Kidney TransplantAITXNCT07600541
Perceptions of Kidney Transplant Recipients Regarding the Role of Artificial Intelligence in Medicine
An observational study of Kidney Transplant, run by Paris Translational Research Center for Organ Transplantation at 2 sites in France.
Trial summary
The AITX study is an international, multicenter survey exploring how kidney transplant recipients perceive artificial intelligence (AI) in medicine and, specifically, a system that predicts graft loss risk. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Kidney Transplant
- How many people
- 1,000 participants
- Where
- 2 sitesFrance
- Study dates
- 15 Apr 2026to 30 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: The study targets adult kidney transplant recipients (≥18 years), fluent in French or English, and able to provide electronic consent. Patients with severe cognitive impairment preventing comprehension or technical inability to access the online questionnaire are excluded. Participants are recruited through transplant centers and patient associations in France and the United States. The questionnaire will be distributed to an estimated 10,000-20,000 patients, with an expected response rate of 10-15%
Who may be able to join
- Age ≥ 18 years
- Fluency in French or English
- Electronic consent given
Who cannot join
- Severe cognitive impairment preventing comprehension
- Technical inability to access the questionnaire
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
France2 sites, 2 recruiting
- Recruiting
Le flambeau de la vie
Paris, Île-de-France Region, 75015
Michel Baujard, MSc, 06 71 19 86 27, leflambeaudelavie@gmail.com
- Recruiting
Nice Pasteur
Paris, Île-de-France Region, 75015
Nathalie Cerruti, MD, +33 4 92 03 77 77, Cerruti.n@chu-nice.fr
About this study
The AITX study is an international, multicenter survey exploring how kidney transplant recipients perceive artificial intelligence (AI) in medicine and, specifically, a system that predicts graft loss risk. Through an open-ended online questionnaire distributed across transplant centers and patient associations in France and the United States, the study captures patients' expectations, concerns, and the perceived impact of AI-driven prediction on their daily lives. Responses are analyzed using large language models (LLMs) with systematic human verification. The study aims to ensure that the deployment of AI in transplantation is ethical, transparent, and patient-centered.
Read the detailed description
# Study Summary
## Background
Kidney transplantation is currently the gold standard treatment for end-stage renal disease, with over 170,000 transplants performed each year worldwide. Despite major advances in short-term survival, long-term graft survival remains limited: approximately 40% of patients return to dialysis within 10 to 15 years following transplantation.
In recent years, artificial intelligence (AI) has emerged as a promising tool for predicting graft outcomes and supporting clinical decision-making. The iBox system, developed by the Paris Institute for Transplantation and Organ Regeneration, calculates a personalized probability of graft loss up to ten years after evaluation of the kidney transplant recipient.
This algorithm has been validated internationally across multiple cohorts and clinical trials, outperforms clinicians in predicting risk, and was qualified by the European Medicines Agency as an endpoint for clinical trials in 2022.
However, while the clinical value of the iBox and similar predictive tools is well documented, patient perceptions remain largely unexplored. Understanding how kidney transplant recipients perceive the prediction of their individual risk of graft loss (in terms of usefulness, acceptability, fears, or impact on their daily life), as well as their broader views on the role of artificial intelligence in medicine - including their hopes, expectations, and concerns about its deployment - is essential to ensure an ethical, transparent, and truly patient-centered implementation.
## Study period
January 2026: Distribution of the questionnaire to the collaborating transplant centers and patient associations. February 2026: Data collection and management. April 2026: Analysis of results. June 2026: Presentation and discussion of results with the various participating centers/associations. September 2026: Publication.
## Sample size
The questionnaire will be distributed to a sample of between 10,000 and 20,000 kidney transplant recipients. Based on response rates observed in comparable studies using patient questionnaires, an estimated response rate of 10% to 15% is expected, which would yield a sufficient volume of responses to ensure statistical robustness and diversity of represented profiles.
## Study type
Cross-sectional, international, and multicenter survey, conducted via an anonymized online questionnaire. The study adopts a mixed-methods approach, combining qualitative and quantitative analysis of responses. No medical or biological data will be used, nor will data collected during clinical care be reused.
