Clinical trial NCT05112770: AI for Allograft Diseases Diagnosis and Prognosis After Kidney Transplantation
Official title: AI for Allograft Diseases Diagnosis and Prognosis After Kidney Transplantation
Study type: OBSERVATIONAL
Overall status: WITHDRAWN
Conditions: Kidney Transplantation
Keywords: Kidney transplantation
Acute renal transplant rejection
Urinary concentrations of chemokines
Multiparametric models
Artificial intelligence
Lead sponsor: Assistance Publique - Hôpitaux de Paris
Sponsor class: OTHER
First posted: 2021-11-09
Enrollment: 0
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All renal transplant patients whose medical follow-up is provided by the nephrology and adult renal transplantation department of the Necker hospital between 2004 and 12/31/2020.

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* All renal transplant patients whose medical follow-up is provided by the nephrology and adult renal transplantation department of the Necker hospital between 2004 and 12/31/2020;
* Patient having signed a consent form for the storage, use and transfer of samples taken during treatment, for scientific research purposes;
* Patient not objecting to the processing of his personal data as part of the study.

Exclusion Criteria:

- A deceased patient who, during his lifetime, objected in writing to the processing of his data for research purposes.

Primary outcomes:
measure: Diagnostic model accuracy
timeFrame: 3 years
measure: Prognostic model accuracy
timeFrame: 3 years
Secondary outcomes:
measure: Strenght of the models
timeFrame: 3 years
Other outcomes:
Locations:
city: Paris
country: France
facility: Hôpital Necker-Enfants Malades
Study officials:
affiliation: Assistance Publique - Hôpitaux de Paris
name: Dani Anglicheau, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: