What does this research mean for the field?

A risk prediction model for vancomycin-resistant Enterococcus (VRE) infection in liver transplant recipients can effectively identify patients at higher risk based on specific clinical factors. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Identify risk factors associated with VRE infection in liver transplant recipients and develop a risk prediction score.

March 17, 2026Open Access

Risk Prediction Model for Vre Infection in Liver Transplant Recipients

Key Points

Identify risk factors associated with VRE infection in liver transplant recipients and develop a risk prediction score.
Historical cohort study of liver transplant recipients from 2010 to 2022
Follow-up from 90 days pre-transplant to 180 days post-transplant
Bivariate and multivariate analyses using the Fine–Gray model
Incorporation of various risk factors into a prediction model
6.4% of patients developed VRE infection post-transplant
Identified key risk factors like renal replacement therapy, intraoperative bleeding, and post-transplant VRE colonization
Developed a prediction model with an area under the curve between 76.01% and 84.75%
Achieved sensitivity between 75.13% and 89.33% and specificity between 74.6% and 85.87%

Abstract

Vancomycin-resistant Enterococcus spp. (VRE) are multidrug-resistant microorganisms frequently isolated in liver transplant (LT) patients. In this population, VRE infection is potentially associated with worse clinical outcomes. The objective of this study was to identify the main risk factors associated with VRE infection in the post-transplant period and to develop a risk prediction score for VRE infection. Historical cohort study including all adults who underwent LT between 2010 and 2022 in a tertiary-care hospital. Follow-up was from 90 days before transplant to 180 days after transplant. The outcome was VRE infection within the first 180 days after LT. Bivariate and multivariate analyses were performed using the Fine–Gray model, considering death as a competing risk. Prediction model metrics were calculated weekly, considering VRE colonization as a time-dependent variable, and described by ROC curves, sensitivity, specificity, accuracy, precision, and F1 score. A total of 1,209 patients were included; of these, 77 (6.4%) developed VRE infection post-transplant, most of which were intra-abdominal infections (58.4%). The main risk factors identified were: need for renal replacement therapy (OR 2.01; 95% CI 1.4–2.8), intraoperative bleeding (OR 4.01; 95% CI 2.7–6.0), post-transplant VRE colonization (OR 7.59; 95% CI 5.1–11.1) and pre-transplant colonization (OR 3.19; 95% CI 2.2–4.5), retransplantation (OR 3.85; 95% CI 2.6–5.6) and ICU length of stay (OR 1.01; 95% CI 1.0–1.02). Conversely, history of viral hepatitis (OR 0.54; 95% CI 0.3–0.7), autoimmune hepatitis (OR 0.33; 95% CI 0.1–0.6), Charlson comorbidity score (OR 0.84; 95% CI 0.7–0.9) and deceased-donor transplant (OR 0.23; 95% CI 0.1–0.3) were protective factors. All these variables were incorporated into the risk prediction model. The final score presented an area under the curve between 76.01% and 84.75%, with sensitivity from 75.13% to 89.33% and specificity from 74.6% to 85.87%. It was possible to develop a risk prediction score for VRE infection in liver transplant recipients with good discriminative performance. The proposed tool may support targeted surveillance and prevention strategies in higher-risk populations.

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Cite This Study

Nunes et al. (Sun,) studied this question.

synapsesocial.com/papers/69b8ef52deb47d591b8c5579 https://doi.org/https://doi.org/10.1016/j.bjid.2026.105117

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