Gregor Donabauer, Anca Rath, Aila Caplunik-Pratsch, Anja Eichner, Jürgen Fritsch, Martin Kieninger, Susanne Gaube, Wulf Schneider-Brachert, Udo Kruschwitz, Bärbel Kieninger
Abstract
The isolation of affected patients and intensified infection control measures are used to prevent nosocomial transmission of vancomycin-resistant enterococci (VRE), but early detection of VRE carriers is needed. However, there are still no standard screening criteria for VRE, which poses a significant threat to patient safety. Our study aimed to develop and evaluate an artificial intelligence (AI)-based approach for identifying and predicting of at-risk patients who could assist infection prevention and control staff through a human-in-the-loop approach.
Introduction
Given the global rise in antibiotic resistance, hospitals must develop effective strategies to combat nosocomial infections caused by multidrug-resistant organisms. According to the WHO Bacterial Priority Pathogen List, vancomycin-resistant enterococci (VRE) are a significant threat to patient safety and causing a growing burden of disease. In Europe. A rising number of reported enterococcal bloodstream infections (BSI; +57.3%) – and especially of VRE BSI (+80.0%) – was reported between 2017-202. Moreover, in 2019, 200,000 deaths worldwide were attributable to or associated with VRE.
Methods
The study was approved by and conducted according to the guidelines of the Ethics Committee of the University of Regensburg (approval number 22-3113-104, 27th October 2022).
Results
To train our deep learning models and evaluate the approach, we utilized a historical dataset from one year (2019, deliberately chosen before the COVID-19 pandemic) from our tertiary care hospital. This dataset includes data from 8,372 patients (sex: 5,128 males, 3,244 females; age distribution: 0-10 years: 55, 11-20 years: 59, 21-30 years: 327, 31-40 years: 477, 41-50 years: 570, 51-60 years: 1,328, 61-70 years: 1,969, 71-80 years: 1,913, 81-90 years: 1,462, 91-100 years: 208, 101-110 years: 4), representing approximately one-third of the patients hospitalized in our facility during 2019.
Discussion
In this study, we successfully developed an AI-based method that uses hospital movement data within heterogeneous graph structures to predict the putative VRE status of patients. The findings from various studies [4,7,10–17] indicating that VRE transmission occurs through contact with VRE carriers and surface contamination inspired us to model hospitals as nodes and edges, thereby replicating transmission routes.
Conclusion
We demonstrated that modeling a “living” hospital as a graph and processing it with a GNN is a promising approach for the early detection of VRE carriers. We successfully identified patients in a historical dataset who were most likely to test positive for VRE within the next three days. If this method could be applied in real time, it could prove to be a powerful alternative for the current unsystematic VRE screening procedures.
Citation: Donabauer G, Rath A, Caplunik-Pratsch A, Eichner A, Fritsch J, Kieninger M, et al. (2025) AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. PLOS Digit Health 4(4): e0000821. https://doi.org/10.1371/journal.pdig.0000821
Editor: Sulaf Assi, Reader in Forensic Intelligent Data Analysis, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
Received: September 4, 2024; Accepted: March 8, 2025; Published: April 10, 2025
Copyright: © 2025 Donabauer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The test data set is a historical data set of our hospital and may not be published for data protection reasons. The implementations of our approach are publicly available via GitLab and can be accessed via the following link: https://git.uni-regensburg.de/dog21258/vre-outbreak-detection.git
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
