Predicting Mortality in Kidney Transplant Recipients
- Conditions
- Death
- Interventions
- Other: No intervention
- Registration Number
- NCT06531967
- Lead Sponsor
- Paris Translational Research Center for Organ Transplantation
- Brief 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.
- Detailed Description
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.
Recruitment & Eligibility
- Status
- ENROLLING_BY_INVITATION
- Sex
- All
- Target Recruitment
- 13000
- Adult kidney recipients
- Multi-organ transplantation
- Prior kidney transplant
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Necker hospital from Paris, France No intervention Kidney recipients from Necker hospital Saint-Louis hospital from Paris, France No intervention Kidney recipients from Saint-Louis hospital Leiden University Medical Center from the Netherlands No intervention Kidney recipients from Leiden University Medical Center Toulouse hospital, France No intervention Kidney recipients from Toulouse hospital Hospital of the University of Pennsylvania from Philadelphia, US No intervention Kidney recipients from Hospital of the University of Pennsylvania Mayo Clinic from Phoenix, US No intervention Kidney recipients from Mayo Clinic Bichat hospital from Paris, France No intervention Kidney recipients from Bichat hospital Bretonneau hospital from Tours, France No intervention Kidney recipients from Bretonneau hospital Liege hospital from Belgium No intervention Kidney recipients from Liege hospital KU Leuven, Belgium No intervention Kidney recipients from KU Leuven UCSF database No intervention Kidney recipients data from real-world UCSF database AP-HP database No intervention Kidney recipients data from real-world AP-HP database
- Primary Outcome Measures
Name Time Method Patient death Up to 10 years after kidney transplantation Patient death
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (11)
Department of Nephrology and Renal Transplantation, University Hospitals Leuven
🇧🇪Leuven, Belgium
Division of Nephrology, University Hospital Liège (CHU)
🇧🇪Liège, Belgium
Necker hospital
🇫🇷Paris, France
Tenon hospital
🇫🇷Paris, France
Leiden Transplant Center, Leiden University Medical Center
🇳🇱Leiden, Netherlands
Penn Transplant Institute, Hospital of the University of Pennsylvania
🇺🇸Philadelphia, Pennsylvania, United States
Department of Medicine, Mayo Clinic
🇺🇸Phoenix, Arizona, United States
Bakar Computational Health Sciences Institute, University of California
🇺🇸San Francisco, California, United States
Saint-Louis hospital
🇫🇷Paris, France
Department of Nephrology and Organ Transplantation, Toulouse University Hospital
🇫🇷Toulouse, France
Department of Nephrology and Clinical Immunology, University Hospital of Tours
🇫🇷Tours, France