to&#xD;
        determine the overall frequency of immunological events and de novo HLA specific antibody&#xD;
        formation. Data on clinical outcomes will be collected and the investigators would look to&#xD;
        compare the &lt;60s with those 60 or older patients undergoing kidney transplant.&#xD;
  &#xD;
        Determining Predictive Models for Post-transplant HLA-specific Antibody Formation&#xD;
  &#xD;
        As an adjunct to the proposed study looking at the influence of age on immunological events,&#xD;
        the investigators aim to examine the cohort as a whole to determine the differential clinical&#xD;
        outcomes for patients with and without de novo HLA-specific antibody over time. An important&#xD;
        part of this arm of the study will be to determine predictive models using machine learning&#xD;
        methodology to determine those most at risk for the development of de-novo HLA specific&#xD;
        antibody post-transplant.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 1, 2023</start_date>
    <completion_date type="Anticipated">September 1, 2026</completion_date>
    <primary_completion_date type="Anticipated">September 1, 2025</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Other</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>dd-cf DNA</measure>
      <time_frame>6-12 months post-transplant</time_frame>
      <description>Occurrence of a positive cell free DNA test in high-risk post-transplant patients at 6-12 months</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Immunological events in older age</measure>
      <time_frame>through study completion, an average of 1 year</time_frame>
      <description>Frequency of the development of immunological events in the over 60s versus &lt;60 cohorts</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Longitudinal DSA monitoring</measure>
      <time_frame>through study completion, an average of 1 year</time_frame>
      <description>Frequency of development of de novo HLA specific antibody in the 1st year following transplantation in patients previously unsensitised.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Occurrence of UTIs, viral reactivation and structural transplant abnormalities in high-risk post-transplant patients at 6-12 months</measure>
      <time_frame>6-12 months</time_frame>
      <description>as above</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Association of cell free DNA result and any identified pathology (DSA, UTI, viral infection)</measure>
      <time_frame>through study completion, an average of 1 year</time_frame>
      <description>as above</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Comparison of graft and patient survival to 12 months in the over 60s and &lt;60</measure>
      <time_frame>through study completion, an average of 1 year</time_frame>
      <description>as above</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Comparison of renal function, viral reactivation, readmission and reoperation rates between the 2 age groups (over 60s and &lt;60)</measure>
      <time_frame>through study completion, an average of 1 year</time_frame>
      <description>as above</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>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</measure>
      <time_frame>through study completion, an average of 1 year</time_frame>
      <description>as above</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>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</measure>
      <time_frame>through study completion, an average of 1 year</time_frame>
      <description>as above</description>
    </secondary_outcome>
    <number_of_groups>3</number_of_groups>
    <enrollment type="Anticipated">282</enrollment>
    <condition>Kidney Transplant</condition>
    <condition>Renal Transplant Failure</condition>
    <condition>Kidney Transplant Rejection</condition>
    <condition>Frailty</condition>
    <condition>Kidney Transplant; Complications</condition>
    <condition>Transplant Dysfunction</condition>
    <condition>Diagnosis</condition>
    <condition>Renal Transplant</condition>
    <arm_group>
      <arm_group_label>cf-DNA arm</arm_group_label>
      <description>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.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Immunological Events following renal transplant in older age</arm_group_label>
      <description>The renal transplant population will be divided on the basis of age into two cohort: ≥60 and &lt;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).</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Determining Predictive Models for Post-transplant HLA-specific Antibody Formation</arm_group_label>
      <description>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.</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>cf-DNA</intervention_name>
      <description>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 &gt;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.</description>
      <arm_group_label>cf-DNA arm</arm_group_label>
      <other_name>donor derived cell-free DNA (dd-cf DNA)</other_name>
    </intervention>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>Immunological Events following renal transplant in older age</intervention_name>
      <description>Determine the overall frequency of immunological events and de novo HLA specific antibody formation in the &lt;60 and &gt;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).</description>
      <arm_group_label>Immunological Events following renal transplant in older age</arm_group_label>
      <other_name>Longitudinal DSA monitoring</other_name>
    </intervention>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Determining Predictive Models for Post-transplant HLA-specific Antibody Formation</intervention_name>
      <description>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.</description>
      <arm_group_label>Determining Predictive Models for Post-transplant HLA-specific Antibody Formation</arm_group_label>
