Prediction of symptomatic and asymptomatic bacteriuria in spinal cord injury patients using machine learning
Our findings demonstrate that signatures within catheter and urine microbiota could serve as tools to monitor the health status of SCI patients. Establishing a classification system based on these microbial signatures could equip physicians with alternative management strategies, potentially reducing UTI episodes and associated hospital costs, thus significantly improving patient quality of life while mitigating the impact of drug-resistant UTI. Video Abstract.
Authors
- Bonsan B. Lee (ORCID: https://orcid.org/0000-0002-0433-2913)
- Gerard Weber
- Stuart A. Rice (ORCID: https://orcid.org/0000-0002-9486-2343)
- Gustavo Espinoza-Vergara (ORCID: https://orcid.org/0000-0001-9869-4650)
- Parisa Noorian (ORCID: https://orcid.org/0000-0002-4215-1080)
- Julie Pryor (ORCID: https://orcid.org/0000-0003-4907-8530)
- Diane McDougald (ORCID: https://orcid.org/0000-0001-5827-8441)
- M. Mozammel Hoque (ORCID: https://orcid.org/0000-0002-6014-6955)
- Priyadarshini Chari
- Iain G. Duggin (ORCID: https://orcid.org/0000-0003-4868-7350)
- Joyce To
- Dominic Leo
Institutions
- University of Technology Sydney (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- The University of Sydney (AU)
- Royal North Shore Hospital (AU)
- Prince of Wales Hospital (AU)
- Agriculture and Food (AU)
- Royal Rehabilitation Centre (AU)
- Neuroscience Research Australia (AU)
Publication Details
- Journal
- Microbiome
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1186/s40168-025-02213-8
- Primary Topic
- Urinary Tract Infections Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Technology Sydney