Ignite Creation Date:
2024-05-06 @ 3:47 PM
Last Modification Date:
2024-10-26 @ 1:57 PM
Study NCT ID:
NCT04759209
Status:
UNKNOWN
Last Update Posted:
2021-02-21
First Post:
2021-02-11
Brief Title:
Development and Validation of a Virtual Biopsy System in Kidney Transplant
Sponsor:
Paris Translational Research Center for Organ Transplantation
Organization:
Paris Translational Research Center for Organ Transplantation
Study Overview
Official Title:
Development and Validation of a Machine Learning Based Virtual Biopsy System in Kidney Transplant Patients
Status:
UNKNOWN
Status Verified Date:
2021-02
Last Known Status:
ACTIVE_NOT_RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None