Clinical trial NCT03474003: Development and Validation of a Multidimensional Score to Predict Long-term Kidney Transplant Outcomes
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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Kidney Transplantation
Keywords: Score prediction
Allograft survival
Lead sponsor: Paris Translational Research Center for Organ Transplantation
Sponsor class: OTHER
First posted: 2018-03-22
Enrollment: 7557
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Kidney recipients aged over 18 and of all sexes recruited from 2002 in European and North American centers, who have eGFR follow-up and data from protocol and for cause biopsies for allograft survival assessment as well as RCTs with longitudinal data including baseline and follow-up clinical, functional, immunological and histological data.

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.

Eligibility criteria: Inclusion Criteria:

* Kidney recipient transplanted after 2002
* Kidney recipient over 18 years of age

Exclusion Criteria:

* Combined transplantation

Primary outcomes:
measure: Allograft survival probability
timeFrame: Allograft survival probability at 7 year post transplantation
Secondary outcomes:
Other outcomes:
Locations:
city: Baltimore
country: United States
facility: Department of Surgery, Johns Hopkins University School of Medicine
state: Maryland
city: Rochester
country: United States
facility: William J. von Liebig Center for Transplantation and Clinical Regeneration
state: Minnesota
city: Richmond
country: United States
facility: Virginia Commonwealth University School of Medicine
state: Virginia
city: Leuven
country: Belgium
facility: Department of Nephrology and Renal Transplantation, University Hospitals Leuven
city: Lyon
country: France
facility: Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon
city: Nantes
country: France
facility: Centre Hospitalier Universitaire de Nantes
city: Paris
country: France
facility: Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France ;
city: Paris
country: France
facility: Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France;
city: Suresnes
country: France
facility: Department of Transplantation, Nephrology and Clinical Immunology, Hôpital Foch, Suresnes, France
city: Toulouse
country: France
facility: Department of Nephrology and Organ Transplantation, CHU Rangueil
Study officials:
affiliation: Paris Translational Research Center for Organ Transplantation
name: Alexandre Loupy, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Paris Translational Research Center for Organ Transplantation
name: Carmen Lefaucheur, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Aubert O, Divard G, Pascual J, Oppenheimer F, Sommerer C, Citterio F, Tedesco H, Chadban S, Henry M, Vincenti F, Srinivas T, Watarai Y, Legendre C, Bernhardt P, Loupy A. Application of the iBox prognostication system as a surrogate endpoint in the TRANSFORM randomised controlled trial: proof-of-concept study. BMJ Open. 2021 Oct 7;11(10):e052138. doi: 10.1136/bmjopen-2021-052138.
citation: Loupy A, Aubert O, Orandi BJ, Naesens M, Bouatou Y, Raynaud M, Divard G, Jackson AM, Viglietti D, Giral M, Kamar N, Thaunat O, Morelon E, Delahousse M, Kuypers D, Hertig A, Rondeau E, Bailly E, Eskandary F, Bohmig G, Gupta G, Glotz D, Legendre C, Montgomery RA, Stegall MD, Empana JP, Jouven X, Segev DL, Lefaucheur C. Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study. BMJ. 2019 Sep 17;366:l4923. doi: 10.1136/bmj.l4923.