Establishment and validation of a prediction model for older people with sarcopenia

The burden of sarcopenia is increasing. However, most cases of sarcopenia are undiagnosed due to the lack of simple screening tools. Here, we aimed to develop and validate an individualized and simple nomogram for predicting sarcopenia in older Chinese people. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia (AWGS) 2019 consensus. The primary data were randomly divided into a train and validation set. Univariate logistic regression analysis was performed to select the risk factors of sarcopenia, which were subjected to the LASSO regression model for feature selection. The nomogram was built using multivariate logistic regression analysis by incorporating the features selected in the LASSO regression model. The discrimination and calibration of the predictive model were verified by the concordance index (C-index), receiver operating characteristic curve (ROC), and calibration curve. In this study, there were 55 cases of sarcopenia. Risk predictors included age, albumin (ALB), blood urea nitrogen (BUN), grip strength, and calf circumference. The model had good discrimination and calibration. C-index was 0.92 (95% confidence interval:0.84–1.00) and the area under the ROC curve (AUC) was 0.92 (95% confidence interval:0.83–1.00) in validation set. The Hosmer-Lemeshow test (HL) had a p-value of 0.94. Our predictive model will be a clinically useful tool for predicting the risk of sarcopenia. It facilitates earlier detection and therapeutic intervention for physicians and patients.