A simple predictive model for sarcopenia based on the calf muscle maximum circumference

Background : The correlation between calf circumference and sarcopenia has been demonstrated, but the correlation between the calf muscle maximum circumference and sarcopenia has not been studied. This study aimed to explore the relationship between the two and to construct a simple model to predict sarcopenia in hospitalized elderly patients. Methods: This was a retrospective controlled study of patients >60 years of age hospitalized in the geriatric department of Hunan Provincial People's Hospital. The patients were thoroughly evaluated by questionnaires, laboratory, and ultrasound examinations, including measuring muscle thickness and calf muscle maximum circumference using ultrasound. Patients were categorized into sarcopenia and non-sarcopenia groups according to the consensus for diagnosis of sarcopenia recommended by the Asian Working Group on Sarcopenia 2019 (AWGS2). Independent predictors of sarcopenia were identified by univariate and multivariate logistic regression analyses, and a predictive model was developed and simplified. The prediction performance of the models was assessed using sensitivity, specificity, and area under the curve (AUC) and compared with independent predictors. Results: We found that patient age, albumin level(ALB), brachioradialis muscle thickness (BRMT), gastrocnemius lateral head muscle thickness(Glh MT), and calf muscle maximal circumference(CMMC) were independent predictors of sarcopenia in hospitalized elderly patients. A predictive model was developed and simplified as Logistic P = - 4.5 + 1.4 × Age + 1.3 × ALB + 1.6 × BR MT + 3.7 × CMMC + 1.8 × Glh MT, and the diagnostic optimal cutoff value of the equation was 0.485. The sensitivity, specificity, and AUC of the model were 0.884(0.807-0.962), 0.837(0.762-0.911), and 0.927(0.890-0.963), respectively, which were significantly higher than those of the independent predictors. Conclusion : We constructed a simple predictive model for sarcopenia including five variables: age, ABL level, BR MT, Glh MT, and CMMC. The AUC of the model is 0.927, which can help clinicians predict less muscle disease in patients with senile inpatients quickly.