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In Vivo Mouse Model of Spinal Implant Infection

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Predicting VRE Infection After Liver Transplantation With a Time-Updated Colonization Score.

Nathalia N Nunes^1,2, Adhemar Villani Junior³, Lohayne A Ferreira¹

¹Department of Infectious Diseases and Tropical Medicine, Hospital das Clinicas, Faculdade de Medicina, University of Sao Paulo, Sao Paulo, Brazil.

Transplant Infectious Disease : an Official Journal of the Transplantation Society

|March 6, 2026

Summary

This summary is machine-generated.

Post-liver transplant Vancomycin-resistant Enterococcus (VRE) infection risk factors were identified. Time-updated VRE colonization post-transplant is the strongest predictor, aiding in developing predictive models for VRE infection.

Keywords:

change for risk factor analysis machine learning multidrug‐resistant organisms renal replacement therapy time‐updated colonization

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Area of Science:

Hepatology and Transplant Surgery
Infectious Diseases
Medical Informatics

Background:

Vancomycin-resistant Enterococcus (VRE) infection poses a significant threat to liver transplant (LT) recipients.
Identifying risk factors for VRE infection is crucial for improving patient outcomes post-LT.

Purpose of the Study:

To identify key risk factors associated with VRE infection after liver transplantation.
To develop a predictive score for VRE infection in LT recipients.

Main Methods:

Retrospective cohort study of adult LT recipients (2010-2022).
Time-to-event analysis using Fine-Gray models with time-dependent covariates for VRE colonization.
Machine learning (Random Forests) as a comparator, with model discrimination assessed by AUROC.

Main Results:

VRE infection occurred in 6.3% of 1209 LT recipients; intra-abdominal infections were most common.
Time-updated post-LT VRE colonization (HR 7.59) was the strongest predictor.
Other risk factors included intraoperative bleeding, re-transplantation, pre-LT colonization, and RRT; certain conditions like viral hepatitis and autoimmune hepatitis were protective.

Conclusions:

A predictive model incorporating identified risk factors, especially post-LT VRE colonization, can effectively identify LT recipients at high risk for VRE infection.
This model demonstrates good discrimination and balanced sensitivity/specificity, aiding clinical risk stratification.