ArticleBMC geriatrics2025
Development and validation of a dynamic nomogram for predicting cognitive impairment risk in older adults with dentures: analysis from CHARLS and CLHLS data.
Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Investigating the dynamic association of multiple risk factors on cognitive impairment in older people: a retrospective cohort study in Baisha Town, Hepu County, China.BMC geriatrics · 2026Article
- Development and validation of a risk prediction model for mild cognitive impairment in older Chinese adults with chronic diseases.BMC geriatrics · 2026Observational
- Association between residential proximity to major roadways and low muscle mass among Chinese older adults: a nationwide cross-sectional study.BMC public health · 2025Article
- Hippocampal T1WI radiomics- and clinical feature-based models for predicting early mild cognitive impairment in secondary hydrocephalus.Frontiers in aging neuroscience · 2025Article
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12 authors.
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Abstract
BACKGROUND AND
aimsCognitive impairment is a common issue among older adults, with denture use identified as a potential, easily recognizable clinical risk factor. However, the link between denture wear and cognitive decline in older Chinese adults remains understudied. This study aimed to develop and validate a dynamic nomogram to predict the risk of cognitive impairment in community-dwelling older adults who wear dentures.
methodsWe selected 2066 elderly people with dentures from CHARLS2018 data as the development and internal validation group and 3840 people from CLHLS2018 as the external validation group. Develop and treat the concentrated unbalanced data with the synthetic minority oversampling technique, select the best predictors with the LASSO regression ten-fold cross-validation method, analyze the influencing factors of cognitive impairment in the elderly with dentures using Logistic regression, and construct a nomogram. Subject operating characteristic curves, sensitivity, specificity, accuracy, precision, F1 score, calibration curve, and decision curve were used to evaluate the validity of the model in terms of identification, calibration, and clinical validity.
resultsWe identified five factors (age, residence, education, instrumental activities of daily living, 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.
conclusionsFive risk factors, including age, place of residence, education, instrumental activities of daily living, and depression level, were selected as the final nomogram to predict the risk of cognitive impairment in elderly denture wearers. The nomogram has acceptable discrimination and can be used by healthcare professionals and community health workers to plan preventive interventions for cognitive impairment among older denture-wearing populations.
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