Development and Validation of a Multidimensional Score to Predict Long-term Kidney Transplant Outcomes
NCT ID: NCT03474003
Last Updated: 2020-05-01
Study Results
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Basic Information
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COMPLETED
7557 participants
OBSERVATIONAL
2002-01-31
2020-04-29
Brief Summary
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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.
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Detailed Description
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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.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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No intervention
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.
Eligibility Criteria
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Inclusion Criteria
* Kidney recipient over 18 years of age
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Paris Translational Research Center for Organ Transplantation
OTHER
Responsible Party
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Professor Alexandre Loupy
Professor Alexandre Loupy
Principal Investigators
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Alexandre Loupy, PhD
Role: PRINCIPAL_INVESTIGATOR
Paris Translational Research Center for Organ Transplantation
Carmen Lefaucheur, PhD
Role: PRINCIPAL_INVESTIGATOR
Paris Translational Research Center for Organ Transplantation
Locations
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Department of Surgery, Johns Hopkins University School of Medicine
Baltimore, Maryland, United States
William J. von Liebig Center for Transplantation and Clinical Regeneration
Rochester, Minnesota, United States
Virginia Commonwealth University School of Medicine
Richmond, Virginia, United States
Department of Nephrology and Renal Transplantation, University Hospitals Leuven
Leuven, , Belgium
Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon
Lyon, , France
Centre Hospitalier Universitaire de Nantes
Nantes, , 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 Transplantation, Nephrology and Clinical Immunology, Hôpital Foch, Suresnes, France
Suresnes, , France
Department of Nephrology and Organ Transplantation, CHU Rangueil
Toulouse, , France
Countries
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References
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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.
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.
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.
Other Identifiers
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IBOX001
Identifier Type: -
Identifier Source: org_study_id
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