HRRT: A Holistic Renal Replacement Therapy Decision-Making Support System Using Hierarchical Reinforcement Learning

HRRT: A Holistic Renal Replacement Therapy Decision-Making Support System Using Hierarchical Reinforcement Learning | 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 Article HRRT: A Holistic Renal Replacement Therapy Decision-Making Support System Using Hierarchical Reinforcement Learning Qianyi Xu, Feng Wu, Zi Yi Christopher Thong, Mark Sen Liang Goh, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8266196/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Renal replacement therapy (RRT) is a critical intervention for patients with acute kidney injury (AKI). However, clinical decision-making regarding the timing of initiation, modality selection, optimal ultrafiltration rate, and weaning criteria remains highly complex and exhibits significant practice variation. While RCTs have demonstrated that indiscriminate high-intensity RRT offers no benefit in unselected populations, they fail to guide adaptive strategies, leaving dynamic decision-making heavily reliant on empirical experience. Consequently, a substantial gap persists in delivering patientspecific recommendations, particularly for dynamically adjusting treatment in response to clinical progression. Therefore, we develop Hierarchical Reinforcement Learning for Renal Replacement Therapy (HRRT), a holistic clinical decision support system (CDSS) that covers the full decisionmaking process in RRT. 2,467 Intensive Care Unit (ICU) stays of 1,439 patients, within a cohort of patients with AKI from a US hospital, were used for training and internal testing of the model. The model’s performance was evaluated on two external validation sets, where we selected 1085 ICU stays of 1085 patients from Netherlands and 1230 stays of 845 patients from China. The estimated mortality rate decreased by 6.1 percentage points from 47.7% (95%CI: 45.2 – 50.0) to 41.6% (95%CI: 35.6 - 47.2) compared to clinician-led outcomes. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Nephrology Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviews received at journal 21 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 07 Dec, 2025 Submission checks completed at journal 06 Dec, 2025 First submitted to journal 03 Dec, 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. We do this by developing innovative software and high quality services for the global research community. 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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-8266196","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558429482,"identity":"d33a7f80-f36c-4e2d-81b9-38b0980e67e6","order_by":0,"name":"Qianyi Xu","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Qianyi","middleName":"","lastName":"Xu","suffix":""},{"id":558429483,"identity":"b38d69e0-dea3-4cec-9afd-fa6b86522235","order_by":1,"name":"Feng Wu","email":"","orcid":"","institution":"National University of 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Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital 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