ORIGINAL RESEARCH article
Front. Neurol.
Sec. Cognitive and Behavioral Neurology
Development and Validation of a Predictive Model for Mild Cognitive Impairment in Older Adults with Multimorbidity
Provisionally accepted
- The First Affiliated Hospital of Xiamen University, Xiamen, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: To analyze the factors associated with mild cognitive impairment (MCI) in older adults with multimorbidity and to develop and validate a corresponding risk prediction model, thereby providing a reference for the early identification and prevention of MCI in this population. Methods: This cross-sectional study consecutively enrolled 238 older adult inpatients with multimorbidity at The Affiliated Kangning Hospital of Wenzhou Medical University, China, between April 2022 and February 2025. Participants were assessed using a self-designed general information questionnaire and the Montreal Cognitive Assessment Basic Scale (MoCA-B). MCI was diagnosed according to the Chinese Expert Consensus. Associated factors were identified using logistic regression analysis. A nomogram prediction model was constructed based on these factors. The model's performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). An external validation cohort (n=68) was used to further test the model. Results: The incidence of MCI among older adults with multimorbidity was 26.81%. Multivariable logistic regression identified age ≥80 years (OR=3.227, 95%CI: 1.572–6.653), hearing impairment (OR=4.035, 95%CI: 1.912–8.564), and emotional disorders (OR=3.250, 95%CI: 1.465–7.216) as risk factors for MCI. Higher education level (OR=0.234, 95%CI: 0.131–0.420), more frequent physical exercise (OR=0.145, 95%CI: 0.057–0.394), and more frequent social activities (OR=0.263, 95%CI: 0.103–0.697) were protective factors (all P<0.05). The areas under the ROC curve (AUC) for the nomogram model were 0.862 (95%CI: 0.800–0.942) in the training set and 0.832 (95%CI: 0.775–0.890) in the external validation set. The calibration curves showed good agreement between predicted and observed probabilities. The DCA indicated that the model provided a net clinical benefit across a wide range of threshold probabilities (approximately 10% to 65%). Conclusions: Several factors, including age, hearing impairment, emotional disorders, education level, physical exercise frequency, and social activity frequency, are significantly associated with MCI in older adults with multimorbidity. The developed nomogram model demonstrates good predictive accuracy and clinical applicability, offering a practical tool for early clinical screening and targeted prevention.
Keywords: Elderly, Mild Cognitive Impairment, multimorbidity, Prediction model, Predictive performance, Prevention and control measures
Received: 19 Sep 2025; Accepted: 24 Dec 2025.
Copyright: © 2025 Lv, Xu, Li and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yanfei Lv
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.