Scholarlink

Background <p>Rheumatoid arthritis patients (RA) are associated with frailty. Frailty significantly detrimentally affects patients’ physical and emotional wellness and lowers their standards of life. Establishing a risk stratification model for frailty patients with RA was the goal of this investigation.</p> Methods <p>The study included 283 RA patients from Anhui Province. The risk factors are determined through Minimum Absolute Contraction and Selection Operator (lasso) analysis, random forest, and multivariate logistic regression. The risk stratification model has been developed via multivariate logistic regression. We employed correction curves to assess the nomogram model's accuracy. Area under curve (AUC) and decision curve analysis (DCA) were used to assess predictive performance.</p> Results <p>188 patients (66.4%) with RA had frailty. Physical activity, depression, pain, and disease activity, Pittsburgh sleep quality index, were found to be risk factors of RA patients’ frailty. The internal validation set had an AUC of 0.825 (95% CI 0.730–0.920) and the risk stratification model had an AUC of 0.877 (95% CI 0.842–0.932). The findings of the H–L test indicated that the <i>P</i> ≥ 0.05. The nomogram model and the actual observation findings are in good agreement, as seen by the calibration curve. The nomogram performs well in terms of risk stratification, according to the ROC and DCA values.</p> Conclusion <p>In order to screen high-risk groups for clinical care, the nomogram is a useful and promising method for determining the frailty risk of RA patients in this study.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A risk stratification model for frailty in rheumatoid arthritis patients based on clinical risk factors: a cross-sectional study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月28日

查看全文
  • Rui-Chen Gao,
  • Sha-Sha Guo,
  • Xu-Ming Zhang,
  • Liu-Hong,
  • Wei-Wei Zhang

摘要

Background

Rheumatoid arthritis patients (RA) are associated with frailty. Frailty significantly detrimentally affects patients’ physical and emotional wellness and lowers their standards of life. Establishing a risk stratification model for frailty patients with RA was the goal of this investigation.

Methods

The study included 283 RA patients from Anhui Province. The risk factors are determined through Minimum Absolute Contraction and Selection Operator (lasso) analysis, random forest, and multivariate logistic regression. The risk stratification model has been developed via multivariate logistic regression. We employed correction curves to assess the nomogram model's accuracy. Area under curve (AUC) and decision curve analysis (DCA) were used to assess predictive performance.

Results

188 patients (66.4%) with RA had frailty. Physical activity, depression, pain, and disease activity, Pittsburgh sleep quality index, were found to be risk factors of RA patients’ frailty. The internal validation set had an AUC of 0.825 (95% CI 0.730–0.920) and the risk stratification model had an AUC of 0.877 (95% CI 0.842–0.932). The findings of the H–L test indicated that the P ≥ 0.05. The nomogram model and the actual observation findings are in good agreement, as seen by the calibration curve. The nomogram performs well in terms of risk stratification, according to the ROC and DCA values.

Conclusion

In order to screen high-risk groups for clinical care, the nomogram is a useful and promising method for determining the frailty risk of RA patients in this study.