What question did this study set out to answer?

This study aims to identify factors influencing oral frailty and create a predictive model for hypertensive patients in rural areas.

February 8, 2026Frontiers in Public Health0 citationsOpen Access

Construction and validation of a risk prediction model for oral frailty in rural hypertensive patients

NFNina FengCentral South University YTYanzhen TianCentral South University SZShiji ZhangHarbin University of Science and Technology

Key Points

This study aims to identify factors influencing oral frailty and create a predictive model for hypertensive patients in rural areas.
Conducted a survey of 538 older adults with hypertension using questionnaires and scales.
Divided participants into a modeling group (377) and an external validation group (161).
Applied logistic regression to examine influencing factors for oral frailty.
Used Bootstrap sampling for internal validation and ROC curves for evaluation.
61.5% incidence of oral frailty observed in the population.
Logistic regression identified key factors: age, education, living alone, polypharmacy, smoking, dysphagia, xerostomia, and comorbidities.
Model had an area under the curve of 0.781, indicating good predictive ability for oral frailty.

Abstract

Objective To explore the influencing factors of oral frailty in rural hypertensive patients in Zhuzhou City, Hunan Province, and construct a risk prediction model. Methods A randomized sampling method was employed to investigate 538 older adults hypertensive patients in a rural area of Zhuzhou City, Hunan Province, using general data questionnaires, the Oral Hypertension Index-8 (OHI-8), and the EAT-10 scale. Among them, 377 cases were assigned to the modeling group, with internal validation conducted through 1,000 repeated Bootstrap sampling. An independent external validation group of 161 cases was also established. Results The incidence of oral frailty in rural older adults hypertensive patients was 61.5%. Logistic regression analysis showed that age, education level, living alone, polypharmacy, smoking, dysphagia, xerostomia, and co-morbidities were influencing factors for oral frailty in rural older adults hypertensive patients ( P 0.05). The modeling results showed an area under the curve of 0.781 (95% CI : 0.735–0.827). The Hosmer-Lemeshow goodness-of-fit test showed χ 2 = 13.736, p = 0.089. The areas under the ROC curves for internal and external validation were 0.769 (95% CI : 0.754–0.779) and 0.810 (95% CI : 0.745–0.875), respectively, and the p values for the Hosmer-Lemeshow test were 0.089 and 1.133, respectively. The calibration plot showed a high degree of overlap between the predicted curve and the ideal curve, and the DCA plot indicated good applicability of the model in clinical decision-making. Conclusion The incidence of oral frailty is high in rural older adults hypertensive patients. Its influencing factors include age, education, living alone, polypharmacy, smoking, dysphagia, xerostomia, and comorbidities. The oral frailty risk prediction model constructed based on these influencing factors has high predictive and discriminative power and can be used as a screening tool for the risk of oral frailty in rural older adults hypertensive patients.

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