- Questionnaire development: The questionnaire was developed based on a review of the existing literature on patient perceptions of artificial intelligence and predictive medicine. An initial version of the questionnaire was developed and tested with four French patients. After adjustments, a bilingual translation was carried out by French-speaking and English-speaking collaborators. The final questionnaire was then shared with American patients for linguistic and cultural validation (questionnaire presented in the appendix of this document**).
- Questionnaire distribution:** Distribution will be carried out through partner patient associations and collaborating transplant centers in France and the United States. The questionnaire will be distributed via a secure and anonymized REDCap platform, hosted on institutional servers compliant with data protection standards (GDPR). Participants will receive a unique link allowing them to access the online questionnaire directly. Participants will be able to respond at their own pace and discontinue their participation at any time without justification or consequence.
- Data analysis:** The collected responses will be analyzed using a quantitative approach. Qualitative analyses will rely on thematic analysis assisted by large language models (LLMs). These models will automatically extract recurring themes, associated emotions, and nuances of perception in the free-text responses, using a standardized methodology developed by the investigators. This type of LLM-based thematic analysis was already successfully conducted in the investigators' group. To ensure reliability and scientific rigor, a systematic manual verification will be performed on a representative sub-sample of responses. This independent review will compare human and automated coding and refine the thematic categories.
- Data collected
- Age
- Sex
- Year of last transplant
- Country of last transplant
- Transplant follow-up center
- Transplant rank
- Current occupation or last occupation held
- Perceptions of the role of AI in medicine
- Perceptions regarding a graft loss risk prediction system
- Data flow
Data will be collected via the REDCap platform (HDS-certified hosting, GDPR-compliant). The questionnaire is anonymous: no nominative or identifiable medical data will be collected. Each participant will receive a unique link generating an untraceable alphanumeric identifier. Data will be transferred from REDCap and stored in encrypted form on institutional servers. Access will be strictly limited to the project investigators (Marc Raynaud, Alexandre Loupy) and the data manager (Thibaut Thalamas) via secure authentication. Data analyses will be performed on an internal environment using R. Fully anonymized datasets may be archived for secondary research or scientific replicability purposes.
## Participant information
Participants will be contacted by email via their follow-up center, using contact databases already existing at these centers. The invitation message will briefly present the study and its sponsor, the Paris Institute for Transplantation and Organ Regeneration, as well as the responsible researchers. It will also contain an information notice outlining the study objectives, confidentiality procedures, and a direct link to the online questionnaire hosted on REDCap. Participation will be entirely voluntary, with no impact on medical follow-up or the relationship with the transplant team.
- Conditions
- Kidney Transplant
- Keywords
- artificial intelligence, survey
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 |
|---|---|---|
| Vita move | Association of transplant recipients | |
| France rein pays de la loire | Association of patients with kidney disease | |
| France rein île de france | Association of patients with kidney disease | |
| France rein nord pas de calais | Association of patients with kidney disease | |
| Saint-Louis hospital | Hospital | |
| Nice Pasteur | Hospital | |
| Marseille hospital | hospital | |
| Lille | hospital | |
| The voice of the patient | Association of patients with kidney disease | |
| Utah university | hospital | |
| Mayo Clinic jacksonville | hospital |
What is being measured
The primary outcome is the main question the study is designed to answer.
Perceptions of patients about AI
Perceptions of kidney transplant recipients regarding the use of artificial intelligence, assessed with a questionnaire
Measured: Baseline (corresponding to questionnaire administration)
Secondary outcomes (1)
Perceptions of patients about the use of a graft failure prediction system
This study aims to explore the perceptions of kidney transplant recipients regarding the use of a graft loss risk prediction system, assessed with a questionnaire
Measured: Baseline (corresponding to questionnaire administration)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 May 2026
- Study start15 Apr 2026
- Primary completion30 Sep 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Kidney TransplantStudies of the same condition, open and under way.
- 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
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Paris Translational Research Center for Organ Transplantation
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Marc Raynaud, Paris Translational Research Center for Organ Transplantation
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07600541
- Sponsor's protocol ID
- AI_patient
- Acronym
- AITX
- Status last verified
- May 2026
- First submitted
- 28 Apr 2026
- Last updated
- 20 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: This is part of the investigators' protocol: as these data will reflect the views of the patients, the investigators prefer to keep them confidential.
