Cystatin C for Early Mortality Prediction in First msTBI: Insights from Traditional and Machine Learning Approaches

Cystatin C for Early Mortality Prediction in First msTBI: Insights from Traditional and Machine Learning Approaches | 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 Cystatin C for Early Mortality Prediction in First msTBI: Insights from Traditional and Machine Learning Approaches Hao Qi, Lingli Li, Ao Li, Tianwei Pei, Zhisong Ding, Juan Fang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8058776/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 Cystatin C, a cysteine protease inhibitor, has been implicated in various central nervous system disorders, yet its clinical significance in traumatic brain injury (TBI) remains incompletely understood. This study aimed to evaluate the association between serum cystatin C levels and early mortality in patients with moderate to severe TBI (msTBI) admitted to the ICU, and to develop a machine learning-based predictive model for 28-day all-cause mortality. A total of 369 patients were included and categorized into tertiles according to their serum cystatin C concentrations. Multivariate Cox analysis confirmed that elevated cystatin C levels were independently associated with significantly increased risks of both 28-day and 90-day mortality in msTBI patients (HR > 1, P < 0.05). Using the Boruta algorithm, cystatin C was identified as the fourth most important predictive variable. A Cox proportional hazards (coxph) model achieved the best predictive performance, with an area under the curve (AUC) of 0.8644 in the training set and 0.8422 in the test set. These findings indicate that high cystatin C levels are independently associated with early mortality in msTBI, highlighting its potential utility as a prognostic biomarker. The developed model may serve as a practical tool for clinical risk assessment and inform intervention strategies. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Health sciences/Risk factors msTBI cystatin C All-cause mortality Machine learning Full Text Additional Declarations No competing interests reported. Table 1 to 3 are available in the Supplementary Files section. Supplementary Files SupplementaryMaterial2.docx table.docx 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. 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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-8058776","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608435301,"identity":"b929b3c9-c232-459f-ab77-58b6a4da79d1","order_by":0,"name":"Hao Qi","email":"","orcid":"","institution":"the 904th hospital of PLA, Wuxi Clinical College of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Qi","suffix":""},{"id":608435307,"identity":"f8ba96f1-7e26-4a2e-b5f0-16087333c2f5","order_by":1,"name":"Lingli Li","email":"","orcid":"","institution":"The First People’s Hospital of Xiangtan 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from Traditional and Machine Learning Approaches\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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},"keywords":"msTBI, cystatin C, All-cause mortality, Machine 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This study aimed to evaluate the association between serum cystatin C levels and early mortality in patients with moderate to severe TBI (msTBI) admitted to the ICU, and to develop a machine learning-based predictive model for 28-day all-cause mortality. A total of 369 patients were included and categorized into tertiles according to their serum cystatin C concentrations. Multivariate Cox analysis confirmed that elevated cystatin C levels were independently associated with significantly increased risks of both 28-day and 90-day mortality in msTBI patients (HR \u003e 1, P \u003c 0.05). Using the Boruta algorithm, cystatin C was identified as the fourth most important predictive variable. A Cox proportional hazards (coxph) model achieved the best predictive performance, with an area under the curve (AUC) of 0.8644 in the training set and 0.8422 in the test set. These findings indicate that high cystatin C levels are independently associated with early mortality in msTBI, highlighting its potential utility as a prognostic biomarker. 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