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Acute Graft-Versus-Host Disease After Orthotopic Liver Transplantation: Predicting This Rare Complication Using

Jason P Cooper¹, James D Perkins, Paul R Warner

¹Division of HematologyDepartment of Medicine University of Washington Seattle WA Division of Transplant Surgery University of Washington Seattle WA Clinical and Bio-Analytics Transplant Laboratory in the Department of Surgery at the University of Washington School of Medicine Seattle WA Bloodworks Northwest Seattle WA Division of GastroenterologyDepartment of Medicine University of Washington Seattle WA Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, Vanderbilt University Medical Center Nashville TN.

Liver Transplantation : Official Publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society

|September 29, 2021

Summary

This summary is machine-generated.

Machine learning models can predict rare acute graft-versus-host disease (GVHD) after liver transplants. This tool identifies high-risk patients for closer monitoring, potentially improving outcomes for this serious complication.

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Induction of Graft-versus-host Disease and In Vivo T Cell Monitoring Using an MHC-matched Murine Model

Induction of Graft-versus-host Disease and In Vivo T Cell Monitoring Using an MHC-matched Murine Model

Published on: August 29, 2012

Area of Science:

Transplantation immunology
Medical informatics
Computational biology

Background:

Acute graft-versus-host disease (GVHD) is a rare but serious complication following orthotopic liver transplantation (OLT), associated with high mortality.
Predicting rare events like GVHD is challenging, necessitating advanced analytical approaches.

Purpose of the Study:

To develop and validate machine-learning algorithms for predicting the risk of acute GVHD in orthotopic liver transplant recipients.
To identify patients at high risk who may benefit from intensified monitoring strategies.

Main Methods:

Retrospective analysis of 1938 donor-recipient pairs undergoing OLT, with 19 (1.0%) developing GVHD.
Development of 7 machine-learning classification algorithms using a training set (70%) and evaluation on a test set (30%).
Validation of top-performing algorithms on an independent dataset of 75 recent OLT cases.

Main Results:

Several algorithms, including C5.0, heterogeneous ensemble, and GGBM, showed predictive capability in the test set (AUROC 0.83-0.86).
In the validation set, logistic regression, heterogeneous ensemble, and GGBM algorithms identified 9-11% of recipients as high-risk (AUROC 0.93-0.96).
The models successfully flagged patients who subsequently developed GVHD in the validation cohort.

Conclusions:

A practical machine-learning model can effectively identify orthotopic liver transplant recipients at high risk for acute GVHD.
This predictive model may guide the use of peripheral blood chimerism testing for enhanced patient monitoring.
Early identification of high-risk patients can potentially lead to timely interventions and improved transplant outcomes.