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Background <p>Chronic heart failure combined with sarcopenia is significantly associated with negative health outcomes in elderly patients. Therefore, early identification of sarcopenia risk in elderly patients with heart failure is crucial for their prognosis, however, there is currently no simple and practical predictive model available for clinicians.This study aimed to develop and externally validate a risk prediction model for sarcopenia in elderly patients with heart failure with preserved ejection fraction.</p> Methods <p>A retrospective study design was employed.A cohort of HFpEF patients from the Geriatrics Department of Ruijin Hospital was used as the development cohort (<i>n</i> = 272) for model construction and internal validation.Variables with significant differences in intergroup comparisons were initially screened, followed by variable compression using LASSO regression.A multivariable logistic regression analysis was ultimately performed to establish the prediction model.The discriminative ability and calibration of the model were assessed using ROC curve and calibration curve, respectively.Subsequently, an independent external validation cohort (<i>n</i> = 84) from Nursing Home in Changning District was used to validate the model’s generalizability and clinical utility through ROC curve analysis, calibration curve analysis, and decision curve analysis.</p> Results <p>The final model included five predictors: 1, 25OH-VitD3, BMI, NRS2002 score, handgrip strength, and homocysteine. In the development cohort, the model showed strong discriminative ability, with an AUC of 0.923 (95% CI: 0.892–0.954), and was well-calibrated. External validation confirmed its robust performance, yielding an AUC of 0.937 (95% CI: 0.890–0.984). The calibration curve indicated high agreement between predictions and observations, and decision curve analysis demonstrated a favorable net clinical benefit.</p> Conclusions <p>This study developed and validated the first risk prediction model for sarcopenia tailored to elderly HFpEF patients. The model performed excellently in both internal and external validation, enabling effective identification of high-risk individuals. It offers a practical quantitative tool for early screening and targeted intervention.</p>

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Development and external validation of a sarcopenia risk prediction model in elderly patients with heart failure with preserved ejection fraction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月2日

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  • Qirui Yang,
  • QianWen Jiang,
  • Tingting Bai,
  • Yuanyue Zhu,
  • Yajie Zhao,
  • Gang Xu,
  • Fang Wu,
  • Peijing Cui,
  • Feika Li

摘要

Background

Chronic heart failure combined with sarcopenia is significantly associated with negative health outcomes in elderly patients. Therefore, early identification of sarcopenia risk in elderly patients with heart failure is crucial for their prognosis, however, there is currently no simple and practical predictive model available for clinicians.This study aimed to develop and externally validate a risk prediction model for sarcopenia in elderly patients with heart failure with preserved ejection fraction.

Methods

A retrospective study design was employed.A cohort of HFpEF patients from the Geriatrics Department of Ruijin Hospital was used as the development cohort (n = 272) for model construction and internal validation.Variables with significant differences in intergroup comparisons were initially screened, followed by variable compression using LASSO regression.A multivariable logistic regression analysis was ultimately performed to establish the prediction model.The discriminative ability and calibration of the model were assessed using ROC curve and calibration curve, respectively.Subsequently, an independent external validation cohort (n = 84) from Nursing Home in Changning District was used to validate the model’s generalizability and clinical utility through ROC curve analysis, calibration curve analysis, and decision curve analysis.

Results

The final model included five predictors: 1, 25OH-VitD3, BMI, NRS2002 score, handgrip strength, and homocysteine. In the development cohort, the model showed strong discriminative ability, with an AUC of 0.923 (95% CI: 0.892–0.954), and was well-calibrated. External validation confirmed its robust performance, yielding an AUC of 0.937 (95% CI: 0.890–0.984). The calibration curve indicated high agreement between predictions and observations, and decision curve analysis demonstrated a favorable net clinical benefit.

Conclusions

This study developed and validated the first risk prediction model for sarcopenia tailored to elderly HFpEF patients. The model performed excellently in both internal and external validation, enabling effective identification of high-risk individuals. It offers a practical quantitative tool for early screening and targeted intervention.