Multidimensional System to Dynamically Predict Graft Survival After Kidney Transplantation

Completed

• Conditions: Kidney Transplant Failure

• Registration Number: NCT04258891
• Lead Sponsor: Paris Translational Research Center for Organ Transplantation

Brief Summary

The incidence of end stage renal disease (ESRD) is rapidly increasing, now affecting an estimated 7.4 million people worldwide. Numerous parameters such as demographic, clinical and functional factors drive the deterioration of the kidney, ultimately leading to ESRD. Although some ESRD prediction models have been derived in the past years, none of these models are dynamic: they do not integrate the repeated measurements recorded throughout individuals' follow-up.

As highlighted in several studies, kidney function repeated measurements (i.e., trajectories) are highly associated with graft survival after kidney transplantation. The investigators made the hypothesis that these trajectories may bring relevant information in the context of graft survival risk prediction model. Hence, combining these trajectories with standard graft survival risk factors may enhance prediction performance. This could permit to derive a robust tool that could be updated over time by continuously capturing patient' personal evolution.

Detailed Description

850 million individuals suffer from chronic kidney disease (CKD), while diabetes, cancer, and HIV/AIDS affect 422, 42, and 37 million individuals, respectively. End stage renal disease (ESRD) hence places a heavy burden on health systems worldwide. Linked to that, the kidney-disease-associated mortality rate worldwide has risen over the past decade, now causing the death of 5 to 10 million individuals every year.

In kidney transplantation, numerous parameters such as demographic, clinical and functional factors drive the deterioration of the kidney, sometimes leading to graft failure. Current approaches for investigating the relationship between these factors and graft failure have been limited by standard statistical approaches and by registries with an overall lack on granular data, including infrequent kidney function measurements for a single patient and convenience clinical samples. Identifying the determinants of graft failure with a dynamic approach may bring an original perspective to the traditional graft survival risk prediction model that are impeded by their reliance on low-granularity datasets, cross-sectional parameters, and limited follow-up.

Study Timeline

• Study Start: 2004-01-01
• Primary Completion: 2019-12-31
• Study First Submitted: 2020-02-04
• Study First Submitted QC: 2020-02-04
• Study First Posted: 2020-02-06
• Study Completion: 2020-06-30
• Status Verified: 2020-09
• Last Update Submitted: 2020-09-14
• Last Update Posted: 2020-09-16

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 14000

Inclusion Criteria

• Kidney recipients transplanted after 2004
• Kidney recipients over 18 years of age
• Kidney recipients with at least two estimated glomerular filtration rate and proteinuria measurements after transplantation

Exclusion Criteria

• Combined transplantation

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Allograft survival probability	Up to 10 years after kidney transplantation	Allograft survival probability, calculated from a dynamic prediction system, based on clinical, histological, immunological and estimated glomerular filtration rate and proteinuria repeated measurements, assessed at the time of risk evaluation and that can be updated thereafter.

Secondary Outcome Measures

Name	Time	Method
Added prognostic value	Up to 10 years after kidney transplantation	Added prognostic value of the dynamic prediction system over standard of care monitoring of kidney transplant recipients based on single value of estimated glomerular filtration rate and proteinuria

Trial Locations

Locations (18)

William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic; 🇺🇸 Rochester, Minnesota, United States

Albert Einstein College of Medicine, Renal Division Montefiore Medical Center, Kidney Transplantation Program; 🇺🇸 New York, New York, United States

Unidad de Trasplante Renopáncreas, Centro de Educación Médica e Investigaciones Clínicas; 🇦🇷 Buenos Aires, Argentina

Universidade Federal de São Paulo, Hospital do Rim, Escola Paulista de Medicina; 🇧🇷 São Paulo, Brazil

Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Renal Transplantation Service; 🇧🇷 São Paulo, Brazil

Clinica Alemana de Santiago; 🇨🇱 Santiago, Chile

Department of Nephrology, Arterial Hypertension, Dialysis and Transplantation, University Hospital Centre Zagreb, School od Medicine University of Zagreb; 🇭🇷 Zagreb, Croatia

Department of Nephrology, Centre Hospitalier Universitaire de Montpellier; 🇫🇷 Montpellier, France

Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris; 🇫🇷 Paris, France

Nephrology Dialysis Transplantation Department, University of Lorraine, Centre Hospitalier Universitaire de Nancy; 🇫🇷 Nancy, France

Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris; 🇫🇷 Paris, France

Department of Transplantation, Nephrology and Clinical Immunology, Hôpital Foch; 🇫🇷 Suresnes, France

Department of Nephrology and Organ Transplantation, Centre Hospitalier Universitaire Rangueil; 🇫🇷 Toulouse, France

Bretonneau Hospital, Nephrology and Immunology Department; 🇫🇷 Tours, France

Department of Nephrology, Hospital del Mar; 🇪🇸 Barcelona, Spain

Division of Transplantation, Department of Surgery, Feinberg School of Medicine, Northwestern University; 🇺🇸 Chicago, Illinois, United States

Department of Medicine, Division of Nephrology, Comprehensive Transplant Center, Cedars Sinai Medical Center; 🇺🇸 Los Angeles, California, United States

Department of Surgery, Johns Hopkins University School of Medicine; 🇺🇸 Baltimore, Maryland, United States