Multidimensional System to Dynamically Predict Graft Survival After Kidney Transplantation (DYNAKT)

Development and Validation of a Multidimensional System to Dynamically Predict Graft Survival After Kidney Transplantation

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.

Study Overview

• Status: Completed
• Conditions: Kidney Transplant Failure
• Intervention / Treatment: Other: No intervention

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 Type: Observational
• Enrollment (Anticipated): 14000

Contacts and Locations

Study Locations

• Argentina
  • Buenos Aires, Argentina, 4102
    • Unidad de Trasplante Renopáncreas, Centro de Educación Médica e Investigaciones Clínicas
• Brazil
  • São Paulo, Brazil, 04038-002
    • Universidade Federal de São Paulo, Hospital do Rim, Escola Paulista de Medicina
  • São Paulo, Brazil, 05403-000
    • Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Renal Transplantation Service
• Chile
  • Santiago, Chile, 3204
    • Clínica Alemana de Santiago
• Croatia
  • Zagreb, Croatia, 10000
    • Department of Nephrology, Arterial Hypertension, Dialysis and Transplantation, University Hospital Centre Zagreb, School od Medicine University of Zagreb
• France
  • Montpellier, France, 34295
    • Department of Nephrology, Centre Hospitalier Universitaire de Montpellier
  • Nancy, France, 54035
    • Nephrology Dialysis Transplantation Department, University of Lorraine, Centre Hospitalier Universitaire de Nancy
  • Paris, France, 75010
    • Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris
  • Paris, France, 75015
    • Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris
  • Suresnes, France, 92150
    • Department of Transplantation, Nephrology and Clinical Immunology, Hôpital Foch
  • Toulouse, France, 31059
    • Department of Nephrology and Organ Transplantation, Centre Hospitalier Universitaire Rangueil
  • Tours, France, 37000
    • Bretonneau Hospital, Nephrology and Immunology Department
• Spain
  • Barcelona, Spain, 08003
    • Department of Nephrology, Hospital del Mar
• United States
  • California
    • Los Angeles, California, United States, 90048
      • Department of Medicine, Division of Nephrology, Comprehensive Transplant Center, Cedars Sinai Medical Center
  • Illinois
    • Chicago, Illinois, United States, 60611
      • Division of Transplantation, Department of Surgery, Feinberg School of Medicine, Northwestern University
  • Maryland
    • Baltimore, Maryland, United States, 21205
      • Department of Surgery, Johns Hopkins University School of Medicine
  • Minnesota
    • Rochester, Minnesota, United States, 55905
      • William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic
  • New York
    • New York, New York, United States, 10467
      • Albert Einstein College of Medicine, Renal Division Montefiore Medical Center, Kidney Transplantation Program

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• 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.

Description

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 Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Allograft survival probability	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.	Up to 10 years after kidney transplantation

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Added prognostic value	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	Up to 10 years after kidney transplantation

Collaborators and Investigators

• Sponsor: Paris Translational Research Center for Organ Transplantation
• Investigators: Principal Investigator: Alexandre Loupy, Professor, Paris Translational Research Center for Organ Transplantation

Publications and helpful links

General Publications

• Raynaud M, Aubert O, Divard G, Reese PP, Kamar N, Yoo D, Chin CS, Bailly É, Buchler M, Ladrière 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, Böhmig 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.

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2004
• Primary Completion (Actual): December 31, 2019
• Study Completion (Actual): June 30, 2020

Study Registration Dates

• First Submitted: February 4, 2020
• First Submitted That Met QC Criteria: February 4, 2020
• First Posted (Actual): February 6, 2020

Study Record Updates

• Last Update Posted (Actual): September 16, 2020
• Last Update Submitted That Met QC Criteria: September 14, 2020
• Last Verified: September 1, 2020

More Information

Terms related to this study

• Keywords: Kidney Transplantation; Prediction; Graft survival; Kidney function; Dynamic
• Other Study ID Numbers: DYNAKT

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No