Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT05112770 |
| Other study ID # |
APHP210907 |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
August 2024 |
| Est. completion date |
August 2027 |
Study information
| Verified date |
April 2024 |
| Source |
Assistance Publique - Hôpitaux de Paris |
| Contact |
Dani Anglicheau, MD, PhD |
| Phone |
1 44 49 54 41 |
| Email |
dany.anglicheau[@]aphp.fr |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
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.
Description:
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.
For this, all the clinical parameters (demographic, medical history, characteristics of
donors, immunosuppressive treatments, etc.) and biological (follow up of the usual biological
parameters obtained as part of the routine care of transplant patients, urinary chemokines)
of transplant patients followed in the nephrology and renal transplantation department of
Necker hospital between 2004 and 2020, will be treated without a priori and by artificial
intelligence methods.
