Last updated on May 2018

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

Brief description of study

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 and allograft function trajectories, 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;
  3. To predict individual patient kidney function trajectories;
  4. To dynamically predict kidney allograft survival with the help of evolution overtime of selected clinical and biological parameters.

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.

Detailed Study Description

Background The field of kidney transplantation currently lacks robust models to predict long-term allograft failure and allograft function trajectories, which represents a major unmet need in clinical care and clinical trials. This study aims to generate and validate an accessible scoring system that predicts individual patients' risk of long-term kidney allograft failure and allograft function trajectories.

Main Outcome(s) and Measure(s)

A score based on classical statistical approaches to model determinants of allograft and patient survival (Cox model, multinomial regression). These models will be further completed with statistical approaches derived from artificial intelligence and machine learning.

A final approach combining the Cox Model and the e-GFR measurements will be used to create a dynamic prediction model, that takes into account the cross-sectional aspect of variables assessed at the time of transplant at 1-year post transplant and the longitudinal aspect of kidney function (joint modelling).

Clinical Study Identifier: NCT03474003

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