Stopped: 29 / 5 000 Abandoned research project
Kidney transplantation is the treatment of choice for patients with end stage renal disease. One of the major challenges is to better diagnose the attacks undergone by the kidney transplant in order to increase its longevity. Multiple attacks are caused by non-immune and immune mechanisms, first and foremost the acute rejection of the transplant. Biopsy, an invasive method, remains the "Gold Standard" for diagnosing rejection and other pathologies affecting the kidney transplant. The invasive nature of these biopsies limits their use and alternative biomarkers have been evaluated in order to diagnose kidney transplant pathologies in a non-invasive manner. It is in this context that the nephrology and renal transplantation department of the Necker hospital and Inserm U1151 have carried out several studies leading to the identification of the diagnostic and prognostic potential of acute rejection, by the determination of urinary concentrations of two chemokines, CXCL9 and CXCL10. The most recent study conducted within these teams demonstrated that the diagnostic potential of urinary chemokines could be improved by taking into account standard clinicobiological parameters in multiparametric models. The main objective of the study is to develop, train and validate artificial intelligence models including urinary chemokines, efficient, robust, explainable and interpretable for the diagnosis and non-invasive prognosis of acute renal transplant rejection, trained on a data set made up of clinical and biological parameters.
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Diagnostic model accuracy
Timeframe: 3 years
Prognostic model accuracy
Timeframe: 3 years