Publications
Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health. 2021 Sep;3(9):e599-e611. doi: 10.1016/S2589-7500(21)00132-1.
PubMed 34446266https://osf.io/preprints/psyarxiv/pnx9e_v1
Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit Health. 2022 Aug 8;8:20552076221116772. doi: 10.1177/20552076221116772. eCollection 2022 Jan-Dec.
PubMed 35983102Erul E, Aktekin Y, Danisman FB, Gumustas SA, Aktekin BS, Yekeduz E, Urun Y. Perceptions, Attitudes, and Concerns on Artificial Intelligence Applications in Patients with Cancer. Cancer Control. 2025 Jan-Dec;32:10732748251343245. doi: 10.1177/10732748251343245. Epub 2025 May 23.
PubMed 40407404Divard G, Raynaud M, Tatapudi VS, Abdalla B, Bailly E, Assayag M, Binois Y, Cohen R, Zhang H, Ulloa C, Linhares K, Tedesco HS, Legendre C, Jouven X, Montgomery RA, Lefaucheur C, Aubert O, Loupy A. Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure. Commun Med (Lond). 2022 Nov 23;2(1):150. doi: 10.1038/s43856-022-00201-9.
PubMed 36418380Truchot A, Raynaud M, Helantera I, Aubert O, Kamar N, Divard G, Astor B, Legendre C, Hertig A, Buchler M, Crespo M, Akalin E, Pujol GS, Ribeiro de Castro MC, Matas AJ, Ulloa C, Jordan SC, Huang E, Juric I, Basic-Jukic N, Coemans M, Naesens M, Friedewald JJ, Silva HT Jr, Lefaucheur C, Segev DL, Collins GS, Loupy A. Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure. J Am Soc Nephrol. 2025 Apr 1;36(4):688-701. doi: 10.1681/ASN.0000000517. Epub 2024 Oct 16.
PubMed 40168162Lombardi Y, Raynaud M, Schatzl M, Mayer KA, Diebold M, Patel UD, Schrezenmeier E, Akifova A, Budde K, Loupy A, Bohmig GA. Estimating the efficacy of felzartamab to treat antibody-mediated rejection using the iBox prognostication system. Am J Transplant. 2025 May;25(5):1130-1132. doi: 10.1016/j.ajt.2024.12.004. Epub 2024 Dec 12. No abstract available.
PubMed 39674514Loupy A, Preka E, Chen X, Wang H, He J, Zhang K. Reshaping transplantation with AI, emerging technologies and xenotransplantation. Nat Med. 2025 Jul;31(7):2161-2173. doi: 10.1038/s41591-025-03801-9. Epub 2025 Jul 14.
PubMed 40659768Raynaud M, Aubert O, Divard G, Reese PP, Kamar N, Yoo D, Chin CS, Bailly E, Buchler M, Ladriere M, Le Quintrec M, Delahousse M, Juric I, Basic-Jukic N, Crespo M, Silva HT Jr, Linhares K, Ribeiro de Castro MC, Soler Pujol G, Empana JP, Ulloa C, Akalin E, Bohmig G, Huang E, Stegall MD, Bentall AJ, Montgomery RA, Jordan SC, Oberbauer R, Segev DL, Friedewald JJ, Jouven X, Legendre C, Lefaucheur C, Loupy A. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021 Dec;3(12):e795-e805. doi: 10.1016/S2589-7500(21)00209-0. Epub 2021 Oct 28.
PubMed 34756569Hart A, Gustafson SK, Wey A, Salkowski N, Snyder JJ, Kasiske BL, Israni AK. The association between loss of Medicare, immunosuppressive medication use, and kidney transplant outcomes. Am J Transplant. 2019 Jul;19(7):1964-1971. doi: 10.1111/ajt.15293. Epub 2019 Mar 5.
