Risk Factors and Prediction of Chronic Postsurgical Pain Among Patients with Distal Lower fracture: Cohort Analysis

Risk Factors and Prediction of Chronic Postsurgical Pain Among Patients with Distal Lower fracture: Cohort Analysis | 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 Risk Factors and Prediction of Chronic Postsurgical Pain Among Patients with Distal Lower fracture: Cohort Analysis Yangzi Zhu, Ying Wu, Kailun Gao, Yuning Sun, Liwei Wang, Junfeng Hu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5807228/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background: The surgical interventions aimed at fracture repair can paradoxically lead to chronic postoperative pain (CPSP), which is associated with depression, impaired quality of life, and increased societal burden. This phenomenon is particularly understudied in young patients with distal lower extremity fracture. Developing a scalable and accurate predictive model could revolutionize postoperative care by enabling early detection of high-risk patients and guiding personalized pain management strategies. Methods: This study collected in-hospital medical records and conducted follow-up for all patients over a one-year period. We developed a predictive model through a three-stage approach involving Least Absolute Shrinkage and Selection Operator (LASSO) regression, information gain analysis, and multivariate logistic regression, followed by model validation. Using the Shinyapps.io platform to build a webpage risk calculator for the final prediction model. Results: The final cohort included 818 patients: 38.39% of whom experienced CPSP, and 18.15% experienced neuropathic pain. There are six independent variables associated with CPSP: postoperative analgesic technique, fixation type, preoperative clinical management, and NRS score on the day of the visit and postoperative day 1. The optimism-corrected area under the receiver operating curve for the development cohort and validation cohort were 0.872 and 0.838, respectively and this model demonstrated good calibration and clinical utility. A web-based predictive nomogram was established by integrating machine learning-based big data variable screening with the interpretability of traditional logistic regression. Conclusions: This study demonstrates that pain management strategies, surgical approaches, and patient psychological factors collectively influence the development of CPSP. By integrating machine learning-based big data variable screening with the interpretability of traditional logistic regression, we developed a web-based predictive nomogram capable of identifying early CPSP risk at hospital discharge, thereby improving accessibility to transitional pain care interventions. Health sciences/Neurology/Neurological disorders/Neuropathic pain Health sciences/Biomarkers/Predictive markers Health sciences/Risk factors Health sciences/Health care/Disease prevention Health sciences/Health care/Fracture repair Health sciences/Diseases/Neurological disorders/Movement disorders Health sciences/Diseases/Trauma Chronic postsurgical pain Fracture Internal fixation Prediction model Pain management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-5807228","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":404666385,"identity":"4689a760-74a3-401b-9658-f453e2124646","order_by":0,"name":"Yangzi Zhu","email":"","orcid":"","institution":"Xuzhou Clinical College of Xuzhou Medical University, Xuzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yangzi","middleName":"","lastName":"Zhu","suffix":""},{"id":404666377,"identity":"795101f0-1490-4559-8b6a-c82b50c50351","order_by":1,"name":"Ying Wu","email":"","orcid":"","institution":"Xuzhou Clinical College of Xuzhou Medical University, Xuzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wu","suffix":""},{"id":463907712,"identity":"5376a933-c8a5-4b69-983e-0f36d3cf9dba","order_by":2,"name":"Kailun Gao","email":"","orcid":"","institution":"Xuzhou Clinical College of Xuzhou Medical University, Xuzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kailun","middleName":"","lastName":"Gao","suffix":""},{"id":404666378,"identity":"da51ec96-2190-4113-b58d-1f7a21a15ccb","order_by":3,"name":"Yuning Sun","email":"","orcid":"","institution":"Xuzhou Clinical College of Xuzhou Medical University, Xuzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuning","middleName":"","lastName":"Sun","suffix":""},{"id":404666384,"identity":"3a173ce8-8680-4c17-9a25-10762dff20c0","order_by":4,"name":"Liwei Wang","email":"","orcid":"","institution":"Xuzhou Clinical College of Xuzhou Medical University, Xuzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liwei","middleName":"","lastName":"Wang","suffix":""},{"id":404666386,"identity":"d9e1b576-cd76-4fb0-af9d-34488f34b84a","order_by":5,"name":"Junfeng Hu","email":"","orcid":"","institution":"Shaoxing People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Hu","suffix":""},{"id":463907713,"identity":"0f30fde1-57ce-457f-a807-70344b4c5122","order_by":6,"name":"Jun-Li Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYJACgwcGNTxs7A2MBxKI1pJQcUyGn+cAA/FaGBLOMNtIzkhgOECUav72wwcKEtvYeAxuPn5w4GHb4TwG9sNHN+DTInEmLcEgsU2Gx+B2msGBxLbDxQw8aWk38PqDIcfAAGzL7QSwlsQGCR4z/Fr434C0MAMddvwDkVokgLYAvc8jOYOHSFskbjxLAAUyDz9PTsGBhHPpiW2E/MLfn3zM4INBjT0b+/GND3+UWSf2sx8+hlcLELAZwJmMbEAuAeUgwPwAwf5DhPpRMApGwSgYcQAAFoRQZp5eAR0AAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, China","correspondingAuthor":true,"prefix":"","firstName":"Jun-Li","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2025-01-11 04:38:05","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5807228/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-5807228/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84562530,"identity":"d2b864e3-1d43-4f27-863f-ab40597c4a4f","added_by":"auto","created_at":"2025-06-13 13:24:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89377,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Flow Diagram\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5807228/v2/ad17fa0cf75ee95c6366a3cc.png"},{"id":84562523,"identity":"6b6fcc2d-3e4b-4f29-8236-1f4fd9f32726","added_by":"auto","created_at":"2025-06-13 13:23:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49293,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of NRS at 3 months postoperative across different fracture sites\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5807228/v2/7547d46594fe47640158be4d.png"},{"id":84562846,"identity":"c34a6a09-7637-4b0e-afbf-8094e3a7b7e1","added_by":"auto","created_at":"2025-06-13 13:31:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":192372,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression analysis was used to select characteristic factors.