Development and Validation of a Multidimensional Score to Predict Long-term Kidney Transplant Outcomes
| Status: | Recruiting |
|---|---|
| Conditions: | Renal Impairment / Chronic Kidney Disease |
| Therapuetic Areas: | Nephrology / Urology |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 5/25/2018 |
| Start Date: | January 2002 |
| End Date: | December 2019 |
| Contact: | Alexandre Loupy, PhD |
| Email: | alexandreloupy@gmail.com |
| Phone: | 0033612491082 |
Multicenter International Observational Study to Build and Validate Multidimensional Risk Score in the Clinical Setting of Kidney Allograft Biopsies to Predict Long-term Allograft Survival and Allograft Function Trajectories
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.
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.
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).
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).
Inclusion Criteria:
- Kidney recipient transplanted after 2002
- Kidney recipient over 18 years of age
Exclusion Criteria:
- Combined transplantation
We found this trial at
4
sites
Click here to add this to my saved trials
Click here to add this to my saved trials
Click here to add this to my saved trials
Rochester, Minnesota 55905
Click here to add this to my saved trials