      <other_name>Machine learning algorithms</other_name>
    </intervention>
    <biospec_retention>Samples With DNA</biospec_retention>
    <biospec_descr>
      <textblock>
        Donor-derived cell-free DNA next generation sequencing assay (dd-cfDNA) Donor Specific&#xD;
        Antibody (screen +/- single antigen bead) (DSA)&#xD;
      </textblock>
    </biospec_descr>
    <eligibility>
      <study_pop>
        <textblock>
          1. cf-DNA arm: Patients who have undergone a &quot;high risk&quot; renal transplant in our unit&#xD;
               within the last 6-12 months will be retrospectively recruited to the study.&#xD;
  &#xD;
            2. Older Age Immunological Events: Post-transplant patients prospectively recruited,&#xD;
               irrespective of age, undergoing transplant to determine the overall frequency of&#xD;
               immunological events and de novo HLA specific antibody formation. We will collect data&#xD;
               on clinical outcomes and would look to compare the &lt;60s with those 60 or older looking&#xD;
               for a difference in immunological events beteween the 2 groups.&#xD;
  &#xD;
            3. Predictive models: A subset of the cohort of recruits to the immunological factors in&#xD;
               older age study will be used to determine machine learning algorithms of predictive&#xD;
               factors for the development of de novo donor specific antibody.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. cf-DNA arm:&#xD;
  &#xD;
                 -  Adult patients transplanted within 6-12 months (retrospective recruitment)&#xD;
  &#xD;
                 -  Patients admitted for renal transplant or within the first 6 months following&#xD;
                    transplant (prospective recruitment)&#xD;
  &#xD;
                 -  Patients must have capacity to provide informed consent&#xD;
  &#xD;
                 -  Patients must have received a high-risk transplant defined as level 4 mismatch,&#xD;
                    cRF &gt;20, second or subsequent transplant, ABO or HLA incompatible&#xD;
  &#xD;
            2. Older Age Immunological Events:&#xD;
  &#xD;
               - Any adult patient with capacity undergoing, or within 72 hours of, a renal&#xD;
               transplant&#xD;
  &#xD;
            3. Predictive models:&#xD;
  &#xD;
                 -  Any adult patient with capacity undergoing, or within 72 hours of, a renal&#xD;
                    transplant&#xD;
  &#xD;
                 -  Unsensitized pre-transplant&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. cf-DNA arm:&#xD;
  &#xD;
                 -  Transplanted for longer than 12 months;&#xD;
  &#xD;
                 -  Low risk transplants;&#xD;
  &#xD;
                 -  Patients lacking capacity;&#xD;
  &#xD;
            2. Older Age Immunological Events:&#xD;
  &#xD;
                 -  Patients lacking capacity&#xD;
  &#xD;
                 -  Patients transplanted longer than 2 weeks&#xD;
  &#xD;
            3. Predictive models:&#xD;
  &#xD;
                 -  Sensitised patients&#xD;
  &#xD;
                 -  Patients lacking capacity&#xD;
  &#xD;
                 -  Patients transplanted longer than 2 weeks&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Petra M Goldsmith, MBBChir PhD FRCS</last_name>
      <role>Principal Investigator</role>
      <affiliation>Liverpool University Hospitals NHS Foundation Trust</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>George E Nita, MD MSc MRCSEd</last_name>
      <phone>01517062000</phone>
      <email>georgeemilian.nita@liverpoolft.nhs.uk</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Petra M Goldsmith, MBBChir PhD FRCS</last_name>
      <phone>01517055550</phone>
      <email>petra.goldsmith@liverpoolft.nhs.uk</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Liverpool University Hospitals NHS Foundation Trust</name>
        <address>
          <city>Liverpool</city>
          <state>Merseyside</state>
          <zip>L7 8XP</zip>
          <country>United Kingdom</country>
        </address>
      </facility>
      <contact>
        <last_name>George E Nita, MD MSc MRCSEd</last_name>
        <phone>01517062000</phone>
        <email>georgeemilian.nita@liverpoolft.nhs.uk</email>
      </contact>
      <contact_backup>
        <last_name>Petra M Goldsmith, MBBChir PhD FRCS</last_name>
        <phone>01517065550</phone>
        <email>petra.goldsmith@liverpoolft.nhs.uk</email>
      </contact_backup>
      <investigator>
        <last_name>George E Nita, MD MSc MRCSEd</last_name>
        <role>Sub-Investigator</role>
      </investigator>
      <investigator>
        <last_name>Petra M Goldsmith, MBBChir PhD FRCS</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location_countries>
      <country>United Kingdom</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 22, 2023</study_first_submitted>
    <study_first_submitted_qc>August 29, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 6, 2023</study_first_posted>
    <last_update_submitted>August 29, 2023</last_update_submitted>
    <last_update_submitted_qc>August 29, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 6, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>kidney transplant</keyword>
    <keyword>novel biomarkers</keyword>
    <keyword>post-transplant antibodies</keyword>
    <keyword>HLA-specific antibodies</keyword>
    <keyword>donor specific antibodies</keyword>
    <keyword>transplant in older age</keyword>
    <keyword>machine learning</keyword>
    <keyword>predictive models</keyword>
    <keyword>rejection</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Frailty</mesh_term>
    </condition_browse>
    <intervention_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Antibodies</mesh_term>
    </intervention_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT06025240
  org study id: LHS0037
  nct id: NCT06025240
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  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.
