Guiding Parenteral Nutrition Therapy After Hematopoietic Stem Cell Transplantation

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Abstract

Abstract Adults undergoing hematopoietic cell transplantation often develop serious complications that cause rapid nutritional decline. We developed and evaluated an AI approach to standardize intravenous nutrition (total parenteral nutrition, TPN) during this vulnerable period. Using real-world records from Stanford Health Care (6,402 transplants, 2008–2025), we analyzed 1,404 adults who received TPN, totaling 27,447 patient-days linking each day’s clinical state to the next day’s prescription. We created a library of 30 standardized TPN regimens and trained a model to recommend next-day dose adjustments based on laboratory data and the existing prescription. (Pearson r ≈ 0.71). We then assessed an AI policy learned from past care and found that the Reinforcement learning agent selected dose adjustments with a higher composite score than the existing clinical policy. These results show that AI-guided TPN is feasible and may enhance bedside decision-making for adult transplant care, warranting prospective evaluation. Teaser AI-enabled, standardized TPN potentially cuts complexity and boosts consistency in transplant nutrition.
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Guiding Parenteral Nutrition Therapy After Hematopoietic Stem Cell Transplantation | 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 Guiding Parenteral Nutrition Therapy After Hematopoietic Stem Cell Transplantation Abdel Badih el Ariss, Thanaphong Phongpreecha, Marc Ghanem, Mehrdad Kimari, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7982137/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Adults undergoing hematopoietic cell transplantation often develop serious complications that cause rapid nutritional decline. We developed and evaluated an AI approach to standardize intravenous nutrition (total parenteral nutrition, TPN) during this vulnerable period. Using real-world records from Stanford Health Care (6,402 transplants, 2008–2025), we analyzed 1,404 adults who received TPN, totaling 27,447 patient-days linking each day’s clinical state to the next day’s prescription. We created a library of 30 standardized TPN regimens and trained a model to recommend next-day dose adjustments based on laboratory data and the existing prescription. (Pearson r ≈ 0.71). We then assessed an AI policy learned from past care and found that the Reinforcement learning agent selected dose adjustments with a higher composite score than the existing clinical policy. These results show that AI-guided TPN is feasible and may enhance bedside decision-making for adult transplant care, warranting prospective evaluation. Teaser AI-enabled, standardized TPN potentially cuts complexity and boosts consistency in transplant nutrition. Health sciences/Health care Health sciences/Medical research Full Text Additional Declarations Competing interest reported. The methods described in this manuscript are covered in the U.S. provisional patent no. 63/268,689 (WO2022256850A1; “Systems and methods to assess neonatal health risk and uses thereof”) approved in 2022. T.P. is a co-founder of Takeoff41. N.A. is a member of the Scientific Advisory Boards of January AI, Parallel Bio, Celine Therapeutics, and WellSim Biomedical Technologies, is a co-founder of Takeoff41, and is a paid consultant for MaraBio Systems. Other authors declare that they have no other competing interests relevant to this work. Supplementary Files Supplementarymaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Feb, 2026 Reviews received at journal 15 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviews received at journal 01 Jan, 2026 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 29 Oct, 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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The methods described in this manuscript are covered in the U.S. provisional patent no. 63/268,689 (WO2022256850A1; “Systems and methods to assess neonatal health risk and uses thereof”) approved in 2022. T.P. is a co-founder of Takeoff41. N.A. is a member of the Scientific Advisory Boards of January AI, Parallel Bio, Celine Therapeutics, and WellSim Biomedical Technologies, is a co-founder of Takeoff41, and is a paid consultant for MaraBio Systems. Other authors declare that they have no other competing interests relevant to this work.","formattedTitle":"Guiding Parenteral Nutrition Therapy After Hematopoietic Stem Cell Transplantation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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 Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7982137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7982137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdults undergoing hematopoietic cell transplantation often develop serious complications that cause rapid nutritional decline. We developed and evaluated an AI approach to standardize intravenous nutrition (total parenteral nutrition, TPN) during this vulnerable period. Using real-world records from Stanford Health Care (6,402 transplants, 2008–2025), we analyzed 1,404 adults who received TPN, totaling 27,447 patient-days linking each day’s clinical state to the next day’s prescription. We created a library of 30 standardized TPN regimens and trained a model to recommend next-day dose adjustments based on laboratory data and the existing prescription. (Pearson r ≈ 0.71). We then assessed an AI policy learned from past care and found that the Reinforcement learning agent selected dose adjustments with a higher composite score than the existing clinical policy. 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