Diagnostic Prediction Models for Sarcopenia: A Systematic Review and Meta-Analysis

Early detection and diagnosis of sarcopenia remain challenging. Despite significant progress in predictive modeling, there is no comprehensive evaluation of their diagnostic performance and methodologic quality across different modeling approaches and populations. This study aims to systematically evaluate the diagnostic accuracy of prediction models for sarcopenia across different modeling approaches and reference standards.

Systematic review and meta-analysis of diagnostic test accuracy studies.

Both men and women at any age and ethnicity with sarcopenia, regardless of comorbidities.

We systematically searched Ovid MEDLINE, Embase, and Cochrane Central databases until June 2024. Studies developing or validating prediction models for sarcopenia diagnosis were included. We performed a bivariate random-effects meta-analysis and used hierarchical summary receiver operating characteristic models to synthesize diagnostic accuracy data.

Thirteen studies comprising 122,252 participants were included. The prediction models demonstrated robust overall performance in development sets [sensitivity, 82%; 95% CI, 75%-87%; specificity, 84%; 95% CI, 74%-90%; area under curve (AUC), 0.89; 95% CI, 0.86-0.91) and internal validation sets (AUC, 0.86; 95% CI, 0.83-0.89]. In validation sets, traditional statistical models maintained consistent performance (sensitivity, 86%; 95% CI, 80%-91%; specificity, 72%; 95% CI, 67%-77%), whereas machine learning approaches achieved higher specificity (84%; 95% CI, 71%-91%) despite moderate sensitivity (70%; 95% CI, 56%-81%). Only one study conducted external validation, reporting moderate sensitivity (71%; 95% CI, 62%-78%) and excellent specificity (98%; 95% CI, 96%-99%) with an AUC of 0.97.

Current prediction models show promising diagnostic accuracy for sarcopenia, with different modeling approaches having complementary strengths. However, further research is needed to address the limitations of existing models, including methodologic heterogeneity and limited external validation, before clinical implementation can be recommended.