Risk factors and prediction model for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic...

Sarcopenia is highly prevalent and predicts poor outcomes in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt (TIPS). However, the risk factors for the development of sarcopenia in this population remain unclear. In addition, validated predictive models based on easily accessible clinical variables have not been developed. This study aimed to address these issues in patients with cirrhosis undergoing TIPS. All patients with cirrhosis undergoing TIPS at the Second Affiliated Hospital of Xi'an Jiaotong University from January 2018 to March 2024 were screened. The risk factors for sarcopenia were identified using least absolute shrinkage and selection operator regression and multivariate logistic regression. A new model and nomogram were developed based on the selected variables. The model was evaluated and compared using discrimination, calibration, and decision curve analysis. A total of 202 patients with cirrhosis undergoing TIPS were included in the study. Six independent risk factors for sarcopenia were identified: age; gender; body mass index (BMI); etiology; hepatic encephalopathy (HE); and right psoas muscle thickness (RPMT). A new model and nomogram for predicting sarcopenia were developed based on these risk factors. The model exhibited good overall performance, discrimination, calibration, and clinical utility, offering advantages over existing non-dedicated sarcopenia models. Patients were stratified into high-risk and low-risk groups based on the optimal cutoff value of 0.385, with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for distinguishing sarcopenia of 78.5%, 85.4%, 71.8%, 89.3%, and 83.2%, respectively. In patients with cirrhosis undergoing TIPS, age, male sex, BMI, etiology, HE, and RPMT were identified as independent risk factors for sarcopenia. Based on these risk factors, the newly developed sarcopenia model showed good predictive performance and holds potential as a practical screening tool in clinical settings.