Development and Validation of Prediction Models for Incident Reversible Cognitive Frailty Based on Social-Ecological Predictors Using Generalized Linear Mixed Model and Machine Learning Algorithms: A Prospective Cohort Study.
This study aimed to develop and validate prediction models for incident reversible cognitive frailty (RCF) based on social-ecological predictors. Older adults aged ≥60 years from China Health and Retirement Longitudinal Study (CHARLS) 2011–2013 survey were included as training set (n = 1230). The ge...
| Publicado en: | Journal of Applied Gerontology Vol. 44; no. 2; pp. 255 - 267 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Feb2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=182194336&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 182194336 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07334648 JGR jtl: Journal of Applied Gerontology issn: 07334648 maglogo: Y pubinfo: dt: Feb2025 vid: 44 iid: 2 pid: 344 pub: Sage Publications Inc. artinfo: ui: 182194336 10.1177/07334648241270052 ppf: 255 ppct: 12 formats: tig: atl: Development and Validation of Prediction Models for Incident Reversible Cognitive Frailty Based on Social-Ecological Predictors Using Generalized Linear Mixed Model and Machine Learning Algorithms: A Prospective Cohort Study. aug: au: Liu, Qinqin Si, Huaxin Li, Yanyan Zhou, Wendie Yu, Jiaqi Bian, Yanhui Wang, Cuili affil: School of Nursing, 12465 Peking University, Beijing, China School of Public Health, 12465 Peking University, Beijing, China su: China Social factors Old age Cognition disorder risk factors Risk assessment Boosting algorithms Random forest algorithms Prediction models Research funding Frail elderly Longitudinal method Surveys Support vector machines Machine learning Algorithms sug: subj: Social factors Old age China Cognition disorder risk factors Risk assessment Boosting algorithms Random forest algorithms Prediction models Research funding Frail elderly Longitudinal method Surveys Support vector machines Machine learning Algorithms keyword: cognition community environment frailty risk factors cognition community environment frailty risk factors ab: This study aimed to develop and validate prediction models for incident reversible cognitive frailty (RCF) based on social-ecological predictors. Older adults aged ≥60 years from China Health and Retirement Longitudinal Study (CHARLS) 2011–2013 survey were included as training set (n = 1230). The generalized linear mixed model (GLMM), eXtreme Gradient Boosting, support vector machine, random forest, and Binary Mixed Model forest were used to develop prediction models. All models were evaluated internally with 5-fold cross-validation and evaluated externally via CHARLS 2013–2015 survey (n = 1631). Only GLMM showed good discrimination (AUC = 0.765, 95% CI = 0.736, 0.795) in training set, and all models showed fair discrimination (AUC = 0.578–0.667, 95% CI = 0.545, 0.725) in internal and external validation. All models showed acceptable calibration, overall prediction performance, and clinical usefulness in training and validation sets. Older adults were divided into three groups using risk score based on GLMM, which could assist healthcare providers to predict incident RCF, facilitating early identification of high-risk population. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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