Development and Validation of a Risk Prediction Model for Oral Frailty in Older Adults With Mild Cognitive Impairment: A Cross-Sectional Study

Objectives Oral frailty is a multidimensional geriatric syndrome associated with adverse health outcomes. Older adults with mild cognitive impairment (MCI) may be particularly vulnerable; however, prediction tools tailored to this population remain limited. This study aimed to develop and internally validate a risk prediction model for oral frailty in older adults with MCI. Design Cross-sectional study for model development. Setting and Participants A total of 456 older adults diagnosed with MCI were recruited from a geriatric outpatient clinic between October 2025 and January 2026. Participants were randomly divided into a training cohort (70%) and an independent validation cohort (30%). Methods Oral frailty was assessed using the Oral Frailty Index-8. Candidate predictors included demographic, clinical, nutritional, and psychosocial variables. Feature selection was performed using random forest and least absolute shrinkage and selection operator regression. Logistic regression, decision tree, and extreme gradient boosting models were developed. Model discrimination, calibration, and clinical utility were evaluated in the validation cohort. Results The prevalence of oral frailty was 73.9%. In the validation cohort, the logistic regression model demonstrated good discrimination (AUC 0.917, 95% CI 0.855–0.979) and satisfactory calibration. Decision curve analysis indicated favorable net clinical benefit across a range of threshold probabilities. Independent predictors included denture burden (OR 161.016, 95% CI 42.603–920.001), chewing difficulty (OR 12.055, 95% CI 4.483–36.770), malnutrition (OR 8.076, 95% CI 2.909–25.738), depressive symptoms (OR 0.665, 95% CI 0.484–0.893), and living arrangement (OR 0.047, 95% CI 0.003–0.545). Conclusions and Implications Oral frailty among older adults with MCI is strongly associated with structural oral impairment, functional decline, nutritional vulnerability, and psychosocial factors. The proposed model demonstrates good predictive performance and may facilitate early identification of high-risk individuals in clinical settings. Integrated oral–cognitive assessment strategies may improve comprehensive geriatric care for this vulnerable population.