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MR-proADM as a Early Biomarker for DGF and AR in Kidney and Liver Transplantation

ConditionsKidney Transplant; ComplicationsLiver Transplant; Complications
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-70
SponsorUniversity of Rome Tor Vergata

About this trial

To define the sensibility and the specificity of increased levels of MR-proADM for early, non-invasive, diagnosis of AR and DGF after kidney and liver transplantation creating a predictive model for related complications after kidney and liver transplantation based on the pre-operative and post-operative levels of MR-proADM and by a machine learning process.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Kidney transplant recipient at our Institution

Liver transplant recipient at our Institution

Disqualifiers

Re-transplantation

Dual kidney transplantation

Combined transplant (kidney-liver, kidney-pancreas)

Autoimmune disease as indication to transplant

Trial population

Any adult kidney or liver transplant recipients who underwent OLT or KT at the University of Rome Tor Vergata with no indication for autoimmune diseases or for combined/dual transplant or re-transplantation

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • MR-proADM dosage

    Biological/Vaccine

    Dosage of MR proADM at OLT/KT, 1, 3, 5, 15 POD and 1, 3, 6, 9, 12 months F-U

Treatment groups

300 Participants
are divided into 2 treatment groups
Group A: OLT1 intervention
Group B: KT1 intervention

Trial outcomes

Primary outcomes

1

Accuracy of MR-proADM as biomarker of DGF and AR in OLT/KT

To define the sensibility and the specificity of increased levels of MR-proADM for early, non-invasive, diagnosis of AR and DGF after kidney and liver transplantation.

Time frame
3 years

Secondary outcomes

1

Accuracy of MR-proAMD for early detection of other complications in OLT/KT

Creating a predictive model of complications after kidney and liver transplantation based on the pre-operative and post-operative levels of MR-proADM by machine learning process.

Time frame
3 years
2

Algorithm for risk prediction

Development of a software algorithm predicting the risk of post-transplant complications

Time frame
3 years
3

Digital Pathology Dataset

Development of digital pathology for graft biopsy

Time frame
3 years
4

MR-proADM Online Dataset

Development of an online platform to collect and correlate data on post-transplant biopsy and MRPro-ADM levels.

Time frame
3 years

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

University of Rome Tor Vergata

Lead sponsor

Source ClinicalTrials.gov
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