Development and Validation of a Multidimensional Score to Predict Long-term Kidney Transplant Outcomes
- Conditions
- Kidney Transplantation
- Registration Number
- NCT03474003
- Lead Sponsor
- Paris Translational Research Center for Organ Transplantation
- Brief Summary
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.
- Detailed Description
Background The field of kidney transplantation currently lacks robust models to predict long-term allograft failure, 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.
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.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 7557
- Kidney recipient transplanted after 2002
- Kidney recipient over 18 years of age
- Combined transplantation
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Allograft survival probability Allograft survival probability at 7 year post transplantation Allograft survival probability, calculated from a composite score (based on clinical, histological, immunological, and functional variables) assessed at the time of biopsy.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (10)
Department of Surgery, Johns Hopkins University School of Medicine
🇺🇸Baltimore, Maryland, United States
Department of Nephrology and Renal Transplantation, University Hospitals Leuven
🇧🇪Leuven, Belgium
Centre Hospitalier Universitaire de Nantes
🇫🇷Nantes, France
Virginia Commonwealth University School of Medicine
🇺🇸Richmond, Virginia, United States
William J. von Liebig Center for Transplantation and Clinical Regeneration
🇺🇸Rochester, Minnesota, United States
Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon
🇫🇷Lyon, France
Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France ;
🇫🇷Paris, France
Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France;
🇫🇷Paris, France
Department of Nephrology and Organ Transplantation, CHU Rangueil
🇫🇷Toulouse, France
Department of Transplantation, Nephrology and Clinical Immunology, Hôpital Foch, Suresnes, France
🇫🇷Suresnes, France