Predicting Mortality in Kidney Transplant Recipients (mBox)

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

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.

Study Overview

• Status: Enrolling by invitation
• Conditions: Death
• Intervention / Treatment: Other: No intervention

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.

• Study Type: Observational
• Enrollment (Estimated): 13000

Contacts and Locations

Study Locations

• Belgium
  • Leuven, Belgium
    • Department of Nephrology and Renal Transplantation, University Hospitals Leuven
  • Liège, Belgium
    • Division of Nephrology, University Hospital Liège (CHU)
• France
  • Paris, France
    • Saint-Louis Hospital
  • Paris, France
    • Tenon Hospital
  • Paris, France
    • Necker Hospital
  • Toulouse, France
    • Department of Nephrology and Organ Transplantation, Toulouse University Hospital
  • Tours, France
    • Department of Nephrology and Clinical Immunology, University Hospital of Tours
• Netherlands
  • Leiden, Netherlands
    • Leiden Transplant Center, Leiden University Medical Center
• United States
  • Arizona
    • Phoenix, Arizona, United States, 85054
      • Department of Medicine, Mayo Clinic
  • California
    • San Francisco, California, United States, 94158
      • Bakar Computational Health Sciences Institute, University of California
  • Pennsylvania
    • Philadelphia, Pennsylvania, United States, 19104
      • Penn Transplant Institute, Hospital of the University of Pennsylvania

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Adult, de novo, kidney recipients who received only a kidney transplant

Description

Inclusion Criteria:

• Adult kidney recipients

Exclusion Criteria:

• Multi-organ transplantation
• Prior kidney transplant

Study Plan

How is the study designed?

Number of groups / cohorts

12

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Necker hospital from Paris, France; Kidney recipients from Necker hospital	Other: No intervention; No intervention
Saint-Louis hospital from Paris, France; Kidney recipients from Saint-Louis hospital	Other: No intervention; No intervention
Bichat hospital from Paris, France; Kidney recipients from Bichat hospital	Other: No intervention; No intervention
Bretonneau hospital from Tours, France; Kidney recipients from Bretonneau hospital	Other: No intervention; No intervention
Toulouse hospital, France; Kidney recipients from Toulouse hospital	Other: No intervention; No intervention
KU Leuven, Belgium; Kidney recipients from KU Leuven	Other: No intervention; No intervention
Liege hospital from Belgium; Kidney recipients from Liege hospital	Other: No intervention; No intervention
Leiden University Medical Center from the Netherlands; Kidney recipients from Leiden University Medical Center	Other: No intervention; No intervention
Hospital of the University of Pennsylvania from Philadelphia, US; Kidney recipients from Hospital of the University of Pennsylvania	Other: No intervention; No intervention
Mayo Clinic from Phoenix, US; Kidney recipients from Mayo Clinic	Other: No intervention; No intervention
UCSF database; Kidney recipients data from real-world UCSF database	Other: No intervention; No intervention
AP-HP database; Kidney recipients data from real-world AP-HP database	Other: No intervention; No intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Patient death	Patient death	Up to 10 years after kidney transplantation

Collaborators and Investigators

• Sponsor: Paris Translational Research Center for Organ Transplantation
• Investigators: Principal Investigator: Alexandre Loupy, Paris Institute for Transplantation and Organ Regeneration (PITOR)

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2004
• Primary Completion (Actual): June 1, 2024
• Study Completion (Estimated): August 1, 2024

Study Registration Dates

• First Submitted: July 25, 2024
• First Submitted That Met QC Criteria: July 31, 2024
• First Posted (Actual): August 1, 2024

Study Record Updates

• Last Update Posted (Actual): August 1, 2024
• Last Update Submitted That Met QC Criteria: July 31, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Death
• Other Study ID Numbers: mBox_001

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No