Development and Validation of a Predictive Model for Mild Cognitive Impairment in Older Adults with Multimorbidity.

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.