Development and validation of a nomogram prediction model for oral frailty in community-dwelling older adults

Objective This study aims to investigate the factors influencing oral frailty among community-dwelling older adults and to develop and validate a risk prediction model. Methods A cross-sectional study was conducted between October 2023 and November 2024, enrolling community-dwelling older adults from Zigong City, Sichuan Province, China. Participants were randomly allocated into a training set ( n = 271) and a validation set ( n = 117) in a 7:3 ratio. Variable selection was performed using Lasso regression, followed by multivariate logistic regression to develop a risk prediction model, which was presented as a nomogram. The model’s goodness-of-fit and predictive performance were assessed using the Hosmer–Lemeshow (H-L) test and the receiver operating characteristic (ROC) curve, respectively. Internal validation was further carried out via 1,000 bootstrap resamples. ROC curves, decision curve analysis (DCA), and calibration curves were plotted to comprehensively evaluate the predictive performance of the nomogram. Result The prevalence of oral frailty among community-dwelling older adults was 32.5%. Logistic regression analysis identified several independent risk factors for OF (all p < 0.05), including sex, age, education level, number of chronic diseases, smoking status, number of natural teeth, difficulty chewing hard foods, frailty status, and oral health-related self-efficacy. The prediction model demonstrated excellent performance, with an area under the ROC curve (AUC) of 0.945 (95% CI : 0.919–0.970), a sensitivity of 0.828, and a specificity of 0.913 in the training set. Internal validation using the validation set yielded an AUC of 0.910 (95% CI : 0.857–0.962), a sensitivity of 0.923, and a specificity of 0.808, indicating robust model performance. Conclusion The occurrence of oral frailty among community-dwelling older adults is influenced by multiple factors including sex, age, difficulty chewing hard foods, and oral health-related self-efficacy. The risk prediction model constructed based on these factors demonstrates favorable discrimination and calibration, with its predictive performance being adequately validated. This model can serve as an evidence-based practical tool to support early screening, prevention, and the formulation of individualized intervention strategies for oral frailty in community settings.