Abstract Background Individuals with spinal cord injuries (SCI) frequently rely on urinary catheters to drain urine from the bladder, making them susceptible to asymptomatic and symptomatic catheter-associated bacteriuria and urinary tract infections (UTI). Proper identification of these conditions lacks precision, leading to inappropriate antibiotic use which promotes selection for drug-resistant bacteria. Since infection often leads to dysbiosis in the microbiome and correlates with health status, this study aimed to develop a machine learning-based diagnostic framework to predict potential UTI by monitoring urine and/or catheter microbiome data, thereby minimising unnecessary antibiotic use and improving patient health. Results Microbial communities in 609 samples (309 catheter and 300 urine) with asymptomatic and symptomatic bacteriuria status were analysed using 16S rRNA gene sequencing from 27 participants over 18 months. Microbial community compositions were significantly different between asymptomatic and symptomatic bacteriuria, suggesting microbial community signatures have potential application as a diagnostic tool. A significant decrease in local (alpha) diversity was noted in symptomatic bacteriuria compared to the asymptomatic bacteriuria ( P &lt; 0.01). Beta diversity measured in weighted unifrac also showed a significant difference ( P &lt; 0.05) between groups. Supervised machine learning models trained on amplicon sequence variant (ASVs) counts and bacterial taxonomic abundances (Taxa) to classify symptomatic and asymptomatic bacteriuria with a 10-fold cross-validation approach. Combining urine and catheter microbiome data improved the model performance during cross-validation, yielding a mean area under the receiver operating characteristic curve (AUROC) of 0.91-0.98 (Interquartile range, IQR 0.93-0.96) and 0.78-0.91 (IQR 0.86-0.88) for ASVs and taxonomic features, respectively. ASVs and taxa features achieve a mean AUROC of 0.85-1 (IQR 0.93-0.98) and 0.69-0.99 (IQR 0.78-0.88) in the independent held-out test set, respectively, signifying their potential in differentiating symptomatic and asymptomatic bacteriuria states. Conclusions Our findings demonstrate that signatures within catheter and urine microbiota could serve as tools to monitor the health status of SCI patients. Establishing an early warning 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.

Prediction of symptomatic and asymptomatic bacteriuria in spinal cord injury patients using machine learning