PubMed 30838768
Study at a glance
From the registry record, last updated by the sponsor on 20 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Perceptions of Kidney Transplant Recipients Regarding the Role of Artificial Intelligence in Medicine
- ClinicalTrials.gov ID
- NCT07600541
- Acronym / sponsor ID
- AITX / AI_patient
- Conditions
- Kidney Transplant
- Keywords
- artificial intelligence, survey
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Paris Translational Research Center for Organ TransplantationUniversity, hospital or other
- Study start
- 15 Apr 2026 (actual)
- Primary completion
- 30 Sep 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 20 May 2026 (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.
The AITX study is an international, multicenter survey exploring how kidney transplant recipients perceive artificial intelligence (AI) in medicine and, specifically, a system that predicts graft loss risk. Through an open-ended online questionnaire distributed across transplant centers and patient associations in France and the United States, the study captures patients' expectations, concerns, and the perceived impact of AI-driven prediction on their daily lives. Responses are analyzed using large language models (LLMs) with systematic human verification. The study aims to ensure that the deployment of AI in transplantation is ethical, transparent, and patient-centered.
Detailed description
# Study Summary
## Background
Kidney transplantation is currently the gold standard treatment for end-stage renal disease, with over 170,000 transplants performed each year worldwide. Despite major advances in short-term survival, long-term graft survival remains limited: approximately 40% of patients return to dialysis within 10 to 15 years following transplantation.
In recent years, artificial intelligence (AI) has emerged as a promising tool for predicting graft outcomes and supporting clinical decision-making. The iBox system, developed by the Paris Institute for Transplantation and Organ Regeneration, calculates a personalized probability of graft loss up to ten years after evaluation of the kidney transplant recipient.
This algorithm has been validated internationally across multiple cohorts and clinical trials, outperforms clinicians in predicting risk, and was qualified by the European Medicines Agency as an endpoint for clinical trials in 2022.
However, while the clinical value of the iBox and similar predictive tools is well documented, patient perceptions remain largely unexplored. Understanding how kidney transplant recipients perceive the prediction of their individual risk of graft loss (in terms of usefulness, acceptability, fears, or impact on their daily life), as well as their broader views on the role of artificial intelligence in medicine - including their hopes, expectations, and concerns about its deployment - is essential to ensure an ethical, transparent, and truly patient-centered implementation.
## Study period
January 2026: Distribution of the questionnaire to the collaborating transplant centers and patient associations. February 2026: Data collection and management. April 2026: Analysis of results. June 2026: Presentation and discussion of results with the various participating centers/associations. September 2026: Publication.
## Sample size
The questionnaire will be distributed to a sample of between 10,000 and 20,000 kidney transplant recipients. Based on response rates observed in comparable studies using patient questionnaires, an estimated response rate of 10% to 15% is expected, which would yield a sufficient volume of responses to ensure statistical robustness and diversity of represented profiles.
## Study type
Cross-sectional, international, and multicenter survey, conducted via an anonymized online questionnaire. The study adopts a mixed-methods approach, combining qualitative and quantitative analysis of responses. No medical or biological data will be used, nor will data collected during clinical care be reused.
- Questionnaire development: The questionnaire was developed based on a review of the existing literature on patient perceptions of artificial intelligence and predictive medicine. An initial version of the questionnaire was developed and tested with four French patients. After adjustments, a bilingual translation was carried out by French-speaking and English-speaking collaborators. The final questionnaire was then shared with American patients for linguistic and cultural validation (questionnaire presented in the appendix of this document**).
- Questionnaire distribution:** Distribution will be carried out through partner patient associations and collaborating transplant centers in France and the United States. The questionnaire will be distributed via a secure and anonymized REDCap platform, hosted on institutional servers compliant with data protection standards (GDPR). Participants will receive a unique link allowing them to access the online questionnaire directly. Participants will be able to respond at their own pace and discontinue their participation at any time without justification or consequence.
- Data analysis:** The collected responses will be analyzed using a quantitative approach. Qualitative analyses will rely on thematic analysis assisted by large language models (LLMs). These models will automatically extract recurring themes, associated emotions, and nuances of perception in the free-text responses, using a standardized methodology developed by the investigators. This type of LLM-based thematic analysis was already successfully conducted in the investigators' group. To ensure reliability and scientific rigor, a systematic manual verification will be performed on a representative sub-sample of responses. This independent review will compare human and automated coding and refine the thematic categories.
