Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis

Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis | 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 Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis Can-yang Li, Ya-qin Li, Zhuang Zhuang, Ya-qi Wang, Ni Gong, Qi-yuan Lyu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4531143/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 Background Frailty is common in people with arthritis and may result in a range of adverse consequences. This study aimed to investigate risk factors for frailty in people with arthritis and to develop and validate a nomogram prediction model. Methods The study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS). This study analyzed 36 indicators including socio-demographic, behavioral, and health status factors. Participants were randomly included in training and validation sets in a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used on the training set to screen the best predictor variables of the model through 10-fold cross-validation. Binary logistic regression was used to explore the related factors of frailty in people with arthritis. Construct nomograms to develop prediction models. Use receiver operating characteristic (ROC) curves to evaluate the discrimination ability of the model, Calibration curves to evaluate calibration, and decision curve analysis (DCA) to evaluate clinical validity. Results A total of 6209 people with arthritis were included in this study, of whom 952 (15.3%) suffered from frailty. The nomogram model includes 9 predictive factors: age, gender, activities of daily living (ADL), waistline, cognitive function, depressive symptoms, hearing status, self-perceived health status, and inpatient needs. The model shows good consistency and accuracy. The AUC values for the model in the training set and validation set are 0.866 (95% CI = 0.852-0.880) and 0.854 (95% CI = 0.832-0.876) respectively. The calibration curves showed good accuracy between the nomogram model and actual observations. ROC and DCA showed that the nomogram had good predictive performance. Conclusions The frailty risk prediction model constructed in this study has good discrimination, calibration, and clinical validity in people with arthritis. It is a promising and convenient tool that can be used as an objective guide for the clinical screening of high-risk populations. Arthritis Frailty Prediction model Nomogram Lasso regression CHARLS 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-4531143","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312517562,"identity":"30e2e5d7-5f2e-4096-94ad-ebf969c546ae","order_by":0,"name":"Can-yang Li","email":"","orcid":"","institution":"School of Nursing, Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Can-yang","middleName":"","lastName":"Li","suffix":""},{"id":312517563,"identity":"cd43ed5d-427e-40c9-aedb-704d9aa0da40","order_by":1,"name":"Ya-qin Li","email":"","orcid":"","institution":"School of Nursing, Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ya-qin","middleName":"","lastName":"Li","suffix":""},{"id":312517564,"identity":"55795ac1-7551-487a-9558-77eafb5ebb1d","order_by":2,"name":"Zhuang Zhuang","email":"","orcid":"","institution":"School of Nursing, Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Zhuang","middleName":"","lastName":"Zhuang","suffix":""},{"id":312517566,"identity":"2ce7edea-7129-427c-91cc-7d47aabdfa31","order_by":3,"name":"Ya-qi Wang","email":"","orcid":"","institution":"School of Nursing, Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ya-qi","middleName":"","lastName":"Wang","suffix":""},{"id":312517568,"identity":"7622a4a6-3a37-445c-8caf-3ba25f3f413f","order_by":4,"name":"Ni Gong","email":"","orcid":"","institution":"School of Nursing, Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ni","middleName":"","lastName":"Gong","suffix":""},{"id":312517570,"identity":"f0f3b25d-a79b-48a7-99c4-58da5bd59ae5","order_by":5,"name":"Qi-yuan Lyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYNCCAgY5Nvb2A6RoMWAw5uM5k0CalsR5Eg4GRCq+kfzswRuDw+ltEgwJDD8qthGjJc3ccI7B4dw26cYDjD1nbhOjJcFMmscgLbdN5kACM2MbUVrSv4G0pLNJJBgQqyUHZItNAvFaJM+8KZOcY2Bj2AYM5INE+YXvePo2iTcVEvLy7e0HH/yoIEKLwgEgwQPlHCCsHgjkG5C0jIJRMApGwSjACgBq1jlhKzPm7wAAAABJRU5ErkJggg==","orcid":"","institution":"School of Nursing, Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Qi-yuan","middleName":"","lastName":"Lyu","suffix":""}],"badges":[],"createdAt":"2024-06-05 03:36:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4531143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4531143/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78959862,"identity":"d380fca3-ef59-4b81-a109-65e38b5ec483","added_by":"auto","created_at":"2025-03-21 11:17:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":884611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4531143/v1_covered_92b1e07e-fecf-4fe5-b5c1-9bab913b1eaa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis","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":"Arthritis, Frailty, Prediction model, Nomogram, Lasso regression, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-4531143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4531143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty is common in people with arthritis and may result in a range of adverse consequences. This study aimed to investigate risk factors for frailty in people with arthritis and to develop and validate a nomogram prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS). This study analyzed 36 indicators including socio-demographic, behavioral, and health status factors. Participants were randomly included in training and validation sets in a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used on the training set to screen the best predictor variables of the model through 10-fold cross-validation. Binary logistic regression was used to explore the related factors of frailty in people with arthritis. Construct nomograms to develop prediction models. Use receiver operating characteristic (ROC) curves to evaluate the discrimination ability of the model, Calibration curves to evaluate calibration, and decision curve analysis (DCA) to evaluate clinical validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 6209 people with arthritis were included in this study, of whom 952 (15.3%) suffered from frailty. The nomogram model includes 9 predictive factors: age, gender, activities of daily living (ADL), waistline, cognitive function, depressive symptoms, hearing status, self-perceived health status, and inpatient needs. The model shows good consistency and accuracy. The AUC values for the model in the training set and validation set are 0.866 (95% CI = 0.852-0.880) and 0.854 (95% CI = 0.832-0.876) respectively. The calibration curves showed good accuracy between the nomogram model and actual observations. ROC and DCA showed that the nomogram had good predictive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe frailty risk prediction model constructed in this study has good discrimination, calibration, and clinical validity in people with arthritis. It is a promising and convenient tool that can be used as an objective guide for the clinical screening of high-risk populations.\u003c/p\u003e","manuscriptTitle":"Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 11:38:22","doi":"10.21203/rs.3.rs-4531143/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":"185ca141-0961-4cf5-9b5b-45687c3fe842","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-21T11:09:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-19 11:38:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4531143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4531143","identity":"rs-4531143","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}