Explainable Machine Learning for the Assessment of Donor Grafts in Liver Transplantation

NCT06535217 | Completed

• 1 other identifier: ZSSY-2403
• Study Type: observational
• Target: 5,636
• Locations: 1 country
• Sites: 1

Brief Summary

Clinically, organ evaluation generally performed by the senior surgeons based on their experience and the visual and tactual inspection of the graft during procurement. However, it is proved that transplant surgeons intuition in the evaluation of donor risk and the estimation of steatosis is inconsistent and usually inaccurate. Besides, graft assessment is a dynamic process refer to amount of complex factors, which is considered to be an incredibly complicated relationship that is nonlinear in nature. Unfortunately, the classical statistic techniques in vogue such as multiple regression require the statistical assumption of independent and linear relationships between explanatory and outcome variables, and fail to analyse a large number of variables. We attempted to develop liver graft assessment models by predicting postoperative DGF using several ML techniques. Secondly, the best prediction model was selected by comparing the performance of different AI algorithms and logistic regression. Finally, we sought to explain the decision made by AI algorithms using a visualization algorithm based on the best prediction model, helping clinicians evaluate specific organ and whether to receive that may develop DGF postoperatively.

Conditions

Liver Transplant; Complications

Outcome Measures

Primary Outcomes (1)

• Delayed Graft Function (DGF)

  defined as early graft dysfunction without the need for a second liver transplant or death

  Within 7 days after liver transplantation

Study Arms (2)

DGF

patients occured delayed graft function after liver transplantation

Procedure: Liver transplantation

non-DGF

patients NOT occured delayed graft function after liver transplantation

Procedure: Liver transplantation

Interventions

Liver transplantation PROCEDURE

Liver transplantation

DGF; non-DGF

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients who underwent deceased donor liver transplantation

You may qualify if:

• Age≥18 years-old
• Underwent deceased donor liver transplantation

You may not qualify if:

• Underwent living-donor LT;
• Missing rates of data were more than 80%

Sponsors & Collaborators

• Third Affiliated Hospital, Sun Yat-Sen University: lead
• the China Liver Transplant Registry: collaborator

Study Sites (1)

The Third Affiliated Hospital of Sun Yat-Sen University

Guangzhou, Guangdong, China

Location

MeSH Terms

Interventions

Liver Transplantation

Intervention Hierarchy (Ancestors)

Tissue Transplantation; Cell- and Tissue-Based Therapy; Biological Therapy; Therapeutics; Digestive System Surgical Procedures; Surgical Procedures, Operative; Organ Transplantation; Transplantation

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: M.D.

Study Record Dates

• First Submitted: July 28, 2024
• First Posted: August 2, 2024
• Study Start: January 1, 2017
• Primary Completion: June 30, 2023
• Study Completion: June 30, 2024
• Last Updated: August 2, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)