Early detection of Vascular Catheter-Associated Infections employing supervised machine learning - A case study in Lleida Region

Early detection of Vascular Catheter-Associated Infections employing supervised machine learning - A case study in Lleida Region | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early detection of Vascular Catheter-Associated Infections employing supervised machine learning - A case study in Lleida Region Radu Spaimoc, Jordi Mateo, Francesc Solsona, Alfredo Jover-Sáez, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5791717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Aug, 2025 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted 9 You are reading this latest preprint version Abstract Healthcare-associated infections (HAIs), particularly Vascular Catheter-Associated Infections (VCAIs), are a significant concern, accounting for over 7% of all infections and are often linked to medical devices. Early detection of VCAIs before invasive infection is crucial for improving hospital care and reducing antibiotic use. This study retrospectively developed reliable machine learning models to clasify VCAIs from patient medical records, excluding fever and antibiotic prescription indicators. The dataset, collected from the group of public hospitals of the Lleida health region in Catalonia (Spain) between 2011 and 2019, consisted of 24,239 episodes with 150 features related to vascular catheter use. After validation, processing and feature engineering, the dataset showed an imbalance, with 94.46% (10,090) non-catheter episodes and 5.53% (591) catheter infection cases. The study’s results underscore that classifiers have demonstrated respectable performance postpreprocessing and feature engineering despite the initial imbalance within the dataset, with balanced accuracies ranging between 70% and 80%. Notably, the Decision Tree (DT) classifier emerged as the frontrunner, achieving 82%, thereby validating its effectiveness. While various oversampling strategies, such as SMOTE, were explored, these did not significantly strengthen the performance beyond what was achieved using DT alone. This study highlights that strategic feature engineering with the DT classifier is sufficient to obtain robust VCAI detection before the apparition of a probable sepsis. Hospital-associated infections Vascular catheter-associated infections diagnosis machine learning imbalance oversampling feature engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Aug, 2025 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 14 May, 2025 Reviews received at journal 12 May, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviews received at journal 21 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers invited by journal 20 Apr, 2025 Submission checks completed at journal 19 Apr, 2025 First submitted to journal 31 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5791717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445968810,"identity":"8217fefc-93c8-4f2f-8a98-51f56d89468d","order_by":0,"name":"Radu Spaimoc","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYHACAzDJBiI+MDDLkKaFcQYDMw+IbiBKCwgA1ROhxbz98DYJhj935PnEDh/8bNtmzcPAfvj5A3xaZM6klUkwtj0zbJNOS5bObUvnYeBJM8RriwRDjpkEY8NhxjbpHDPm3LbDPEAhAlr435gBHXbYHqzFEqyF/SN+LRJAWxjYDieCtTCCtfAQsEXiWbFFYtuzZJBfJHvOpfOw8eQUzsDvsOSNNz78uWM7f3bywQ8/yqzl+NmPb/iATwsYJDAcQHDYCCqHgAMEVYyCUTAKRsEIBgA8x0AAoxnkGQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Lleida","correspondingAuthor":true,"prefix":"","firstName":"Radu","middleName":"","lastName":"Spaimoc","suffix":""},{"id":445968811,"identity":"e6a02c0f-8c28-448c-97a3-e89e737168a3","order_by":1,"name":"Jordi Mateo","email":"","orcid":"","institution":"University of Lleida","correspondingAuthor":false,"prefix":"","firstName":"Jordi","middleName":"","lastName":"Mateo","suffix":""},{"id":445968813,"identity":"649c8c19-6ffc-4d5c-b6f6-a48aba7c5ac4","order_by":2,"name":"Francesc Solsona","email":"","orcid":"","institution":"University of Lleida","correspondingAuthor":false,"prefix":"","firstName":"Francesc","middleName":"","lastName":"Solsona","suffix":""},{"id":445968814,"identity":"85a914fa-b958-4c28-b3d5-29ca9adcd066","order_by":3,"name":"Alfredo Jover-Sáez","email":"","orcid":"","institution":"Hospital Universitari Arnau de Vilanova","correspondingAuthor":false,"prefix":"","firstName":"Alfredo","middleName":"","lastName":"Jover-Sáez","suffix":""},{"id":445968817,"identity":"4b6e6a13-25f2-43a7-bf69-118991865472","order_by":4,"name":"Fernando Barcenilla","email":"","orcid":"","institution":"Hospital Universitari Arnau de Vilanova","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Barcenilla","suffix":""},{"id":445968819,"identity":"80c18caa-d3c6-400e-9e6f-9d303abe032b","order_by":5,"name":"María Ramírez","email":"","orcid":"","institution":"Hospital Universitari Arnau de Vilanova","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Ramírez","suffix":""},{"id":445968820,"identity":"c341d1ca-54f3-4e39-8675-7f954cb090e0","order_by":6,"name":"Marcos Serrano","email":"","orcid":"","institution":"Hospital Universitari de Santa Maria","correspondingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"","lastName":"Serrano","suffix":""},{"id":445968822,"identity":"f9a26a37-756d-4ae7-9107-0b172c4dae6a","order_by":7,"name":"Miquel Mesas","email":"","orcid":"","institution":"Hospital Universitari de Santa Maria","correspondingAuthor":false,"prefix":"","firstName":"Miquel","middleName":"","lastName":"Mesas","suffix":""},{"id":445968823,"identity":"babfd79f-82d3-4659-a076-dd9dcccf2c13","order_by":8,"name":"Dídac Florensa","email":"","orcid":"","institution":"Hospital Universitari de Santa Maria","correspondingAuthor":false,"prefix":"","firstName":"Dídac","middleName":"","lastName":"Florensa","suffix":""}],"badges":[],"createdAt":"2025-01-08 20:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5791717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5791717/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12911-025-03113-5","type":"published","date":"2025-08-11T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89310683,"identity":"41a04295-20b0-40a3-a0f9-5789144af6ec","added_by":"auto","created_at":"2025-08-18 16:09:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2204497,"visible":true,"origin":"","legend":"","description":"","filename":"BMIDM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5791717/v1_covered_ac9314c5-76b2-4b34-9051-833ef8146d65.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early detection of Vascular Catheter-Associated Infections employing supervised machine learning - 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