Clinical trial NCT04258891: Multidimensional System to Dynamically Predict Graft Survival After Kidney Transplantation
Official title: Development and Validation of a Multidimensional System to Dynamically Predict Graft Survival After Kidney Transplantation
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Kidney Transplant Failure
Keywords: Kidney Transplantation
Graft survival
Kidney function
Prediction
Dynamic
Lead sponsor: Paris Translational Research Center for Organ Transplantation
Sponsor class: OTHER
First posted: 2020-02-06
Enrollment: 14000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Kidney recipients aged over 18 and of all sexes recruited from 2004 in European and North American centers, who have estimated glomerular filtration rate and proteinuria repeated measurements, data from protocol and for cause biopsies, as well as immunological, clinical, functional parameters and survival data for allograft and patient survival assessment. Additional kidney recipients from RCTs with longitudinal data including baseline and follow-up clinical, functional, immunological and histological data.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Allograft survival probability
timeFrame: Up to 10 years after kidney transplantation
Secondary outcomes:
measure: Added prognostic value
timeFrame: Up to 10 years after kidney transplantation
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: Department of Medicine, Division of Nephrology, Comprehensive Transplant Center, Cedars Sinai Medical Center
state: California
city: Chicago
country: United States
facility: Division of Transplantation, Department of Surgery, Feinberg School of Medicine, Northwestern University
state: Illinois
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, Mayo Clinic
state: Minnesota
city: New York
country: United States
facility: Albert Einstein College of Medicine, Renal Division Montefiore Medical Center, Kidney Transplantation Program
state: New York
city: Buenos Aires
country: Argentina
facility: Unidad de Trasplante Renopáncreas, Centro de Educación Médica e Investigaciones Clínicas
city: São Paulo
country: Brazil
facility: Universidade Federal de São Paulo, Hospital do Rim, Escola Paulista de Medicina
city: São Paulo
country: Brazil
facility: Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Renal Transplantation Service
city: Santiago
country: Chile
facility: Clinica Alemana de Santiago
city: Zagreb
country: Croatia
facility: Department of Nephrology, Arterial Hypertension, Dialysis and Transplantation, University Hospital Centre Zagreb, School od Medicine University of Zagreb
city: Montpellier
country: France
facility: Department of Nephrology, Centre Hospitalier Universitaire de Montpellier
city: Nancy
country: France
facility: Nephrology Dialysis Transplantation Department, University of Lorraine, Centre Hospitalier Universitaire de Nancy
city: Paris
country: France
facility: Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris
city: Paris
country: France
facility: Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris
city: Suresnes
country: France
facility: Department of Transplantation, Nephrology and Clinical Immunology, Hôpital Foch
city: Toulouse
country: France
facility: Department of Nephrology and Organ Transplantation, Centre Hospitalier Universitaire Rangueil
city: Tours
country: France
facility: Bretonneau Hospital, Nephrology and Immunology Department
city: Barcelona
country: Spain
facility: Department of Nephrology, Hospital del Mar
Study officials:
affiliation: Paris Translational Research Center for Organ Transplantation
name: Alexandre Loupy, Professor
role: PRINCIPAL_INVESTIGATOR
References:
citation: Raynaud M, Aubert O, Divard G, Reese PP, Kamar N, Yoo D, Chin CS, Bailly E, Buchler M, Ladriere M, Le Quintrec M, Delahousse M, Juric I, Basic-Jukic N, Crespo M, Silva HT Jr, Linhares K, Ribeiro de Castro MC, Soler Pujol G, Empana JP, Ulloa C, Akalin E, Bohmig G, Huang E, Stegall MD, Bentall AJ, Montgomery RA, Jordan SC, Oberbauer R, Segev DL, Friedewald JJ, Jouven X, Legendre C, Lefaucheur C, Loupy A. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021 Dec;3(12):e795-e805. doi: 10.1016/S2589-7500(21)00209-0. Epub 2021 Oct 28.