[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128377-en":3,"doc-seo-128377-105":31,"detail-sidebar-cat-0-en-105":84},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128377,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Prediction of symptomatic and asymptomatic bacteriuria in spinal cord injury patients using machine learning","The study addresses imprecise identification of asymptomatic and symptomatic catheter-associated bacteriuria in individuals with spinal cord injury, where inadequate classification drives unnecessary antibiotic use and promotes drug-resistant bacteria. It leverages the link between infection, urinary/catheter microbiome dysbiosis, and health status to build machine learning diagnostic models. Using 609 catheter and urine samples with 16S rRNA sequencing, the approach distinguishes bacteriuria states and enables monitoring to improve patient outcomes.","Hoque et al. Microbiome (2025) 13:246 [https://doi.org/10.1186/s40168-025-02213-8](https://doi.org/10.1186/s40168-025-02213-8)  \nMicrobiome  \n RESEARCH Open Access  \nPrediction of symptomatic and asymptomatic bacteriuria in spinal cord injury patients using machine learning  \nM. Mozammel Hoque 1, Parisa Noorian 1, Gustavo Espinoza‑Vergara 1, Joyce To1, Dominic Leo1, Priyadarshini Chari2, Gerard Weber3, Julie Pryor3,4, Iain G. Duggin 1, Bonsan B. Lee5,6, Scott A. Rice 1,7* and Diane McDougald1*  \nAbstract  \nBackground Individuals with spinal cord injuries (SCI) frequently rely on urinary catheters to drain urine  \nfrom the bladder, making them susceptible to asymptomatic and symptomatic catheter‑associated bacteriuria and urinary tract infections (UTI) . Current identification of these conditions lacks precision, leading to inappropri‑ ate antibiotic use, which promotes selection for drug‑resistant bacteria. Since infection often leads to dysbiosisin the microbiome and correlates with health status, this study aimed to develop a machine learning‑based diag‑ nostic framework to predict potential UTI by monitoring urine and/or catheter microbiome data, thereby minimising unnecessary antibiotic use and improving patient health.  \nResults 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, suggest‑ ing 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 \u003C 0. 01) . Beta diversity measured in weighted unifrac also showed a significant difference (P \u003C 0. 05) between groups. Supervised machine learning models were trained on amplicon sequence variant (ASVs) counts and bacterial taxonomic abun‑ dances (Taxa) to classify symptomatic and asymptomatic bacteriuria with a repeated tenfold and leave‑one‑out participant (LOPO) type of cross‑validation approaches. Combining urine and catheter microbiome data improved the model performance during repeated tenfold cross‑validation, yielding a mean area under the receiver operating characteristic curve (AUROC) of 0.95 (95% CI 93–0. 97) and 0.83 (95% CI 0.79–0. 89) for ASVs and taxonomic features in the independent held‑out test set, respectively. The LOPO cross‑validation yielded a mean AUROC of 0.87 (95% CI 0 .85–0. 89) and 0.79 (95% CI 0 .77–0. 82) for ASVs and taxa features, respectively. These results suggest the potential of microbiome features in differentiating symptomatic and asymptomatic bacteriuria states.  \nConclusions 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  \n*Correspondence:  \nScott A. Rice [Scott.Rice@csiro.au](Scott.Rice@csiro.au)  \nDiane McDougald [diane.mcdougald@uts.edu.au](diane.mcdougald@uts.edu.au)  \nFull list of author information is available at the end of the article  \n© Crown 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate‑ rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to ob","cbCaifvfEfh5S9Be","https://ap.wps.com/l/cbCaifvfEfh5S9Be","pdf",3970010,2,1,20,"English","en",105,"# Abstract\n## Background\n## Results\n## Conclusions\n# Background\n## Catheter-associated urinary tract infection and bacteriuria definitions\n## Asymptomatic vs symptomatic bacteriuria and the urinary microbiome","[{\"question\":\"What impact did combining urine and catheter microbiome data have on performance?\",\"answer\":\"Combining urine and catheter microbiome data improved model performance in repeated tenfold cross-validation, producing higher AUROC values than models based on a single data source.\"}]","Prediction of symptomatic and asymptomatic bacteriuria in spinal cord injury patients using machine learning | PDF",1785947175,50,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"prediction-of-symptomatic-and-asymptomatic-bacteriuria-in-spinal-cord-injury-patients-using-machine-learning","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/prediction-of-symptomatic-and-asymptomatic-bacteriuria-in-spinal-cord-injury-patients-using-machine-learning/128377/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-30","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"What impact did combining urine and catheter microbiome data have on performance?","Question",{"text":76,"@type":77},"Combining urine and catheter microbiome data improved model performance in repeated tenfold cross-validation, producing higher AUROC values than models based on a single data source.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":85},[86,90,94,98,103,107,112,115,119,122,126],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":30,"slug":106},6,"Technology","technology",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":22,"slug":118},9,"Religion & Spirituality","religion-spirituality",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":22,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":99,"slug":129},19,"General","general"]