Development and validation of nomogram and machine learning models to predict sarcopenia in patients with chronic kidney disease.

ABSTRACT: Chronic kidney disease (CKD) is a growing public health problem worldwide. CKD not only leads to renal function decline but also increases the risk of multiple complications, sarcopenia being particularly common and severe. At present, there is a lack of prediction tools specifically for the risk of sarcopenia in CKD patients. Based on the China Health and Retirement Longitudinal Study (CHARLS) data, this study aims to establish a prediction model (Nomogram) for the risk of sarcopenia in Chinese CKD patients to identify high-risk patients early and implement interventions. This study was based on the CHARLS 2015 data, which included 731 CKD patients. After oversampling, the total sample size increased to 1,000, with 70% allocated to the training set and 30% to the internal validation set.

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12660319 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature