Clinical trial NCT06557564: Diagnosis of Graft Pathology by TruGraf
Official title: Development of a Machine Learning Tool for Non-invasive Diagnosis of Liver Graft Pathology Using TruGraf
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Acute Graft Rejection
Keywords: Liver transplant patients
Lead sponsor: University Health Network, Toronto
Sponsor class: OTHER
Collaborators: name: Transplant Genomics, Inc.
First posted: 2024-08-16
Enrollment: 471
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Liver transplant patient with graft pathology

Brief summary: The goal of this observational study is to to identify different causes of liver diseases or damage in liver transplant patients and develop a machine learning algorithm as a non-invasive tool leveraging gene expression and patient clinical information to classify transplant liver diseases We will collect blood samples of the participants who had undergone or will undergo the liver biopsy as part of standard of care, and use this blood in TruGarf. TruGraf is a non-invasive test that measures differentially expressed genes in the blood of transplant recipients to rule out liver damage. Researcher will collect the biopsy result from the medical record and this will be compared with the TruGarf results.

Detailed description: Given the significant investment of healthcare resources into transplantation, it is critical to identify recipients with graft pathologies such as Acute Cellular Rejection (ACR), NASH, cholestasis, etc. at an earlier stage to implement the appropriate intervention, rather than initiating empiric treatment that could be unsafe. This project will develop a practical Machine learning-based tool based on the results of the TruGraf assay alongside clinical and laboratory data for non-invasive diagnosis of graft pathology. TruGraf is a non-invasive test that measures differentially expressed genes in the blood of transplant recipients to identify patients who are likely to be adequately immunosuppressed and, in doing so, rule out graft damage. TruGraf measures the difference in gene expression for a precise panel of specific genes that have been empirically determined to discriminate between allografts that are truly healthy (Non-ACR), and those in transplant patients that have acute rejection on biopsy (AR). Nevertheless, the exact etiology of graft damage may be difficult to discern for the transplant clinician. The clinical characteristics and history of the liver transplant recipient as well as liver enzyme patterns can provide a pre-test probability of one diagnosis being more likely than the other (Acute cellular rejection, NASH, biliary or viral disease). The proposed tool will leverage our expertise in Machine Learning tools applied to clinical and molecular data (TruGraf assay results) to enable effective clinical implementation of the TruGraf assay.

Eligibility criteria: Inclusion Criteria:

1. . Single-organ Liver transplant recipients
2. Male or female, age \> 18 years at the time of signing informed consent.
3. Willing and able to provide informed consent.
4. Patients will be undergoing liver graft biopsy (for any reason), or have had a liver biopsy within 48 hours of consent.

Exclusion Criteria:

1. Repeat transplant
2. Recipient of multi organ transplantation
3. Any treatment for graft rejection such as IV steroids has been given before biopsy.
4. Targeted biopsies for diagnosis of malignancy.

Primary outcomes:
measure: Develop and validate a ML-based algorithm
timeFrame: 36 months
Secondary outcomes:
measure: Identify specific etiologies of ongoing graft damage
timeFrame: 36 months
Other outcomes:
Locations:
city: Toronto
country: Canada
facility: Toronto General Hospital -UHN
state: Ontario
Study officials:
affiliation: UHN
name: Mamatha Bhat, MD
role: PRINCIPAL_INVESTIGATOR
References: