Bloodwork-free Early Screening for Alzheimer’s Disease via Comorbid Pattern Recognition in Electronic Health Records

Bloodwork-free Early Screening for Alzheimer’s Disease via Comorbid Pattern Recognition in Electronic Health Records | 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 Bloodwork-free Early Screening for Alzheimer’s Disease via Comorbid Pattern Recognition in Electronic Health Records Dmytro Onishchenko, James A. Mastrianni, Ishanu Chattopadhyay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8789582/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Early identification of Alzheimer’s disease and related dementias (ADRD) remains limited by the need for specializedtests and late-stage diagnosis. The Zero-burden Risk Assessment (ZeBRA) is a AI-driven score that predictsincident ADRD up to a decade before diagnosis, using only routine electronic health record (EHR) data, withoutlaboratory tests, imaging, or questionnaires. Trained on 487,989 cases and 12,483,718 controls from nationwideU.S. insurance claims and validated on held-back samples , and two independent cohorts, ZeBRA achievedAUC = 0.93 and 0.83 for predicting out to 1-year and 10-year horizons respectively, maintaining positive likelihoodratios (>10) at 95% specificity and stable discrimination over time (AUC drop ≈ 1 to 1.3% per year). Performancewas consistent across age, sex, race, and ethnicity subgroups. In a limited prospective pilot, higher ZeBRA scorescorrelated with lower Montreal Cognitive Assessment (MoCA) scores, indicating a greater degree of cognitiveimpairment (R = −0.78). Compared with prior EHR-based models, ZeBRA provides superior accuracy, cross-site generalizability, and demonstrates noise-corrected interpretability via our novel Λ-OR attrubution metric. Its scalability, low cost, and independence from specialized testing position ZeBRA as a practical tool for population-level early detection and presymptomatic trial enrichment. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Supplementary Files SI.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers invited by journal 22 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 08 Feb, 2026 First submitted to journal 04 Feb, 2026 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-8789582","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":587777678,"identity":"bb2d5634-1a7e-4f9b-9758-7c45d504b499","order_by":0,"name":"Dmytro Onishchenko","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Dmytro","middleName":"","lastName":"Onishchenko","suffix":""},{"id":587777679,"identity":"57631671-a032-4d45-81e0-b005a30a9ff1","order_by":1,"name":"James A. 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