Prediction models for sarcopenia in older adults in China: a scoping review
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By
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Kanfei Yao
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Yihong Xu
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Jia Xu
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Xiaojie Zhang
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Fanglei Gu
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Lijiangshan Hua
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Xiuping Li
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May 28, 2026
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Clinical Scorecard: Review of Sarcopenia Prediction Models for Elderly Populations in China: A Scoping Analysis
At a Glance
| Category | Detail |
|---|
| Condition | Sarcopenia |
| Key Mechanisms | Progressive loss of muscle mass, strength, and physical performance. |
| Target Population | Older adults (≥60 years) in China. |
| Care Setting | Primary care, community-based practice, and resource-limited settings. |
Key Highlights
- Identified 20 articles encompassing 34 prediction models.
- Prevalence of sarcopenia ranged from 12% to 54.17%.
- Logistic regression and machine learning were predominant modeling techniques.
- Models demonstrated acceptable discriminative ability with AUC values from 0.706 to 0.974.
- Common predictors included age, BMI, and sex.
Guideline-Based Recommendations
Diagnosis
- Use of screening tools like SARC-F questionnaire and Ishii’s score for early identification.
Management
- Consider predictive models based on routinely available clinical variables for risk stratification.
Monitoring & Follow-up
- Regular assessment of muscle mass and function using reliable diagnostic methods.
Risks
- Increased risk of fractures, falls, disability, and mortality associated with sarcopenia.
Patient & Prescribing Data
Older adults (≥60 years) in China.
Early identification of sarcopenia is challenging due to insidious progression.
Clinical Best Practices
- Address methodological rigor and variable selection in prediction models.
- Ensure external validation of prediction models for clinical applicability.
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