- Data collected
- Age
- Sex
- Year of last transplant
- Country of last transplant
- Transplant follow-up center
- Transplant rank
- Current occupation or last occupation held
- Perceptions of the role of AI in medicine
- Perceptions regarding a graft loss risk prediction system
- Data flow
Data will be collected via the REDCap platform (HDS-certified hosting, GDPR-compliant). The questionnaire is anonymous: no nominative or identifiable medical data will be collected. Each participant will receive a unique link generating an untraceable alphanumeric identifier. Data will be transferred from REDCap and stored in encrypted form on institutional servers. Access will be strictly limited to the project investigators (Marc Raynaud, Alexandre Loupy) and the data manager (Thibaut Thalamas) via secure authentication. Data analyses will be performed on an internal environment using R. Fully anonymized datasets may be archived for secondary research or scientific replicability purposes.
## Participant information
Participants will be contacted by email via their follow-up center, using contact databases already existing at these centers. The invitation message will briefly present the study and its sponsor, the Paris Institute for Transplantation and Organ Regeneration, as well as the responsible researchers. It will also contain an information notice outlining the study objectives, confidentiality procedures, and a direct link to the online questionnaire hosted on REDCap. Participation will be entirely voluntary, with no impact on medical follow-up or the relationship with the transplant team.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 11
| Arm | Type | Receives |
|---|---|---|
| Vita move | – | –Association of transplant recipients |
| France rein pays de la loire | – | –Association of patients with kidney disease |
| France rein île de france | – | –Association of patients with kidney disease |
| France rein nord pas de calais | – | –Association of patients with kidney disease |
| Saint-Louis hospital | – | –Hospital |
| Nice Pasteur | – | –Hospital |
| Marseille hospital | – | –hospital |
| Lille | – | –hospital |
| The voice of the patient | – | –Association of patients with kidney disease |
| Utah university | – | –hospital |
| Mayo Clinic jacksonville | – | –hospital |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study targets adult kidney transplant recipients (≥18 years), fluent in French or English, and able to provide electronic consent. Patients with severe cognitive impairment preventing comprehension or technical inability to access the online questionnaire are excluded. Participants are recruited through transplant centers and patient associations in France and the United States. The questionnaire will be distributed to an estimated 10,000-20,000 patients, with an expected response rate of 10-15%
Key inclusion criteria (3 listed)
Key exclusion criteria (2 listed)
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
Perceptions of patients about AI
Time frame: Baseline (corresponding to questionnaire administration)
Perceptions of kidney transplant recipients regarding the use of artificial intelligence, assessed with a questionnaire
1 secondary outcome
Perceptions of patients about the use of a graft failure prediction system
Time frame: Baseline (corresponding to questionnaire administration)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 May 2026.
- Background: PubMed 34446266Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health. 2021 Sep;3(9):e599-e611. doi: 10.1016/S2589-7500(21)00132-1.
- Results publicationhttps://osf.io/preprints/psyarxiv/pnx9e_v1
- Results publication: PubMed 35983102Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit Health. 2022 Aug 8;8:20552076221116772. doi: 10.1177/20552076221116772. eCollection 2022 Jan-Dec.
- Results publication: PubMed 40407404Erul E, Aktekin Y, Danisman FB, Gumustas SA, Aktekin BS, Yekeduz E, Urun Y. Perceptions, Attitudes, and Concerns on Artificial Intelligence Applications in Patients with Cancer. Cancer Control. 2025 Jan-Dec;32:10732748251343245. doi: 10.1177/10732748251343245. Epub 2025 May 23.
- Results publication: PubMed 36418380Divard G, Raynaud M, Tatapudi VS, Abdalla B, Bailly E, Assayag M, Binois Y, Cohen R, Zhang H, Ulloa C, Linhares K, Tedesco HS, Legendre C, Jouven X, Montgomery RA, Lefaucheur C, Aubert O, Loupy A. Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure. Commun Med (Lond). 2022 Nov 23;2(1):150. doi: 10.1038/s43856-022-00201-9.
