Volume 24 Issue 4
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LIU Weilong, WANG Xiaoyan, YANG Sijia, ZHANG Xiping. Analysis of factors influencing oral frailty in elderly stroke patients[J]. Chinese Journal of General Practice, 2026, 24(4): 593-595. doi: 10.16766/j.cnki.issn.1674-4152.004447
Citation: LIU Weilong, WANG Xiaoyan, YANG Sijia, ZHANG Xiping. Analysis of factors influencing oral frailty in elderly stroke patients[J]. Chinese Journal of General Practice, 2026, 24(4): 593-595. doi: 10.16766/j.cnki.issn.1674-4152.004447
shu

Analysis of factors influencing oral frailty in elderly stroke patients

doi: 10.16766/j.cnki.issn.1674-4152.004447

  • Department of Emergency, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang 310006, China

Funds:

 2025KY1056

  • Received Date: 2025-12-03
    Abstract
  •   Objective  To identify the risk factors for oral frailty (OF) in elderly stroke patients and to develop a risk prediction model, thereby providing a reference for OF prevention and management.  Methods  The study subjects were elderly stroke patients (n=286) admitted to the First People ' s Hospital of Hangzhou Affiliated to Westlake University from January to October 2025. A general information questionnaire, oral frailty screening scale, micronutrient assessment scale, frailty scale, and the simplified version of the geriatric depression rating scale (GDRS) were used for data collection. Multivariate logistic regression analysis was conducted to identify the risk factors for osteoporosis in elderly stroke patients, followed by the construction of a predictive model. The model ' s goodness of fit and predictive performance were validated using the Homser-Lemeshow test and ROC curve analysis.  Results  Among 286 patients, 179 (62.59%) developed OF. Logistic regression analysis: age (OR=1.607), female sex(OR=1.283), smoking history (OR=1.519), comorbid diabetes (OR=4.022), physical frailty (OR=3.108), malnutrition (OR=4.674), and depression (OR=2.860) were factors influencing OF in elderly stroke patients (P < 0.05). A risk prediction model of OF was constructed, with an area under the ROC curve of 0.916 (95% CI: 0.891-0.943), sensitivity of 0.890, specificity of 0.822. The Homser-Lemeshow goodness-of-fit test indicated good model fit (χ2=5.031, P=0.722).  Conclusion  The incidence of OF in elderly stroke patients is high and is mainly related to factors such as advanced age, smoking history, diabetes, physical weakness, malnutrition, and depression. It is recommended to develop individualized oral management plans to prevent or delay the occurrence of OF.

     

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