\u003c/p\u003e\n\u003cp\u003e(A) Coefficient paths for different variables. The x-axis represents Log(λ), the y-axis represents the coefficients of the variables, and different coloured lines denote different variables. As Log(λ) changes, the coefficients of most variables approach zero.\u003c/p\u003e\n\u003cp\u003e(B) Illustration of the relationship between binomial deviance and Log(λ). Each red dot represents the binomial deviance for a specific λ value, with error bars denoting the standard error of the deviance. As Log(λ) increases, the binomial deviance initially decreases and then increases, reaching a minimum point that determines the optimal λ, which produces ten nonzero coefficients.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5807228/v2/24e2701456bdacfa9a293a7c.png"},{"id":84562522,"identity":"b11a25b7-4879-47b3-a6e4-85812726c243","added_by":"auto","created_at":"2025-06-13 13:23:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113914,"visible":true,"origin":"","legend":"\u003cp\u003eThe degree of information gain of each factor to the prediction of CPSP\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5807228/v2/558a06a02bc61c47735cfaf5.png"},{"id":84562508,"identity":"8c58bbef-e53b-4390-92fa-a0ba9c19271c","added_by":"auto","created_at":"2025-06-13 13:23:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104007,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting CPSP\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5807228/v2/c0d13ff4eead12549b4a6670.png"},{"id":84562520,"identity":"797d0385-7cb9-41ed-a23d-c3adeb615cfb","added_by":"auto","created_at":"2025-06-13 13:23:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":122744,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the nomogram in the training and validation cohort.\u003c/p\u003e\n\u003cp\u003eThe graph presents the area under the receiver operating characteristic (ROC) curves for predictive model in development (A) and validation cohort (B), with higher scores indicating better performance.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5807228/v2/1d24450b290d367f42ec9a92.png"},{"id":84563551,"identity":"141615ad-75f3-47a2-8157-ac7dc27ecb5d","added_by":"auto","created_at":"2025-06-13 13:40:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":726727,"visible":true,"origin":"","legend":"","description":"","filename":"Updatedmunuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5807228/v2_covered_9bac082b-a5de-44d9-be9e-74766054698f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRisk Factors and Prediction of Chronic Postsurgical Pain Among Patients with Distal Lower fracture: Cohort Analysis\u003c/strong\u003e\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":"Chronic postsurgical pain, Fracture, Internal fixation, Prediction model, Pain management","lastPublishedDoi":"10.21203/rs.3.rs-5807228/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5807228/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The surgical interventions aimed at fracture repair can paradoxically lead to chronic postoperative pain (CPSP), which is associated with depression, impaired quality of life, and increased societal burden. This phenomenon is particularly understudied in young patients with distal lower extremity fracture. Developing a scalable and accurate predictive model could revolutionize postoperative care by enabling early detection of high-risk patients and guiding personalized pain management strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study collected in-hospital medical records and conducted follow-up for all patients over a one-year period. We developed a predictive model through a three-stage approach involving Least Absolute Shrinkage and Selection Operator (LASSO) regression, information gain analysis, and multivariate logistic regression, followed by model validation. Using the Shinyapps.io platform to build a webpage risk calculator for the final prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The final cohort included 818 patients: 38.39% of whom experienced CPSP, and 18.15% experienced neuropathic pain. There are six independent variables associated with CPSP: postoperative analgesic technique, fixation type, preoperative clinical management, and NRS score on the day of the visit and postoperative day 1. The optimism-corrected area under the receiver operating curve for the development cohort and validation cohort were 0.872 and 0.838, respectively and this model demonstrated good calibration and clinical utility. A web-based predictive nomogram was established by integrating machine learning-based big data variable screening with the interpretability of traditional logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study demonstrates that pain management strategies, surgical approaches, and patient psychological factors collectively influence the development of CPSP. By integrating machine learning-based big data variable screening with the interpretability of traditional logistic regression, we developed a web-based predictive nomogram capable of identifying early CPSP risk at hospital discharge, thereby improving accessibility to transitional pain care interventions.\u003c/p\u003e","manuscriptTitle":"Risk Factors and Prediction of Chronic Postsurgical Pain Among Patients with Distal Lower fracture: Cohort Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-06-13 13:23:46","doi":"10.21203/rs.3.rs-5807228/v2","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}},{"code":1,"date":"2025-01-20 15:56:30","doi":"10.21203/rs.3.rs-5807228/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":"c21e1f9b-9b71-469d-aaab-be0adf6a31be","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49259253,"name":"Health sciences/Neurology/Neurological disorders/Neuropathic pain"},{"id":49259254,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":49259255,"name":"Health sciences/Risk factors"},{"id":49259256,"name":"Health sciences/Health care/Disease prevention"},{"id":49259257,"name":"Health sciences/Health care/Fracture repair"},{"id":49259258,"name":"Health sciences/Diseases/Neurological disorders/Movement disorders"},{"id":49259259,"name":"Health sciences/Diseases/Trauma"}],"tags":[],"updatedAt":"2025-02-10T03:39:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 13:23:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-5807228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5807228","identity":"rs-5807228","version":["v2"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}