AI for Allograft Diseases Diagnosis and Prognosis After Kidney Transplantation

NCT05112770 | Withdrawn

• 1 other identifier: APHP210907
• Study Type: observational
• Target: N/A
• Locations: 1 country
• Sites: 1

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.

Conditions

Kidney Transplantation

Keywords

Kidney transplantation; Acute renal transplant rejection; Urinary concentrations of chemokines; Multiparametric models; Artificial intelligence

Outcome Measures

Primary Outcomes (2)

• Diagnostic model accuracy

  ROC (receiver operating characteristic) curves AUC (Area under the Curve)

  3 years

• Prognostic model accuracy

  C-statistics

  3 years

Secondary Outcomes (1)

• Strenght of the models

  3 years

Study Arms (1)

Patients

Renal transplant patients whose medical follow-up is provided from 2004 to 2020 by the nephrology and adult renal transplantation department of the Necker hospital.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Assistance Publique - Hôpitaux de Paris: lead
• URC-CIC Paris Descartes Necker Cochin: collaborator

Study Sites (1)

Hôpital Necker-Enfants Malades

Paris, 75015, France

Location

Study Officials

• Dani Anglicheau, MD, PhD

  Assistance Publique - Hôpitaux de Paris

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 28, 2021
• First Posted: November 9, 2021
• Study Start: January 4, 2022
• Primary Completion: January 4, 2022
• Study Completion (Estimated): August 1, 2027
• Last Updated: September 25, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)