Development of a nomogram for predicting cognitive impairment risk among older adults with heart disease
10.3969/j.issn.1006-9771.2026.09.001
- VernacularTitle:老年心脏病患者认知障碍风险列线图预测模型构建
- Author:
Xiaolei WANG
1
;
Xiaoqing ZHAO
2
;
Shuying CHEN
2
;
Minghao ZHANG
1
;
Xiaoyu ZHOU
2
;
Haiyan LI
1
;
Jianhua ZHANG
2
;
Qi JING
1
;
Zhiwei DONG
1
Author Information
1. School of Management, Shandong Second Medical University, Weifang, Shandong 261053, China
2. School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China
- Publication Type:Journal Article
- Keywords:
older adults;
heart disease;
cognitive impairment;
depression;
nomogram;
predictive model
- From:
Chinese Journal of Rehabilitation Theory and Practice
2026;32(9):993-1002
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo develop and validate a nomogram for predicting the risk of concurrent cognitive impairment in older adults with heart disease. MethodsSelf-reported data from 2 673 respondents in the 2020 China Health and Retirement Longitudinal Study were analyzed. The participants were randomly divided into a training set (n = 1 871) and a validation set (n = 802). Heart disease status was determined using a self-report questionnaire. Logistic regression analysis was performed to identify predictors, and a nomogram for predicting the risk of concurrent cognitive impairment in older adults with heart disease was developed using R software. Bootstrap resampling was used for internal validation, and model performance was further evaluated in the randomly split validation set. The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) and calibration curves.. ResultsA total of 2 673 older adults with heart disease were included, out of whom 315 participants in the training set had cognitive impairment. Lasso regression analysis identified age, education level, place of residence, instrumental activities of daily living and depression as predictors of concurrent cognitive impairment. A prediction model incorporating these five variables was constructed. The AUC were 0.827 (95%CI 0.804 to 0.850) in the training set and 0.847 (95%CI 0.812 to 0.882) in the validation set. The Hosmer-Lemeshow test yielded P = 0.154 and P = 0.716 in the training and validation sets, respectively. Calibration curves showed good agreement between predicted and observed probabilities. Decision curve analysis indicated that the model provided favorable net benefit and predictive performance. ConclusionThe nomogram may provide a useful reference for risk screening and early intervention for cognitive impairment in older adults with heart disease.
