With the further aging of the population, the incidence of cognitive impairment is increasing, and there is a lack of effective treatments. The construction of an accurate risk prediction model can be used to help community healthcare workers to identify, warn and intervene with potential patients at an early stage, and to reduce the pressure on social healthcare.

This study aims to construct a prediction model for the risk of cognitive impairment in older adults in the community, analyse the influencing factors of cognitive impairment in older adults, and provide empirical references for the development of targeted interventions.

In April 2024, elderly people aged ≥60 years were selected from the China Health and Retirement Longitudinal Survey (CHARLS) 2020 database (n=7 334) , and their socio-demographic characteristics and data on their health status and behaviours, activities of daily living (ADL) , depression, and cognitive abilities were collected. They were randomly divided into a training set (n=5 133) and a validation set (n=2 201) in a ratio of 7∶3. The best predictor variables were screened using LASSO regression ten-fold cross-validation, the factors influencing cognitive impairment in older adults were analysed using Logistic regression, and nomagram were constructed, and the performance of the predicion model was assessed using the area under the curve of the subject work characteristics (ROC) curves and the analysis of the calibration curves.

The detection rate of cognitive impairment in older adults was 14.48% (1 062/7 334) . LASSO regression screened nine potential predictor variables, which were age, type of residence, marital status, gender, education, exercise, society, activity of daily living, and depression. The results of multifactorial Logistic regression analysis showed that age [OR (95%CI) =1.238 (1.109-1.504) for 70-79 years old and OR (95%CI) =2.231 (1.546-3.222) for ≥80 years old using 60-69 years old as a reference] , type of residence [OR (95%CI) =2.144 (1.617-2.842) for rural using urban as a reference] , marital status [OR (95%CI) =0.691 (0.562-0.851) for no spouse, using spousal as a reference] , education [OR (95%CI) =0.209 (0.173-0.254) for primary school and below, using illiteracy as a reference, and for junior high school OR (95%CI) =0.059 (0.038-0.090) , OR (95%CI) for high school/vocational high school=0.043 (0.021-0.089) , and OR (95%CI) for college and above=0.038 (0.005-0.280) ] , and society [with no society as a reference, and OR (95%CI) with society=0.746 (0.624-0.892) ] , ability to perform ADL [OR (95%CI) =1.529 (1.171-1.997) with no impairment as a reference and OR (95%CI) =1.580 (1.319-1.891) with impairment] , and depression [OR (95%CI) =1.580 (1.319-1.891) with no depression as a reference and OR (95%CI) =1.580 (1.319-1.891) with depression] were the influencing factors of cognitive impairment (P<0.05) . Based on the seven predictor variables screened by multifactor Logistic regression analysis, a prediction model was established. The areas under the ROC curves of the prediction model in the training and validation sets were 0.821 (95%CI=0.805-0.836) and 0.839 (95%CI=0.817-0.861) , respectively; the Hosmer-Lemeshow test χ²=5.022 (P=0.755) and χ²=3.963 (P=0.860) ; calibration curves showed significant agreement between predicted and actual values.

In this study, a prediction model for the risk of cognitive impairment in community-dwelling older adults containing a total of seven indicators, including age, residence, and so on, was established, and the prediction model had good accuracy and differentiation, which can be used to identify the risk of developing cognitive impairment in older adults.