Development and Validation of a Multidimensional Score to Predict Long-term Kidney Transplant Outcomes

Completed

Conditions studied: Kidney Transplantation

In brief

To further develop personalized medicine in kidney transplantation and improve transplant patient outcomes, attention has been given to define early surrogate endpoints that might aid therapeutic interventions, clinical trials and clinical decision-making. Despite a clear pressing need, no population-scale prognostication system exists that will combine traditional factors and biomarker candidates to represent the complete spectrum of risk predicting parameters. To adequately predict transplant patients' individual risks of allograft loss, this would require a complex integration of data, including: donor data, recipient characteristics, transplant characteristics, allograft precision phenotypes, ethnicity, immunosuppressive regimen monitoring, allograft infections, acute kidney injuries, and recipient immune profiles. This project aims: 1. To develop a generalizable, transportable, mechanistically and data driven composite surrogate end point in kidney transplantation; 2. To validate several risk scores to predict kidney allograft survival and response to treatment of individual patients; Eventually, it will provide an easily accessible tool to calculate individual patients' risk profiles after kidney transplantation, by using datasets from prospective cohorts and post hoc analysis of randomized control trial datasets.

Key facts

Study ID
NCT03474003
Run by
Paris Translational Research Center for Organ Transplantation
People needed
7557
Starts
2002-01-01
Expected to finish
2020-04-29
Last updated by the study team
2020-05-01

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.