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<p>Handgrip strength is a key indicator of overall health in older adults, and its decline is linked to various adverse health outcomes. Despite numerous studies on factors influencing handgrip strength, few attempts have integrated multiple factors into a practical clinical tool. This study aims to develop and validate a nomogram based on a logistic regression model to predict the risk of low handgrip strength in older adults. Using data from the China Health and Retirement Longitudinal Study (CHARLS), 1138 participants were included. Firth-adjusted logistic regression identified predictors of low handgrip strength, with variable selection based on the Bayesian Information Criterion (BIC). Model performance was assessed using calibration curves, ROC curves, and decision curve analysis (DCA). Internal validation was performed with 10-fold cross-validation and bootstrapping, determining the optimal risk threshold. Key predictors identified included age, chronic disease history, marital status, lifestyle, education, BMI, activities of daily living, and glycated hemoglobin. The simplified model exhibited good discriminatory ability (AUC = 0.78) and calibration performance. The optimal threshold (0.40) yielded sensitivity of 72.5% and specificity of 69.8%. Decision curve analysis confirmed significant net benefit within the clinically relevant threshold range. The nomogram provides a practical tool for identifying at-risk individuals and guiding intervention, integrating modifiable and non-modifiable factors for personalized risk assessment and early intervention.</p>

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A grip strength prediction tool for older adults based on logistic regression: construction, validation, and clinical application value

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月1日

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  • Tongyang Wu,
  • Bowen Li

摘要

Handgrip strength is a key indicator of overall health in older adults, and its decline is linked to various adverse health outcomes. Despite numerous studies on factors influencing handgrip strength, few attempts have integrated multiple factors into a practical clinical tool. This study aims to develop and validate a nomogram based on a logistic regression model to predict the risk of low handgrip strength in older adults. Using data from the China Health and Retirement Longitudinal Study (CHARLS), 1138 participants were included. Firth-adjusted logistic regression identified predictors of low handgrip strength, with variable selection based on the Bayesian Information Criterion (BIC). Model performance was assessed using calibration curves, ROC curves, and decision curve analysis (DCA). Internal validation was performed with 10-fold cross-validation and bootstrapping, determining the optimal risk threshold. Key predictors identified included age, chronic disease history, marital status, lifestyle, education, BMI, activities of daily living, and glycated hemoglobin. The simplified model exhibited good discriminatory ability (AUC = 0.78) and calibration performance. The optimal threshold (0.40) yielded sensitivity of 72.5% and specificity of 69.8%. Decision curve analysis confirmed significant net benefit within the clinically relevant threshold range. The nomogram provides a practical tool for identifying at-risk individuals and guiding intervention, integrating modifiable and non-modifiable factors for personalized risk assessment and early intervention.