Development and validation of a Cognitive Impairment Risk Prediction Model for Elderly Patients with Multimorbidity

Development and validation of a Cognitive Impairment Risk Prediction Model for Elderly Patients with Multimorbidity | 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 Cognitive Impairment Risk Prediction Model for Elderly Patients with Multimorbidity Ruxu Ge, Xiaoqing Zhao, Ya zhang, Yuxin Jiang, Tongtong Guo, Zhiwei Dong, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6664257/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: Cognitive impairment is a prevalent issue among the elderly population. Multimorbidity has been pinpointed as a clinical risk factor that is both potential and easy to recognize. However, the association between multimorbidity and cognitive decline among older adults in China remains underexplored.Based on this,this study aims to develop a risk prediction model for cognitive impairment in elderly individuals aged 60 and above with multimorbidity. Methods: This investigation used information from the 2020 China Health and Retirement Longitudinal Study (CHARLS), including a total of 5,977 elderly patients with multimorbidity. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was used to select feature variables. To address the issue of imbalanced cognitive impairment data distribution, the Synthetic Minority Oversampling Technique (SMOTE) algorithm was applied for data balancing. A cognitive impairment risk prediction model was constructed, and its performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis (DCA). Results: Among the 5,977 elderly patients with multimorbidity, 3,047 (50.98%) had normal cognition, while 2,930 (49.02%) were diagnosed with cognitive impairment. Logistic regression analysis identified 11 influencing factors for cognitive impairment in elderly patients with multimorbidity, including age, gender, education level, marital status, type of residence, pension insurance, health insurance, social participation, basic activities of daily living (BADL), instrumental activities of daily living (IADL), and depression. Based on these 11 variables, a cognitive impairment risk prediction model was developed. In the training dataset, the model achieved an AUC of 0.809 (95% CI: 0.796–0.822), while in the validation dataset, the AUC was 0.819 (95% CI: 0.800–0.839). The accuracy was 0.742 and 0.749, sensitivity was 0.775 and 0.720, and specificity was 0.711 and 0.779, respectively, demonstrating a strong consistency between predicted and actual values. Conclusion: The cognitive impairment risk prediction model developed in this study exhibited good predictive performance, providing scientific evidence for community healthcare professionals in the early assessment and identification of cognitive impairment risk in elderly patients with multimorbidity. Multimorbidity Elderly Cognitive Impairment Risk 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-6664257","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525106886,"identity":"52529cca-7350-465c-be92-611d4d859128","order_by":0,"name":"Ruxu Ge","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruxu","middleName":"","lastName":"Ge","suffix":""},{"id":525106887,"identity":"96c013e8-4590-4f54-89a8-8a2b62ce488b","order_by":1,"name":"Xiaoqing Zhao","email":"","orcid":"","institution":"Shandong Second Medical 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Multimorbidity has been pinpointed as a clinical risk factor that is both potential and easy to recognize. However, the association between multimorbidity and cognitive decline among older adults in China remains underexplored.Based on this,this study aims to develop a risk prediction model for cognitive impairment in elderly individuals aged 60 and above with multimorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis investigation used information from the 2020 China Health and Retirement Longitudinal Study (CHARLS), including a total of 5,977 elderly patients with multimorbidity. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was used to select feature variables. To address the issue of imbalanced cognitive impairment data distribution, the Synthetic Minority Oversampling Technique (SMOTE) algorithm was applied for data balancing. A cognitive impairment risk prediction model was constructed, and its performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong the 5,977 elderly patients with multimorbidity, 3,047 (50.98%) had normal cognition, while 2,930 (49.02%) were diagnosed with cognitive impairment. Logistic regression analysis identified 11 influencing factors for cognitive impairment in elderly patients with multimorbidity, including age, gender, education level, marital status, type of residence, pension insurance, health insurance, social participation, basic activities of daily living (BADL), instrumental activities of daily living (IADL), and depression. Based on these 11 variables, a cognitive impairment risk prediction model was developed. In the training dataset, the model achieved an AUC of 0.809 (95% CI: 0.796–0.822), while in the validation dataset, the AUC was 0.819 (95% CI: 0.800–0.839). 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