Clinical trial NCT04759209: Development and Validation of a Virtual Biopsy System in Kidney Transplant
Official title: Development and Validation of a Machine Learning Based Virtual Biopsy System in Kidney Transplant Patients
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Kidney Transplantation
Keywords: Kidney transplantation
Day-zero biopsy
Machine learning
Non-invasive biopsy
Common parameters
Lead sponsor: Paris Translational Research Center for Organ Transplantation
Sponsor class: OTHER
Collaborators: name: Mayo Clinic
name: KU Leuven
name: University of Liege
name: University of British Columbia
name: Hospital Clinic of Barcelona
name: Assistance Publique - Hôpitaux de Paris
name: Hospital Vall d'Hebron
name: Hospital Universitari de Bellvitge
name: Clinical Hospital Centre Zagreb
name: University of Alberta
name: University of Adelaide
First posted: 2021-02-18
Enrollment: 12992
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: Living or deceased kidney donors who underwent a kidney biopsy performed prior to kidney transplantation.

Brief summary: Currently, kidney allograft biopsies are routinely performed to determine diagnosis and prognosis of kidney allografts. The histological interpretation of these biopsies is based on the Banff consensus for renal allograft pathology. The purpose of this study is to provide to the physicians a reliable estimation of renal allograft lesions of the day zero biopsy (kidney donor biopsy performed before transplantation).

Detailed description: In kidney transplantation, day-zero biopsies are essential to assess organ quality and discriminate the donor transmitted or acquired lesions and disease progression post-transplant. However, many centers worldwide do not perform those biopsies because they are invasive and costly. We aimed to develop and validate a non-invasive virtual biopsy system. Our goal was to provide clinicians with a virtual biopsy system to guide diagnostics, therapeutics and immediate patient management post-transplant and to minimize additional risks and costs to perform day-zero biopsies only using standard donor parameters. To circumvent these limitations, we sought to build and validate a virtual biopsy system that uses routinely collected donor parameters to predict kidney day-zero biopsy results. Since machine learning has demonstrated its clinical relevance in many medical specialties and superior performance to logistic regression, we based our analyses on machine learning methods as well as traditional statistical approaches using large and qualified international cohort donors who underwent routine and protocolized collection of donor parameters together with day-zero biopsy assessment using the standards of the international Banff allograft histopathology classification.

Eligibility criteria: Inclusion Criteria:

- Kidney donors who underwent kidney biopsies performed prior to kidney transplantation, aged at least 18 years old.

Exclusion Criteria:

* inadequate biopsies according to Banff international classification (number of glomeruli less than 10),
* biopsies with two or more Banff scores of interest missing.

Primary outcomes:
measure: Arteriosclerosis score according to Banff International Classification
timeFrame: up to 1 day post-transplant
measure: Arteriolar Hyalinosis score according to Banff International Classification
timeFrame: up to 1 day post-transplant
measure: Interstitial fibrosis and tubular atrophy score according to Banff International Classification
timeFrame: up to 1 day post-transplant
measure: Continuous percentage of sclerotic glomeruli
timeFrame: up to 1 day post-transplant
Secondary outcomes:
Other outcomes:
Locations:
city: Paris
country: France
facility: Paris Transplant Group
Study officials:
affiliation: Paris Translational Research Center for Organ Transplantation
name: Alexandre Loupy
role: PRINCIPAL_INVESTIGATOR
References: