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Objectives <p>This study aims to identify the potential categories of oral symptoms in stroke patients and to explore their associated influencing factors.</p> Methods <p>A convenience sampling method was employed to collect demographic and oral symptom data from hospitalized stroke patients admitted to the neurology and neurosurgery departments of a tertiary hospital in Guiyang, Guizhou Province, between January 2025 and March 2025. Latent class analysis was applied to classify oral symptoms, and univariate analysis along with multivariate logistic regression was conducted to identify factors associated with each class.</p> Results <p>Three distinct classes of oral symptoms were identified: Structural and Masticatory Dysfunction (21.8%), Oral Mucosal Lesions (34.6%), and Poor Oral Hygiene (43.6%). The multivariate logistic regression analysis revealed that the influencing factors varied across different oral symptom classes (<i>P</i> &lt; 0.05).</p> Conclusion <p>The findings indicate substantial heterogeneity in oral symptoms among stroke patients. Tailored oral care strategies should be developed based on the characteristics and determinants of each class to improve oral health outcomes and overall quality of life in this population.</p>

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Latent class analysis of oral symptoms in stroke patients and exploration of associated

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Huanhuan Zhu,
  • Fengyin Zhang,
  • Simin Li,
  • Qinghua Wen,
  • Xiaoyue Wang,
  • Lihong Xian,
  • Juan Li

摘要

Objectives

This study aims to identify the potential categories of oral symptoms in stroke patients and to explore their associated influencing factors.

Methods

A convenience sampling method was employed to collect demographic and oral symptom data from hospitalized stroke patients admitted to the neurology and neurosurgery departments of a tertiary hospital in Guiyang, Guizhou Province, between January 2025 and March 2025. Latent class analysis was applied to classify oral symptoms, and univariate analysis along with multivariate logistic regression was conducted to identify factors associated with each class.

Results

Three distinct classes of oral symptoms were identified: Structural and Masticatory Dysfunction (21.8%), Oral Mucosal Lesions (34.6%), and Poor Oral Hygiene (43.6%). The multivariate logistic regression analysis revealed that the influencing factors varied across different oral symptom classes (P < 0.05).

Conclusion

The findings indicate substantial heterogeneity in oral symptoms among stroke patients. Tailored oral care strategies should be developed based on the characteristics and determinants of each class to improve oral health outcomes and overall quality of life in this population.