- Results publication: PubMed 40168162Truchot A, Raynaud M, Helantera I, Aubert O, Kamar N, Divard G, Astor B, Legendre C, Hertig A, Buchler M, Crespo M, Akalin E, Pujol GS, Ribeiro de Castro MC, Matas AJ, Ulloa C, Jordan SC, Huang E, Juric I, Basic-Jukic N, Coemans M, Naesens M, Friedewald JJ, Silva HT Jr, Lefaucheur C, Segev DL, Collins GS, Loupy A. Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure. J Am Soc Nephrol. 2025 Apr 1;36(4):688-701. doi: 10.1681/ASN.0000000517. Epub 2024 Oct 16.
- Results publication: PubMed 39674514Lombardi Y, Raynaud M, Schatzl M, Mayer KA, Diebold M, Patel UD, Schrezenmeier E, Akifova A, Budde K, Loupy A, Bohmig GA. Estimating the efficacy of felzartamab to treat antibody-mediated rejection using the iBox prognostication system. Am J Transplant. 2025 May;25(5):1130-1132. doi: 10.1016/j.ajt.2024.12.004. Epub 2024 Dec 12. No abstract available.
- Results publication: PubMed 40659768Loupy A, Preka E, Chen X, Wang H, He J, Zhang K. Reshaping transplantation with AI, emerging technologies and xenotransplantation. Nat Med. 2025 Jul;31(7):2161-2173. doi: 10.1038/s41591-025-03801-9. Epub 2025 Jul 14.
- Results publication: PubMed 34756569Raynaud M, Aubert O, Divard G, Reese PP, Kamar N, Yoo D, Chin CS, Bailly E, Buchler M, Ladriere M, Le Quintrec M, Delahousse M, Juric I, Basic-Jukic N, Crespo M, Silva HT Jr, Linhares K, Ribeiro de Castro MC, Soler Pujol G, Empana JP, Ulloa C, Akalin E, Bohmig G, Huang E, Stegall MD, Bentall AJ, Montgomery RA, Jordan SC, Oberbauer R, Segev DL, Friedewald JJ, Jouven X, Legendre C, Lefaucheur C, Loupy A. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021 Dec;3(12):e795-e805. doi: 10.1016/S2589-7500(21)00209-0. Epub 2021 Oct 28.
- Results publication: PubMed 30838768Hart A, Gustafson SK, Wey A, Salkowski N, Snyder JJ, Kasiske BL, Israni AK. The association between loss of Medicare, immunosuppressive medication use, and kidney transplant outcomes. Am J Transplant. 2019 Jul;19(7):1964-1971. doi: 10.1111/ajt.15293. Epub 2019 Mar 5.
- Individual participant dataSharing planned: No. This is part of the investigators' protocol: as these data will reflect the views of the patients, the investigators prefer to keep them confidential.
Related trials for Kidney Transplant
Enrolling by invitation
Online Exercise Program for Transplant Patients: A Pilot Study
Kidney Transplantation, Physical Function
RecruitingPhase 2
A Study of Mezagitamab in Adults With Late Antibody-Mediated Rejection (AMR) After a Kidney Transplant
Antibody-Mediated Rejection (AMR)
Not yet recruiting
Hypothermic Kidney Machine Perfusion With Omnisol
End Stage Renal Disease (ESRD), Kidney Transplant
Not yet recruitingEarly Phase 1
Biomarker-Guided Mycophenolate Mofetil Elimination in Low-Risk Older Adult Kidney Transplant Recipients
Kidney Transplant Recipient
Recruiting
Non-Invasive Monitoring of Pediatric Kidney Transplant Recipients and Immunosuppression Personalization: an Open-labeled Multicenter Randomized Controlled Study
Pediatric Kidney Disease, Transplant;Failure,Kidney
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.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Kidney Transplant, run by Paris Translational Research Center for Organ Transplantation at 2 sites 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 2 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: looking for participants now, as of the sponsor's last update on 20 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Perceptions of patients about AI (time frame: Baseline (corresponding to questionnaire administration)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07600541, which also lists 10 linked publications.
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 20 May 2026.