Multidimensional System to Dynamically Predict Graft Survival After Kidney Transplantation

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Kidney Transplant Failure

Intervention / Treatment
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* Other: No intervention


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Other: No intervention|Kidney recipients aged over 18 and of all sexes recruited from 2004 in European, North American and South American centers, who have estimated glomerular filtration rate and proteinuria follow-up and data from protocol and for cause biopsies for allograft survival assessment; Randomized controlled trials conducted over the past 20 years with available data on protocol biopsy within the first year and follow-up, clinical, biological and histological data.|

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Dynamic, Kidney Transplantation, Graft survival, Kidney function, Prediction