This study aimed to analyze the influencing factors of oral frailty in older adults with chronic pulpitis, and to construct and validate a predictive model for oral frailty. From July to October 2025, 300 older adults with chronic pulpitis were selected from Nantong Stomatological Hospital using convenient sampling. They were randomly divided into a model training set (n = 210) and a validation set (n = 90) at a ratio of 7:3. Data were collected using a general information questionnaire, the Oral Frailty Index-8 (OFI-8), the Modified Dental Anxiety Scale (MDAS), the Fried Frailty Phenotype (FP), the Numerical Rating Scale (NRS), and the Short Form of Health Literacy Dental Scale (HeLD-14). Logistic regression was used to identify the influencing factors of oral frailty. R software was applied to construct an oral frailty risk prediction model and draw a nomogram. Bootstrap method was used for internal validation, and the predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Hosmer-Lemeshow test. The incidence of oral frailty in older adults with chronic pulpitis was 57.0%. Age (OR=2.368, P<0.001), pain intensity (OR=1.733, P=0.013), number of natural teeth (OR=0.522, P=0.006), course of chronic pulpitis (OR=3.008, P<0.001), frailty (OR=2.106, P=0.036), Dental Anxiety Scale score (OR=1.200, P<0.001), and oral health literacy (OR=0.959, P=0.006) were independent predictive factors for oral frailty. For the training set, the AUC was 0.836 (95%CI: 0.782~0.890) with a cut-off value of 0.531. The sensitivity, and specificity were 79.3%, and 85.0%, respectively. The Hosmer-Lemeshow goodness-of-fit test (χ²=4.198, P=0.521) indicated good model fit. The constructed oral frailty risk prediction model exhibits good discrimination, calibration, and clinical utility. It can provide a reference for the prevention and early screening of oral frailty in older adults with chronic pulpitis. Clinical medical and nursing staff can develop targeted nursing strategies based on the model's prediction results, strengthen comprehensive interventions, promote oral health, and prevent the progression of oral frailty.
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Wang et al. (Tue,) studied this question.
synapsesocial.com/papers/69d895486c1944d70ce06393 — DOI: https://doi.org/10.1186/s12903-026-08254-1
Hao Wang
General Cardiology
Xinyu Ji
Nantong University
Haiou Yan
Nantong University
BMC Oral Health
Nantong University
Affiliated Hospital of Nantong University
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