Long COVID Diagnostic with Differentiation from Chronic Lyme Disease using Machine Learning and Cytokine Hubs

Long COVID Diagnostic with Differentiation from Chronic Lyme Disease using Machine Learning and Cytokine Hubs | 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 Long COVID Diagnostic with Differentiation from Chronic Lyme Disease using Machine Learning and Cytokine Hubs Bruce Patterson, Jose Guevara-Coto, Javier Mora, Edgar B. Francisco, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3873244/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The absence of a diagnostic for long COVID (LC) or post-acute sequelae of COVID-19 (PASC) has profound implications for research and potential therapeutics. Further, symptom-based identification of patients with long-term COVID-19 lacks the specificity to serve as a diagnostic because of the overlap of symptoms with other chronic inflammatory conditions like chronic Lymedisease (CLD), myalgic encephalomyelitis-chronic fatigue syndrome (ME-CFS), and others. Here, we report a machine-learning approach to long COVID diagnosis using cytokine hubs that are also capable of differentiating long COVID from chronic Lyme. We constructed three tree-based classifiers: decision tree, random forest, and gradient-boosting machine (GBM) and compared their diagnostic capabilities. A 223 patient dataset was partitioned into training (178 patients) and evaluation (45 patients) sets. The GBM model was selected based on performance (89% Sensitivity and 96% Specificity for LC) with no evidence of overfitting. We tested the GBM on a random dataset of 124 individuals (106 PASC and 18 Lyme), resulting in high sensitivity (97%) and specificity 90% for LC). A Lyme Index composed of two features ((TNF-alpha +IL-4)/(IFN-gamma + IL-2) and (TNF-alpha *IL-4)/(IFN-gamma + IL-2 + CCL3) was constructed as a confirmatory algorithm to discriminate between LC and CLD. Immunology COVID-19 PASC long COVID cytokines chronic lyme disease (CLD) myalgic encephalomyelitis-chronic fatigue syndrome (ME-CFS) Machine Learning/AI Full Text Additional Declarations The authors declare potential competing interests as follows: Competing interests: B.K.P, C.B., J.G-M, G.L. and E.B.F. are employees of IncellDx, Inc Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3873244","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267652249,"identity":"08c16341-c970-44f6-8b14-c891ca8501e4","order_by":0,"name":"Bruce Patterson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACAwhlwwMkGA88YCNeSxpIC8OBBBK0HGYgXou59BnDDx93nJcxZz984EBC2WEG/vbuBLxaLPtyjCVnnrnNY9mTlnAg4Vwag8SZsxvwO+wMjxkzb9ttHoMbPAYHEttsGAwkconQ8rftHFAL/wegFgkitTC2HQDZwkCcLZY9bMWSvWeSeQzOpBmA/MJD0C/mPMwbP/zcYWdvcPzwwwcfyg7L8bf34tcCBowNCDYPYeXoWkbBKBgFo2AUYAAAlABHpEkPAOUAAAAASUVORK5CYII=","orcid":"","institution":"IncellDx","correspondingAuthor":true,"prefix":"","firstName":"Bruce","middleName":"","lastName":"Patterson","suffix":""},{"id":267655181,"identity":"be4bd0eb-32e9-43dc-abef-24cb459d3fe4","order_by":1,"name":"Jose Guevara-Coto","email":"","orcid":"","institution":"IncellDx Inc","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"","lastName":"Guevara-Coto","suffix":""},{"id":267655182,"identity":"9766e730-4419-46ed-a35f-f237482d12af","order_by":2,"name":"Javier Mora","email":"","orcid":"","institution":"Lab of Tumor Chemosensitivity, CIET / CICICA, Faculty of Microbiology, Universidad de Costa Rica","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Mora","suffix":""},{"id":267655183,"identity":"d9665b00-bb66-4d2e-a64c-c88fd2f02124","order_by":3,"name":"Edgar B. 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