Supplementary Material for: Modeling Low Muscle Mass Screening in Hemodialysis Patients

<b><i>Introduction:</i></b> Computed tomography (CT) can accurately measure muscle mass, which is necessary for diagnosing sarcopenia, even in dialysis patients. However, CT-based screening for such patients is challenging, especially considering the availability of equipment within dialysis facilities. We therefore aimed to develop a bedside prediction model for low muscle mass, defined by the psoas muscle mass index (PMI) from CT measurement. <b><i>Methods:</i></b> Hemodialysis patients (<i>n</i> = 619) who had undergone abdominal CT screening were divided into the development (<i>n</i> = 441) and validation (<i>n</i> = 178) groups. PMI was manually measured using abdominal CT images to diagnose low muscle mass by two independent investigators. The development group’s data were used to create a logistic regression model using 42 items extracted from clinical information as predictive variables; variables were selected using the stepwise method. External validity was examined using the validation group’s data, and the area under the curve (AUC), sensitivity, and specificity were calculated. <b><i>Results:</i></b> Of all subjects, 226 (37%) were diagnosed with low muscle mass using PMI. A predictive model for low muscle mass was calculated using ten variables: each grip strength, sex, height, dry weight, primary cause of end-stage renal disease, diastolic blood pressure at start of session, pre-dialysis potassium and albumin level, and dialysis water removal in a session. The development group’s adjusted AUC, sensitivity, and specificity were 0.81, 60%, and 87%, respectively. The validation group’s adjusted AUC, sensitivity, and specificity were 0.73, 64%, and 82%, respectively. <b><i>Discussion/Conclusion:</i></b> Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions.