A statistical model for tuberculosis diagnosis to guide multi-test strategies and clinical decision-making

A statistical model for tuberculosis diagnosis to guide multi-test strategies and clinical decision-making | 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 A statistical model for tuberculosis diagnosis to guide multi-test strategies and clinical decision-making Marc-Olivier Boldi, Nada Abdulghafor, René Brouillet, Ludovico Cobuccio, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7067339/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 Objectives The diagnosis of tuberculosis (TB) remains complex, requiring multiple microbiological tests - microscopy, PCR, and culture - across various clinical specimen types. A central challenge is determining the optimal combination of test, specimen, and strategy for a given patient. We developed a predictive scoring system that integrates patient characteristics (e.g., age and sex) with test performance data to guide personalized, data-driven diagnostic decisions in multi-test settings. Methods We retrospectively analyzed data from 4,179 patients evaluated for respiratory TB between 2008 and 2018. Variables included age, sex, specimen type and diagnostic test results: smear microscopy, Xpert MTB/RIF, in-house real-time PCR for Mycobacterium tuberculosis complex (MTB), and mycobacterial culture. A multivariate logistic regression model was used to estimate test-specific performance between subgroups. These outputs informed a Hidden Markov Model (HMM) to simulate sequential testing strategies and calculate diagnostic probabilities. The entropy analysis quantified the information gained with each successive test. Results Multivariate logistic regression identified that age significantly influences TB prevalence, peaking in the 21–40 age group. Culture showed the highest sensitivity, followed by PCR, and microscopy performed the poorest. HMM-based simulations revealed that sequential testing improves diagnostic yield, but with diminishing returns. Entropy analysis confirmed that most diagnostic uncertainty is resolved by the first few tests, while subsequent tests contribute marginal additional value. Conclusions Mathematical modeling, combining HMM and entropy analysis, offers a framework for optimizing the diagnostic pathways for TB. Our approach enables test selection to be tailored to patient characteristics, potentially improving diagnostic efficiency and reducing unnecessary tests. Future work should integrate broader clinical and epidemiological variables as well as cost-effectiveness analysis to be able to inform diagnostic stewardship strategies for TB and other complex syndromes. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Biological sciences/Microbiology Full Text Additional Declarations No competing interests reported. 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-7067339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482917520,"identity":"0bd53a34-9aee-48e6-8805-32bc17af69e8","order_by":0,"name":"Marc-Olivier Boldi","email":"","orcid":"","institution":"Department of Operations, Faculty of Business and Economics, University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Marc-Olivier","middleName":"","lastName":"Boldi","suffix":""},{"id":482917521,"identity":"3cc59a0c-1db9-4b38-b473-a77d22609ba0","order_by":1,"name":"Nada 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A central challenge is determining the optimal combination of test, specimen, and strategy for a given patient. We developed a predictive scoring system that integrates patient characteristics (e.g., age and sex) with test performance data to guide personalized, data-driven diagnostic decisions in multi-test settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively analyzed data from 4,179 patients evaluated for respiratory TB between 2008 and 2018. Variables included age, sex, specimen type and diagnostic test results: smear microscopy, Xpert MTB/RIF, in-house real-time PCR for Mycobacterium tuberculosis complex (MTB), and mycobacterial culture. A multivariate logistic regression model was used to estimate test-specific performance between subgroups. These outputs informed a Hidden Markov Model (HMM) to simulate sequential testing strategies and calculate diagnostic probabilities. The entropy analysis quantified the information gained with each successive test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression identified that age significantly influences TB prevalence, peaking in the 21–40 age group. Culture showed the highest sensitivity, followed by PCR, and microscopy performed the poorest. HMM-based simulations revealed that sequential testing improves diagnostic yield, but with diminishing returns. Entropy analysis confirmed that most diagnostic uncertainty is resolved by the first few tests, while subsequent tests contribute marginal additional value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMathematical modeling, combining HMM and entropy analysis, offers a framework for optimizing the diagnostic pathways for TB. Our approach enables test selection to be tailored to patient characteristics, potentially improving diagnostic efficiency and reducing unnecessary tests. Future work should integrate broader clinical and epidemiological variables as well as cost-effectiveness analysis to be able to inform diagnostic stewardship strategies for TB and other complex syndromes.\u003c/p\u003e","manuscriptTitle":"A statistical model for tuberculosis diagnosis to guide multi-test strategies and clinical decision-making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 17:04:52","doi":"10.21203/rs.3.rs-7067339/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf6a382a-f9c0-4902-b9ff-a03804e1e2ea","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51276462,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":51276463,"name":"Health sciences/Diseases"},{"id":51276464,"name":"Health sciences/Health care"},{"id":51276465,"name":"Health sciences/Medical research"},{"id":51276466,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-07-21T07:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 17:04:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7067339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7067339","identity":"rs-7067339","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}