  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.
  observational model: Cohort
  time perspective: Other
  measure: dd-cf DNA
  time frame: 6-12 months post-transplant
  description: Occurrence of a positive cell free DNA test in high-risk post-transplant patients at 6-12 months
  measure: Immunological events in older age
  time frame: through study completion, an average of 1 year
  description: Frequency of the development of immunological events in the over 60s versus <60 cohorts
  measure: Longitudinal DSA monitoring
  time frame: through study completion, an average of 1 year
  description: Frequency of development of de novo HLA specific antibody in the 1st year following transplantation in patients previously unsensitised.
  measure: Occurrence of UTIs, viral reactivation and structural transplant abnormalities in high-risk post-transplant patients at 6-12 months
  time frame: 6-12 months
  description: as above
  measure: Association of cell free DNA result and any identified pathology (DSA, UTI, viral infection)
  time frame: through study completion, an average of 1 year
  description: as above
  measure: Comparison of graft and patient survival to 12 months in the over 60s and <60
  time frame: through study completion, an average of 1 year
  description: as above
  measure: Comparison of renal function, viral reactivation, readmission and reoperation rates between the 2 age groups (over 60s and <60)
  time frame: through study completion, an average of 1 year
  description: as above
  measure: 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
  time frame: through study completion, an average of 1 year
  description: as above
  measure: 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
  time frame: through study completion, an average of 1 year
  description: as above
  arm group label: cf-DNA arm
  description: 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.
  arm group label: Immunological Events following renal transplant in older age
  description: 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).
  arm group label: Determining Predictive Models for Post-transplant HLA-specific Antibody Formation
  description: 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.
  intervention type: Diagnostic Test
  intervention name: cf-DNA
  description: 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.
  arm group label: cf-DNA arm
  other name: donor derived cell-free DNA (dd-cf DNA)
  intervention type: Diagnostic Test
  intervention name: Immunological Events following renal transplant in older age
  description: 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).
  arm group label: Immunological Events following renal transplant in older age
  other name: Longitudinal DSA monitoring
  intervention type: Other
  intervention name: Determining Predictive Models for Post-transplant HLA-specific Antibody Formation
  description: 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.
  arm group label: Determining Predictive Models for Post-transplant HLA-specific Antibody Formation
  other name: Machine learning algorithms
  Donor-derived cell-free DNA next generation sequencing assay (dd-cfDNA) Donor Specific
        Antibody (screen +/- single antigen bead) (DSA)
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Petra M Goldsmith, MBBChir PhD FRCS
  role: Principal Investigator
  affiliation: Liverpool University Hospitals NHS Foundation Trust
  last name: George E Nita, MD MSc MRCSEd
  phone: 01517062000
  email: georgeemilian.nita@liverpoolft.nhs.uk
  last name: Petra M Goldsmith, MBBChir PhD FRCS
  phone: 01517055550
  email: petra.goldsmith@liverpoolft.nhs.uk
  facility: 
  contact: 
  contact backup: 
  investigator: 
  investigator: 
  country: United Kingdom
  responsible party type: Sponsor
  mesh term: Frailty
  mesh term: Antibodies
  sharing ipd: Undecided