NCT04258891: DYNAKT

Multidimensional System to Dynamically Predict Graft Survival After Kidney Transplantation

Completed Last updated 16 September 2020

What this trial tests

trial testing No intervention in Kidney Transplant Failure in 14,000 participants. Completed in 30 June 2020.

Timeline

1 January 2004

Primary endpoint
31 December 2019

30 June 2020

Quick facts

Lead sponsor	Paris Translational Research Center for Organ Transplantation
Status	Completed
Study type	OBSERVATIONAL
Enrollment	14,000
Start date	1 January 2004
Primary completion	31 December 2019
Estimated completion	30 June 2020
Sites	18 locations across France, Chile, Argentina, Spain, Croatia, United States, Brazil

Drugs / interventions tested

• No intervention

Conditions studied

• Kidney Transplant Failure — all drugs for Kidney Transplant Failure →

Sponsor

Paris Translational Research Center for Organ Transplantation — full company profile →

Who can join

18 and older, any sex, with Kidney Transplant Failure. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

2 peer-reviewed publications reference this trial (live from Europe PMC):

1. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study.
   Raynaud M, Aubert O, Divard G, Reese PP, et al · · 2021 · cited 51× · PMID 34756569 · DOI 10.1016/s2589-7500(21)00209-0
2. Dynamic prediction of renal survival using artificial intelligence: an international study of deeply phenotyped cohorts of kidney transplant recipients
   Loupy A, Raynaud M, Aubert O, Reese PP, et al · · 2020 · DOI 10.21203/rs.3.rs-85478/v1