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Aim <p>This study aimed to develop a nomogram for screening the risk of pre-frailty in middle-aged and elderly patients on maintenance hemodialysis (MHD) based on cross-sectional data.</p> Design <p>An explorative cross-sectional design was adopted.</p> Methods <p>From August 2024 to December 2024, 181 participants were recruited from a hemodialysis center in Chengdu, Sichuan Province, China, via convenience sampling. The Frailty Screening Inventory (FRAIL) was used to screen subjects for pre-frailty, leading to the formation of two groups: the pre-frailty group and the non-pre-frailty group. Univariate analysis and multivariate binary logistic regression were used to identify predictors. RStudio (version 4.2.1) was subsequently used to construct the regression model and generate the nomogram.</p> Results <p>The prevalence of pre-frailty among middle-aged and elderly MHD patients was 71.27%. Logistic regression analysis revealed that employment status (retirement), weekly exercise frequency (≤ 2 times/week), presence of hyposomnia, high activities of daily living (ADL) score, presence of multiple complications, and low albumin (ALB) levels were independently associated with increased risk of pre-frailty. Furthermore, the <i>Hosmer–Lemeshow</i> goodness-of-fit test yielded a statistic of <i>χ²</i>=1.774 (<i>P</i> = 0.987), and the area under the receiver operating characteristic (ROC) curve was 0.929 (<i>95% CI</i>: 0.892–0.965). At a cut-off value of 0.648, the <i>Youden</i> index reached its maximum of 0.745, with a sensitivity of 0.846 and a specificity of 0.899.</p> Conclusion <p>Pre-frailty is common in middle-aged and elderly MHD patients. In this study, we developed a column chart of six risk factors, which showed good predictive power. The use of this tool facilitates the early identification of patients at high risk for pre-frailty.</p> Impact <p>This study developed a tool for clinical healthcare professionals to predict the probability and risk factors for pre-frailty in middle-aged and elderly MHD patients. The nomogram allows for personalized intervention.</p> Patient or public contribution <p>Data were collected from patients on MHD via a questionnaire survey.</p> Reporting method <p>The TRIPOD checklist. was used.</p> Clinical trial number <p>Not applicable.</p>

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Development and validation of a nomogram for identifying pre-frailty risk in middle-aged and elderly maintenance hemodialysis patients: a cross-sectional study

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  • 发表日期：

    2026年3月27日

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  • Ying Luo,
  • Jie Li,
  • Yue Gao,
  • Huaihong Yuan

摘要

Aim

This study aimed to develop a nomogram for screening the risk of pre-frailty in middle-aged and elderly patients on maintenance hemodialysis (MHD) based on cross-sectional data.

Design

An explorative cross-sectional design was adopted.

Methods

From August 2024 to December 2024, 181 participants were recruited from a hemodialysis center in Chengdu, Sichuan Province, China, via convenience sampling. The Frailty Screening Inventory (FRAIL) was used to screen subjects for pre-frailty, leading to the formation of two groups: the pre-frailty group and the non-pre-frailty group. Univariate analysis and multivariate binary logistic regression were used to identify predictors. RStudio (version 4.2.1) was subsequently used to construct the regression model and generate the nomogram.

Results

The prevalence of pre-frailty among middle-aged and elderly MHD patients was 71.27%. Logistic regression analysis revealed that employment status (retirement), weekly exercise frequency (≤ 2 times/week), presence of hyposomnia, high activities of daily living (ADL) score, presence of multiple complications, and low albumin (ALB) levels were independently associated with increased risk of pre-frailty. Furthermore, the Hosmer–Lemeshow goodness-of-fit test yielded a statistic of χ²=1.774 (P = 0.987), and the area under the receiver operating characteristic (ROC) curve was 0.929 (95% CI: 0.892–0.965). At a cut-off value of 0.648, the Youden index reached its maximum of 0.745, with a sensitivity of 0.846 and a specificity of 0.899.

Conclusion

Pre-frailty is common in middle-aged and elderly MHD patients. In this study, we developed a column chart of six risk factors, which showed good predictive power. The use of this tool facilitates the early identification of patients at high risk for pre-frailty.

Impact

This study developed a tool for clinical healthcare professionals to predict the probability and risk factors for pre-frailty in middle-aged and elderly MHD patients. The nomogram allows for personalized intervention.

Patient or public contribution

Data were collected from patients on MHD via a questionnaire survey.

Reporting method

The TRIPOD checklist. was used.

Clinical trial number

Not applicable.