Human Versus Computer-based Predictions of Long Allograft Survival

NCT ID: NCT04918199

Last Updated: 2023-03-28

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 400 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-03-01
• Study Completion Date: 2021-12-31

Brief Summary

The clinical decision-making after kidney transplantation is mainly driven by patient individual assessment. However, this task remains difficult and uncertain due to the integration of complex and numerous parameters. We aim to evaluate and compare the ability of transplant physicians to predict long term allograft survival compared with a computer-based survival prediction algorithm (iBox system).

Detailed Description

400 kidney transplant recipients among the cohort of 4,000 patients from the Paris Transplant Group prospective kidney transplant cohort (NCT03474003) were randomly selected. We generated an anonymized electronic health record for each included patient including a total of 60 classical kidney transplant prognostic parameters comprising baseline transplant and recipient characteristics, together with post-transplant parameters including allograft function, proteinuria, histology, diagnoses, and immunological profile collected during the first-year post-transplant. The time of risk evaluation for the human and the iBox system were at 1-year post transplant and the death censored allograft survival predictions made at 7 years after risk assessment. We enrolled transplant physicians at various stages of their careers (residents, fellows and seniors) to assign death censored graft survival probabilities at 7 years post risk assessment. The physicians were blinded to the actual patient outcome (allograft failure) and the iBox predictions. The physicians-based predictions will then be compared with the iBox system, a validated computer-based kidney survival prediction system.

Conditions

Kidney Transplant Failure

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Paris Transplant Group cohort

400 (10%) of the patients were randomly selected from 4,000 consecutive patients over 18 years of age prospectively enrolled at the time of kidney transplantation from a living or deceased donor at Necker Hospital, Saint-Louis Hospital, Foch Hospital, and Toulouse Hospital between January 1, 2005, and January 1, 2014, in France.

Computer based assessment (iBox)

Intervention Type: DEVICE

Individual allograft survival probabilities of death censored allograft survival seven years after the time of risk evaluation, computed using the iBox (NCT03474003), a qualified prognostication system designed to predict long term allograft survival up to seven years after evaluation.

Physician assessement

Intervention Type: OTHER

Based on anonymized electronic health records, physicians have to determine a percentage of death censored allograft survival seven years after the time of risk evaluation,

Interventions

Computer based assessment (iBox)

Individual allograft survival probabilities of death censored allograft survival seven years after the time of risk evaluation, computed using the iBox (NCT03474003), a qualified prognostication system designed to predict long term allograft survival up to seven years after evaluation.

Intervention Type: DEVICE

Physician assessement

Based on anonymized electronic health records, physicians have to determine a percentage of death censored allograft survival seven years after the time of risk evaluation,

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• transplant evaluation available at one year post-transplant

Exclusion Criteria

• no

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Paris Translational Research Center for Organ Transplantation

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Paris Translational Centre for Organ Transplantation

Paris, Île-de-France Region, France

Countries

France

References

Loupy A, Aubert O, Orandi BJ, Naesens M, Bouatou Y, Raynaud M, Divard G, Jackson AM, Viglietti D, Giral M, Kamar N, Thaunat O, Morelon E, Delahousse M, Kuypers D, Hertig A, Rondeau E, Bailly E, Eskandary F, Bohmig G, Gupta G, Glotz D, Legendre C, Montgomery RA, Stegall MD, Empana JP, Jouven X, Segev DL, Lefaucheur C. Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study. BMJ. 2019 Sep 17;366:l4923. doi: 10.1136/bmj.l4923.

Reference Type: BACKGROUND

PMID: 31530561 (View on PubMed)

Other Identifiers

iBoxvsHuman

Identifier Type: -

Identifier Source: org_study_id