Prediction models for sarcopenia in older adults in China: a scoping review

This scoping review synthesized research on sarcopenia prediction models for older adults in China to identify key limitations constraining their clinical applicability.

Adhered to the Arksey and O'Malley framework and the PRISMA-ScR guidelines for this scoping review. Sarcopenia prediction models were systematically retrieved from PubMed, Embase, Web of Science, CNKI, and Wanfang, from inception to December 31, 2024. Two reviewers independently screened the literature and extracted data. Eligible studies were narratively synthesized.

This review identified 20 articles encompassing 34 prediction models. The reported prevalence of sarcopenia across studies ranged from 12 to 54.17%. Logistic regression and machine learning were the predominant modeling techniques. The number of predictor variables per model ranged from 3 to 8. The most frequently included predictors were age (n = 24), BMI (n = 23), and sex (n = 15). The models demonstrated acceptable discriminative ability, with AUC values ranged from 0.706 to 0.974. Sensitivity ranged from 0.405 to 0.963, whereas specificity ranged from 0.400 to 0.947.

Despite the rapid growth of sarcopenia prediction models in recent years, this review reveals persistent deficiencies in variable selection, methodological rigor, and external validation, which collectively limit their clinical applicability. Addressing these issues is essential for developing predictive tools that are statistically robust, clinically applicable, and tailored to China's aging population.