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Enhancing sarcopenia screening in primary care: a machine learning approach using simple physical tests vs. SARC-F in
Zhizhi Jiang1, Changyang Zhong2, Xiaoyu Yin2
1Hangzhou Shangcheng District Xiaoying Street Community Health Service Center, Hangzhou, China.
Background:
SARC-F, a widely used screening tool for sarcopenia, offers high specificity but poor sensitivity (30-50%), leading to substantial missed diagnoses in community settings.
Objectives:
To develop and validate a machine learning model using simple physical function tests to screen for sarcopenia in community-dwelling older adults, and to compare its performance against SARC-F.
Design:
Cross-sectional study.
Setting And Participants:
Data were collected from 2,788 older adults (≥60 years; 64.8% female) across 45 community health centers in Hangzhou, China (August-September 2025). Confirmed sarcopenia prevalence was 18.2% (AWGS 2019 criteria).
Methods:
Predictors included grip strength, five-repetition sit-to-stand (5STS) time, static balance, and reaction time (total <5 min). XGBoost, Random Forest, and Logistic Regression models were developed and evaluated on a temporally independent test set (training n = 2,024; test n = 764). Model performance was assessed using AUC, sensitivity, and specificity. SHAP analysis provided interpretability.
Results:
The XGBoost model achieved superior performance (AUC = 0.92; 95% CI: 0.90-0.94), with sensitivity of 86.5% and specificity of 85.1%-nearly 2.5 times the sensitivity of SARC-F (34.8%). 5STS time emerged as the strongest predictor (mean |SHAP| = 0.21). A 12-s 5STS threshold (exploratory, 95% CI: 11-13 s) was identified using Youden's index and SHAP analysis, warranting prospective validation. Decision curve analysis demonstrated positive net benefit across 10-60% thresholds, with net benefit 0.12 at 20%, equivalent to 12 additional true cases identified per 100 screened individuals without increasing unnecessary referrals.
Conclusions And Implications:
This internally validated machine learning model shows promise for sarcopenia screening using brief, low-cost functional tests. Its superior sensitivity, an exploratory 12-s 5STS threshold, and an estimated 80-90% reduction in per-capita screening costs (based on equipment cost comparison) suggest potential utility in primary care, but external validation is required before widespread deployment. Implications for practice: Community health workers can deploy this tool to enable early identification and timely nutrition and exercise interventions. Implications for policy: Integration into existing community health programs may reduce long-term care burden by delaying functional decline in aging societies.
