.png){kind=icon}
.png){kind=icon}
Return
Development and Validation of a Predictive Model for Mild Cognitive Impairment in Older Adults with Multimorbidity
.png){kind=icon}
OA
AI
L
R
Y
X
DOI：10.2147/cia.s608488.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Lili Shao,1 Ruyi Zhang,2 Yinqing Huang,2 Xiaochun Dai3 1Department of Geriatric Medicine, The Affiliated Kangning Hospital of Wenzhou Medical University, Zhejiang Provincial Clinical Research Center for Mental Disorder, Wenzhou, Zhejiang, 325000, People’s Republic of China; 2Department of Geriatric Medicine, The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, People’s Republic of China; 3Department of Medicine, The Affiliated Kangning Hospital of Wenzhou Medical University, Zhejiang Provincial Clinical Research Center for Mental Disorder, Wenzhou, Zhejiang, 325000, People’s Republic of China Correspondence: Xiaochun Dai, Department of Medicine, The Affiliated Kangning Hospital of Wenzhou Medical University, Zhejiang Provincial Clinical Research Center for Mental Disorder, No. 1, Shengjin Road, Huanglong Residential Quarter, Wenzhou, Zhejiang, 325000, People’s Republic of China, Email epw6492@163.com Objective: To identify factors associated with mild cognitive impairment (MCI) in older adults with multimorbidity and to develop and validate a predictive model for early screening. 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: MCI prevalence was 26.81%. Risk factors: age ≥ 80 years (OR=3.23), hearing impairment (OR=4.04), and emotional disorders (OR=3.25). Protective factors: higher education (OR=0.23), more frequent physical exercise (OR=0.15), and more frequent social activities (OR=0.26) (all P< 0.05). The AUC was 0.862 (training) and 0.832 (external validation). Calibration curves showed good agreement, and DCA indicated net clinical benefit across threshold probabilities of (10– 65%). Conclusion: Age, hearing impairment, emotional disorders, education, physical exercise, and social activities are significantly associated with MCI in older adults with multimorbidity. The nomogram demonstrates good predictive accuracy and clinical utility, aiding early identification and targeted prevention. Keywords: multimorbidity, elderly, mild cognitive impairment, prediction model, prevention and control measures, predictive performance
Keywords:
multimorbidity
elderly
mild cognitive impairment
prediction model
prevention and control measures
predictive performance
Journal
IF:
3.7
Papers:
3.1K
Citations:
1.1W
Organization
Cited Papers