Oral frailty is closely associated with systemic health and quality of life in elderly patients, particularly those with chronic diseases. Early identification of high-risk groups and targeted prevention are essential for reducing adverse outcomes. This study aimed to analyze the influencing factors of oral frailty in elderly hospitalized patients with chronic diseases and to establish a visual risk prediction model.

A cross-sectional study conducted from August 2024 to January 2025 recruited 502 elderly hospitalized patients with chronic diseases from a tertiary general hospital in Chongqing using convenience sampling. Questionnaires assessed oral frailty and related factors. Multivariate logistic regression identified independent predictors to construct a nomogram. Model performance was evaluated using ROC curves, calibration curves, and the Hosmer–Lemeshow test.

Of 502 patients, 346 had oral frailty (prevalence 68.92%). Multivariate analysis identified age, daily tooth brushing frequency, physical frailty, swallowing disorders, malnutrition, oral health status, and social support as significant risk factors (P < 0.05). The nomogram achieved an AUC of 0.846 (95% CI: 0.812–0.880), with a sensitivity of 0.694 and a specificity of 0.853. The Hosmer–Lemeshow test indicated good model fit (χ2 = 4.201, P = 0.839), and the calibration curve demonstrated excellent agreement between predicted and observed outcomes.

Oral frailty is highly prevalent among elderly hospitalized patients with chronic diseases. The developed prediction model, incorporating key risk factors, shows favorable predictive accuracy and calibration, providing clinicians with a useful tool for early identification and individualized intervention in high-risk populations.