Machine Learning-Based Model for Predicting Preoperative Deep Vein Thrombosis in Patients with Peri-ankle Fractures

Machine Learning-Based Model for Predicting Preoperative Deep Vein Thrombosis in Patients with Peri-ankle Fractures | 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 Machine Learning-Based Model for Predicting Preoperative Deep Vein Thrombosis in Patients with Peri-ankle Fractures Meihui Zhao, Li Zhou, Shengxun Huang, Jiale Dai, Yuanyuan Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8087004/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 Objective To investigate the risk factors for preoperative deep vein thrombosis (DVT) in patients with peri-ankle fractures and to develop a machine learning-based prediction model for preoperative DVT risk. Methods A retrospective study was conducted involving 1000 patients with peri-ankle fractures. Predictors were selected using Lasso regression, and the dataset was randomly split into a training set and a validation set at a 7:3 ratio. Four models—Logistic Regression, Decision Tree, Random Forest, and XGBoost—were constructed and internally validated using the Bootstrap method. Model performance was compared using metrics including the area under the ROC curve (AUC), calibration curves, decision curve analysis, and the F1 score. The optimal model was interpreted using the SHAP method. Results Four predictors were identified: age, BMI, preoperative waiting time, and D-dimer level. The XGBoost model demonstrated the best performance, with an AUC of 0.827 in the validation set. SHAP analysis confirmed that higher values of these four features contributed positively to the model's predictions, aligning with clinical knowledge and ensuring a transparent and credible decision-making process. Conclusion The XGBoost-based prediction model exhibits favorable performance and interpretability. It delineates core risk factors to guide targeted nursing interventions and holds potential for translation into a clinical decision-support tool, offering a novel strategy for the precise prevention of preoperative DVT. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Peri-ankle Fracture Deep Vein Thrombosis༛Machine Learning༛Prediction Model 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-8087004","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":603841970,"identity":"e98df197-b0d3-4875-afda-79281c9fb3e7","order_by":0,"name":"Meihui Zhao","email":"","orcid":"","institution":"Baoding First Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meihui","middleName":"","lastName":"Zhao","suffix":""},{"id":603841971,"identity":"ad1a1c89-e9cd-4d7b-9b4b-2613c1ce2ea4","order_by":1,"name":"Li Zhou","email":"","orcid":"","institution":"Chengde Medical 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12:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8087004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8087004/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108165756,"identity":"f10c1997-3f6d-46c6-8636-c17fb682ffc3","added_by":"auto","created_at":"2026-04-30 05:40:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":537039,"visible":true,"origin":"","legend":"","description":"","filename":"file.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8087004/v1_covered_47db7ad9-ce44-4f80-97cb-caf427cb6ab6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning-Based Model for Predicting Preoperative Deep Vein Thrombosis in Patients with Peri-ankle 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Model","lastPublishedDoi":"10.21203/rs.3.rs-8087004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8087004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the risk factors for preoperative deep vein thrombosis (DVT) in patients with peri-ankle fractures and to develop a machine learning-based prediction model for preoperative DVT risk.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective study was conducted involving 1000 patients with peri-ankle fractures. Predictors were selected using Lasso regression, and the dataset was randomly split into a training set and a validation set at a 7:3 ratio. Four models\u0026mdash;Logistic Regression, Decision Tree, Random Forest, and XGBoost\u0026mdash;were constructed and internally validated using the Bootstrap method. Model performance was compared using metrics including the area under the ROC curve (AUC), calibration curves, decision curve analysis, and the F1 score. The optimal model was interpreted using the SHAP method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour predictors were identified: age, BMI, preoperative waiting time, and D-dimer level. The XGBoost model demonstrated the best performance, with an AUC of 0.827 in the validation set. SHAP analysis confirmed that higher values of these four features contributed positively to the model's predictions, aligning with clinical knowledge and ensuring a transparent and credible decision-making process.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe XGBoost-based prediction model exhibits favorable performance and interpretability. It delineates core risk factors to guide targeted nursing interventions and holds potential for translation into a clinical decision-support tool, offering a novel strategy for the precise prevention of preoperative DVT.\u003c/p\u003e","manuscriptTitle":"Machine Learning-Based Model for Predicting Preoperative Deep Vein Thrombosis in Patients with Peri-ankle Fractures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-15 14:32:02","doi":"10.21203/rs.3.rs-8087004/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":"2c0bf280-50e1-415a-836a-9f03de14c93c","owner":[],"postedDate":"March 15th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-04-30T05:26:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64254930,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":64254931,"name":"Health sciences/Diseases"},{"id":64254932,"name":"Health sciences/Health care"},{"id":64254933,"name":"Health sciences/Medical research"},{"id":64254934,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-30T05:40:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-15 14:32:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8087004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8087004","identity":"rs-8087004","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}