A Multivariable Model Based on Ultrasound Imaging Features of Gastrocnemius Muscle to Identify Patients With Sarcopenia.

OBJECTIVES: Low skeletal muscle mass, strength, or somatic function are used to diagnose sarcopenia; however, effective assessment methods are still lacking. Therefore, we evaluated the effectiveness of ultrasound in identifying patients with sarcopenia.

METHODS: This study included 167 patients, 78 with sarcopenia and 89 healthy participants, from two hospitals. We evaluated clinical factors and five ultrasound imaging features, of which three ultrasound imaging features were used to create the model. In both the training and validation datasets, the sarcopenia detection performances of chosen ultrasonic characteristics and the constructed model were evaluated using receiver operating characteristic (ROC) curves. The predictive performance was evaluated by area under the ROC (AUROC), calibration, and decision curves.

RESULTS: There were statistically significant differences in muscle thickness (MT) of gastrocnemius medialis muscle (GM), flaky myosteatosis echo (FE), pennation angle (PA), average shear wave velocity (SWV) in the relaxed state (RASWV), and average SWV in the passive stretched state (PASWV) between sarcopenic and normal subjects. PA, RASWV, and PASWV were effective predictors of sarcopenia. The AUROC (95% confidence interval) for these three parameters were 0.930 (0.882-0.978), 0.865 (0.791-0.940), and 0.849 (0.770-0.928), respectively, in the training set, and 0.873 (0.777-0.969), 0.936 (0.878-0.993), and 0.826 (0.716-0.935), respectively, in the validation set. The combined model had better detection power. Finally, the calibration curve showed that the combined model had good prediction accuracy.

CONCLUSION: Our model can be used to identify sarcopenia in primary medical institutions, which is valuable for the early recognition and management of sarcopenia patients.