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Background <p>The early identification of sarcopenia, a condition linked to serious functional decline in older adults, is critical for intervention. However, there is currently a lack of accurate and efficient tools for rapid screening and self-screening. The aim of the study was to create a novel, user-friendly prediction tool for rapid community screening of sarcopenia among Chinese older adults, along with an easy-to-operate and self-screening tool for home use.</p> Methods <p>This cross-sectional study was conducted in community settings, and used univariate and multiple logistic regression to select predictors and develop prediction models. A user-friendly nomogram was developed based on the simplified model and translated into a publicly accessible online tool. Model performance was evaluated by the area under the receiver operating curve (AUROC) for discrimination, calibration curve for calibration, and decision curve analysis (DCA) for clinical utility. </p> Results <p>A total of 2,453 individuals aged ≥ 60 years were included and randomly split into training and validation sets (7:3). In the training dataset, optimal model (Model 1) includes six variables: age, sex, BMI, calf circumference (CC), diastolic blood pressure, and sitting duration (mean daily sitting hours on weekdays in the past week) with an AUROC of 0.8720 (95% CI, 0.8510–0.8930). The simplified model (Model 2) retained four predictors (age, sex, BMI, CC) and achieved an AUROC of 0.8688 (95% CI, 0.8476–0.8900). In the validation dataset, the simplified model (Model 2) yielded an AUROC of 0.8465 (95% CI, 0.8102–0.8828). At a threshold probability of 0.111, the sensitivity and specificity were 0.8683 (95% CI, 0.8258–0.9108) and 0.7186 (95% CI, 0.6957–0.7416) for simplified model (Model 2), 0.7941 (95% CI, 0.7156–0.8726) and 0.7062 (95% CI, 0.6707–0.7416) for validation model (Model 3). Calibration was good, and DCA indicated clinical utility.</p> Conclusions <p>The study developed a high-performance prediction tool that serves as a low-cost, user-friendly, and freely accessible online tool for community and self-screening of sarcopenia, facilitating personalized early prevention strategies and optimizing healthcare resource utilization. </p> Clinical trial number <p>Not applicable.</p>

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Development and validation of a screening tool for sarcopenia in community-dwelling older adults: a diagnostic cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Huamei Yan,
  • Yongli Chai,
  • Yujie Zhang,
  • Yunfan Zhan,
  • Jiaqi Rong,
  • Yanyan Song,
  • Weian Yuan

摘要

Background

The early identification of sarcopenia, a condition linked to serious functional decline in older adults, is critical for intervention. However, there is currently a lack of accurate and efficient tools for rapid screening and self-screening. The aim of the study was to create a novel, user-friendly prediction tool for rapid community screening of sarcopenia among Chinese older adults, along with an easy-to-operate and self-screening tool for home use.

Methods

This cross-sectional study was conducted in community settings, and used univariate and multiple logistic regression to select predictors and develop prediction models. A user-friendly nomogram was developed based on the simplified model and translated into a publicly accessible online tool. Model performance was evaluated by the area under the receiver operating curve (AUROC) for discrimination, calibration curve for calibration, and decision curve analysis (DCA) for clinical utility.

Results

A total of 2,453 individuals aged ≥ 60 years were included and randomly split into training and validation sets (7:3). In the training dataset, optimal model (Model 1) includes six variables: age, sex, BMI, calf circumference (CC), diastolic blood pressure, and sitting duration (mean daily sitting hours on weekdays in the past week) with an AUROC of 0.8720 (95% CI, 0.8510–0.8930). The simplified model (Model 2) retained four predictors (age, sex, BMI, CC) and achieved an AUROC of 0.8688 (95% CI, 0.8476–0.8900). In the validation dataset, the simplified model (Model 2) yielded an AUROC of 0.8465 (95% CI, 0.8102–0.8828). At a threshold probability of 0.111, the sensitivity and specificity were 0.8683 (95% CI, 0.8258–0.9108) and 0.7186 (95% CI, 0.6957–0.7416) for simplified model (Model 2), 0.7941 (95% CI, 0.7156–0.8726) and 0.7062 (95% CI, 0.6707–0.7416) for validation model (Model 3). Calibration was good, and DCA indicated clinical utility.

Conclusions

The study developed a high-performance prediction tool that serves as a low-cost, user-friendly, and freely accessible online tool for community and self-screening of sarcopenia, facilitating personalized early prevention strategies and optimizing healthcare resource utilization.

Clinical trial number

Not applicable.