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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:
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, 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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No intervention | Other | 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 available for allograft survival assessment; RCT conducted over the past 20 years with available data on protocol biopsy within the first year and follow up clinical, biological and histological data. |
| Measure | Description | Time Frame |
|---|---|---|
| Allograft survival probability | Allograft survival probability, calculated from a composite score (based on clinical, histological, immunological, and functional variables) assessed at the time of biopsy. | Allograft survival probability at 7 year post transplantation |
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Inclusion Criteria:
Exclusion Criteria:
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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.
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| Name | Affiliation | Role |
|---|---|---|
| Alexandre Loupy, PhD | Paris Translational Research Center for Organ Transplantation | Principal Investigator |
| Carmen Lefaucheur, PhD | Paris Translational Research Center for Organ Transplantation | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Surgery, Johns Hopkins University School of Medicine | Baltimore | Maryland | 21205 | United States | ||
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40168162 | Derived | Truchot A, Raynaud M, Helantera I, Aubert O, Kamar N, Divard G, Astor B, Legendre C, Hertig A, Buchler M, Crespo M, Akalin E, Pujol GS, Ribeiro de Castro MC, Matas AJ, Ulloa C, Jordan SC, Huang E, Juric I, Basic-Jukic N, Coemans M, Naesens M, Friedewald JJ, Silva HT Jr, Lefaucheur C, Segev DL, Collins GS, Loupy A. Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure. J Am Soc Nephrol. 2025 Apr 1;36(4):688-701. doi: 10.1681/ASN.0000000517. Epub 2024 Oct 16. | |
| 34620664 |
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| William J. von Liebig Center for Transplantation and Clinical Regeneration |
| Rochester |
| Minnesota |
| 55905 |
| United States |
| Virginia Commonwealth University School of Medicine | Richmond | Virginia | 980663 | United States |
| Department of Nephrology and Renal Transplantation, University Hospitals Leuven | Leuven | 3000 | Belgium |
| Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon | Lyon | 69002 | France |
| Centre Hospitalier Universitaire de Nantes | Nantes | 44093 | France |
| Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France ; | Paris | 75010 | France |
| Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France; | Paris | 7509 | France |
| Department of Transplantation, Nephrology and Clinical Immunology, Hôpital Foch, Suresnes, France | Suresnes | 92150 | France |
| Department of Nephrology and Organ Transplantation, CHU Rangueil | Toulouse | 31059 | France |
| Derived |
| 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. |
| 31530561 | Derived | 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. |