Ignite Creation Date:
2025-12-24 @ 7:14 PM
Ignite Modification Date:
2025-12-25 @ 4:50 PM
Study NCT ID:
NCT03474003
Status:
COMPLETED
Last Update Posted:
2020-05-01
First Post:
2018-03-06
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Development and Validation of a Multidimensional Score to Predict Long-term Kidney Transplant Outcomes
Sponsor:
Paris Translational Research Center for Organ Transplantation
Organization:
Study Overview
Official Title:
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
Status:
COMPLETED
Status Verified Date:
2020-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
iBOX
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.
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.
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.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: