Background Sarcopenia is associated with various adverse outcomes in hemodialysis patients. However, current tools for assessing and diagnosing sarcopenia have limited applicability. In this study, we aimed to develop a simple and reliable nomogram to predict the risk of sarcopenia in hemodialysis patients that could assist physicians identify high-risk patients early. Methods A total of 615 patients undergoing hemodialysis at the First Affiliated Hospital College of Medicine Zhejiang University between March to June 2021 were included. They were randomly divided into either the development cohort (n = 369) or the validation cohort (n = 246). Multivariable logistic regression analysis was used to screen statistically significant variables for constructing the risk prediction nomogram for Sarcopenia. The line plots were drawn to evaluate the effectiveness of the nomogram in three aspects, namely differentiation, calibration, and clinical net benefit, and were further validated by the Bootstrap method. Results The study finally included five clinical factors to construct the nomogram, including age, C-reactive protein, serum phosphorus, body mass index, and mid-upper arm muscle circumference, and constructed a nomogram. The area under the ROC curve of the line chart model was 0.869, with a sensitivity and specificity of 77% sensitivity and 83%, the Youden index was 0.60, and the internal verification C-statistic was 0.783. Conclusions This study developed and validated a nomogram model to predict the risk of sarcopenia in hemodialysis patients, which can be used for early identification and timely intervention in high-risk groups.

Background Sarcopenia is a progressive and generalized skeletal muscle disorder characterized by accelerated loss of muscle mass and function that is more commonly observed in patients receiving maintenance hemodialysis compared to the general population. This study aimed to explore a simple nomogram to evaluate the risk of developing sarcopenia. Methods From March to June 2021, 615 patients on maintenance hemodialysis were identified at the First Affiliated Hospital College of Medicine Zhejiang University and randomly divided into development cohorts (n=369) and validation cohorts (n=246) in a 6:4 ratio. Multi-factor logistic regression analysis was used to screen out statistically significant variables to construct risk prediction models. The line plots were drawn to evaluate the effectiveness of the predictive models from three aspects: differentiation, calibration, and clinical net benefit, and were further tested by Bootstrap method. Results Our study indicated that 16.6% patients enrolled in our study were diagnosed with sarcopenia. Serum creatinine, serum albumin, C-reactive protein, serum phosphorus, body mass index, and upper arm muscle circumference were identified as independent risk factors for the development of sarcopenia in patients on maintenance hemodialysis. The area under the ROC curve of the line chart model was 0.88 with 90% sensitivity and 75% specificity. The Yoden index was 0.64, and the internal verification C-statistic was 0.864. Conclusions Our study not only proved that sarcopenia was commonly observed in patients on maintenance hemodialysis but also established a prediction nomogram to evaluate the risk for developing sarcopenia in such patients.