Development and Validation of a Dynamic Nomogram for Predicting Cognitive Impairment Risk in Older Adults with Dentures: Analysis from CHARLS and CLHLS Data

Abstract Background and Aims Cognitive impairment is a common issue among the elderly, with denture use identified as a potential, easily recognizable clinical risk factor. However, the link between denture wear and cognitive decline in elderly Chinese adults remains understudied. This study aimed to develop and validate a dynamic nomogram to predict the risk of cognitive impairment in community-dwelling elderly denture wearers. Methods Participants were divided into development, internal, and external validation sets. The imbalanced data in the development set were first processed using the Synthetic Minority Over-sampling Technique (SMOTE), followed by predictor selection using the Least Absolute Shrinkage and Selection Operator (LASSO). A nomogram was then constructed to dynamically display and present the results. Receiver operating characteristic curve, sensitivity, specificity, accuracy, precision, F1 Score, calibration curve, and decision curve analysis were used to evaluate the validity of the model in terms of identification, calibration, and clinical validity. Results We identified five factors (age, residence, education, IADL, and depression) to construct the nomogram. The area under the curve of the prediction model was 0.854 (95%CI 0.839–0.870) in the development set, 0.841 (95%CI 0.805–0.877) in the internal validation set, and 0.856 (95%CI 0.838–0.873) in the external validation set. Calibration curves indicated significant agreement between predicted and actual values, and decision curve analysis demonstrated valuable clinical application. Conclusions Five factors were chosen as the final for the established nomogram in predicting the risk of cognitive impairment in older denture wearers. The nomogram has acceptable discrimination and can be used in the planning of preventive interventions for cognitive impairment among older denture-wearing populations by healthcare professionals and community health workers.