Scholarlink

Background <p>Frailty is a common clinical syndrome among older patients undergoing maintenance hemodialysis(MHD). This study aims to develop and validate a TFI-based frailty classification model for this population.</p> Methods <p>Data from 510 cases in three hospitals in Fujian Province were collected as the study cohort. Univariate analysis, LASSO regression, and random forest were applied to screen for frailty-related factors. Multivariate logistic regression was then used to develop a predictive model. Based on the model, a web calculator was developed. The model was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).The AUC values were compared with the DeLong test.</p> Results <p>The model variables included albumin, use of walking aids, sleep condition, pain score, and psychological resilience. The model demonstrated good discriminative ability, with AUCs of 0.918 (95% CI = 0.886–0.951) in the training set, 0.916 (95% CI = 0.864–0.968) in the test set, and 0.915 (95% CI = 0.858–0.972) in the validation set. All three models exhibited good calibration capabilities and were clinically applicable.</p> Conclusion <p>The frailty classification model constructed in this study shows good performance in discriminative ability, calibration, and clinical applicability. It enables clinical healthcare workers to accurately identify frailty in older MHD patients, serving as a TFI-based classification tool to support frailty assessment, thereby providing a basis for routine screening.</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 Tilburg Frailty Indicator (TFI)-based frailty classification model for older maintenance hemodialysis patients: a cross-sectional study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • Junwang Liu,
  • Manman Wang,
  • Weiwei Wu

摘要

Background

Frailty is a common clinical syndrome among older patients undergoing maintenance hemodialysis(MHD). This study aims to develop and validate a TFI-based frailty classification model for this population.

Methods

Data from 510 cases in three hospitals in Fujian Province were collected as the study cohort. Univariate analysis, LASSO regression, and random forest were applied to screen for frailty-related factors. Multivariate logistic regression was then used to develop a predictive model. Based on the model, a web calculator was developed. The model was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).The AUC values were compared with the DeLong test.

Results

The model variables included albumin, use of walking aids, sleep condition, pain score, and psychological resilience. The model demonstrated good discriminative ability, with AUCs of 0.918 (95% CI = 0.886–0.951) in the training set, 0.916 (95% CI = 0.864–0.968) in the test set, and 0.915 (95% CI = 0.858–0.972) in the validation set. All three models exhibited good calibration capabilities and were clinically applicable.

Conclusion

The frailty classification model constructed in this study shows good performance in discriminative ability, calibration, and clinical applicability. It enables clinical healthcare workers to accurately identify frailty in older MHD patients, serving as a TFI-based classification tool to support frailty assessment, thereby providing a basis for routine screening.