Predicting mild cognitive impairment among rural older adults in China: development and validation of a risk prediction model

作者：Jingzheng Yan, Yaning Wang, Zhaotai Wang, Yingjuan Cao · 发表于：BMC Geriatrics · 年份：2026 · DOI：10.1186/s12877-026-07360-7 · 研究领域：Dementia and Cognitive Impairment Research、Frailty in Older Adults、Acute Ischemic Stroke Management

BACKGROUND: Identifying mild cognitive impairment (MCI) in its early stages is vital for averting dementia and fostering healthy aging. However, most screening tools depend on neuropsychological assessments that are expensive, time-consuming, and hard to implement in rural or resource-limited areas. With the increasing use of digital health technologies in community care, there is a pressing need for data-driven models that can be turned into simple, digital tools for large-scale MCI risk screening. This study aimed to identify factors linked to MCI among individuals aged 60 and above in rural China, develop and validate a data-driven, digitally implementable prediction model, and provide evidence to support early intervention strategies. METHODS: Data were collected from 3,375 participants aged ≥ 60 in the 2020 China Health and Retirement Longitudinal Study (CHARLS). Participants were allocated randomly to a training group (70%) and a validation group (30%). Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by multivariable logistic regression to develop the predictive model. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). RESULTS: 3,375 individuals aged 60 and older were included in this analysis, based on data from the 2020 China Health and Retirement Longitudinal Study (CHARLS); 723 individuals (21.4%) were identified as ...