Kidney TransplantationAI4ADAPTNCT05112770
AI for Allograft Diseases Diagnosis and Prognosis After Kidney Transplantation
An observational study of Kidney Transplantation, run by Assistance Publique - Hôpitaux de Paris at 1 site in France.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Kidney Transplantation
- How many people
- 0 participants
- Where
- 1 siteFrance
- Study dates
- 4 Jan 2022to Aug 2027
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- A deceased patient who, during his lifetime, objected in writing to the processing of his data for research purposes.
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read more
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.
Read the 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.
- Conditions
- Kidney Transplantation
- Keywords
- Kidney transplantation, Acute renal transplant rejection, Urinary concentrations of chemokines, Multiparametric models, Artificial intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic model accuracy
ROC (receiver operating characteristic) curves AUC (Area under the Curve)
Measured: 3 years
Prognostic model accuracy
C-statistics
Measured: 3 years
Secondary outcomes (1)
Strenght of the models
Sensitivity analyses comparing the AUC values and the calibration of the models in various clinical scenarios.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Nov 2021
- Study start4 Jan 2022
- Primary completion4 Jan 2022
- Study completionAug 2027 (estimated)
Why it stopped: 29 / 5 000 Abandoned research project
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Kidney TransplantationStudies of the same condition, open and under way.
- More trials run by Assistance Publique - Hôpitaux de ParisThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Sponsor type
- University, hospital or other
- Collaborators
- URC-CIC Paris Descartes Necker Cochin
- Responsible party
- Sponsor
- Investigators
- Dani Anglicheau, MD, PhD, Principal investigator, Assistance Publique - Hôpitaux de Paris
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05112770
- Sponsor's protocol ID
- APHP210907
- Acronym
- AI4ADAPT
- Status last verified
- Sep 2025
- First submitted
- 28 Oct 2021
- Last updated
- 25 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI for Allograft Diseases Diagnosis and Prognosis After Kidney Transplantation
- ClinicalTrials.gov ID
- NCT05112770
- Acronym / sponsor ID
- AI4ADAPT / APHP210907
- Conditions
- Kidney Transplantation
- Keywords
- Kidney transplantation, Acute renal transplant rejection, Urinary concentrations of chemokines, Multiparametric models, Artificial intelligence
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: 29 / 5 000 Abandoned research project
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Assistance Publique - Hôpitaux de ParisUniversity, hospital or other
- Collaborators
- URC-CIC Paris Descartes Necker Cochin
- Study start
- 4 Jan 2022 (actual)
- Primary completion
- 4 Jan 2022 (actual)
- Study completion
- Aug 2027 (estimated)
- First posted
- 9 Nov 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (1 listed)
- A deceased patient who, during his lifetime, objected in writing to the processing of his data for research purposes.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic model accuracy
Time frame: 3 years
ROC (receiver operating characteristic) curves AUC (Area under the Curve)
Prognostic model accuracy
Time frame: 3 years
C-statistics
1 secondary outcome
Strenght of the models
Time frame: 3 years
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Sep 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Dani Anglicheau, MD, PhD principal investigator
Assistance Publique - Hôpitaux de Paris
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Kidney Transplantation, run by Assistance Publique - Hôpitaux de Paris at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 25 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic model accuracy (time frame: 3 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Sep 2025.