Summary

Eligibility
for people ages 18-100 (full criteria)
Location
at San Francisco, California and other locations
Dates
study started
completion around

Description

Summary

Accurately predicting kidney recipient risk of death has a crucial interest because of the organ shortage, the need to optimize allograft allocation by identifying high-risk patients who may not benefit from a transplant and improve the clinical decision-making after transplant to ensure that each patient survives as long as possible.

However, according to a literature review the investigators performed, studies attempting to develop a kidney recipient death prediction model suffer from many shortcomings, including the lack of key risk factors, use of biased registry data, small sample size, lack of external validation in different countries and subpopulations, and short follow-up.

The present study thus aimed to address these limitations and develop a robust, generalizable kidney recipient death prediction model.

Official Title

Development and Validation of a Prediction Model for Risk of Death in Kidney Transplant Recipients

Details

The number of individuals suffering from end-stage chronic renal disease (ESRD) worldwide has increased over time, exceeding seven million of patients in 2020. For individuals with ESRD, kidney transplantation is the best treatment in terms of patient survival, quality of life and from a cost-effective standpoint, as compared with dialysis, even in comorbid or elderly populations.

Although the number of kidney transplantations performed each year has increased as well, it follows a lower pace than the increase of individuals on the waiting-list, resulting in an organ shortage. There is therefore a need to optimize allograft allocation by identifying the high-risk patients who may not benefit from a transplant and improve the clinical decision-making after transplant to ensure that each patient survives as long as possible.

In this context, a kidney recipient death prediction model may improve transplant clinical practice, allowing for the ability to evaluate the individual risk of post transplant mortality, already before undergoing transplantation, thereby guiding decision making. However, developing such a model is a very difficult task, as death after kidney transplantation depends on many parameters, such as donor age, history or cause of death, imaging parameters, patients' past medical history (e.g. diabetes, dialysis duration, hypertension), patients' biological parameters, as well as the function of the allograft, which depends on patients' immunological factors, or allograft related parameters such as HLA mismatches or cold ischemia time.

The goal of the present study was therefore to identify the determinants of death after kidney transplantation, and to develop and validate a prediction model that would help optimize allograft allocation and post-transplant patient management, using a large, international, highly phenotyped cohort of kidney recipients with extensive data collection and long-term follow-up.

Keywords

Death, Necker hospital from Paris, France, Saint-Louis hospital from Paris, France, Bichat hospital from Paris, France, Bretonneau hospital from Tours, France, Toulouse hospital, France, KU Leuven, Belgium, Liege hospital from Belgium, Hospital of the University of Pennsylvania from Philadelphia, US, Mayo Clinic from Phoenix, US, UCSF database, AP-HP database

Eligibility

You can join if…

Open to people ages 18-100

  • Adult kidney recipients

You CAN'T join if...

  • Multi-organ transplantation
  • Prior kidney transplant

Locations

  • Bakar Computational Health Sciences Institute, University of California
    San Francisco California 94158 United States
  • Department of Medicine, Mayo Clinic
    Phoenix Arizona 85054 United States

Details

Status
accepting new patients by invitation only
Start Date
Completion Date
(estimated)
Sponsor
Paris Translational Research Center for Organ Transplantation
ID
NCT06531967
Study Type
Observational
Participants
Expecting 13000 study participants
Last Updated