ArticleFrontiers in public health2026
Enhancing sarcopenia screening in primary care: a machine learning approach using simple physical tests vs. SARC-F in 2,788 community-dwelling older adults.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 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.
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