Clinical utility of the AITIS model for test-free identification of sarcopenia in patients with stage IV-V non-dialysis-dependent chronic kidney disease.

Background: Identifying sarcopenia in resource-limited settings presents a significant challenge. The objective of this study was to validate the clinical usefulness of the Artificial Intelligence to Identify Sarcopenia (AITIS) model, a test-free artificial intelligence model we previously proposed for identifying sarcopenia, in patients with chronic kidney disease (CKD). Methods: This observational cross-sectional study enrolled 236 patients with stage IV-V CKD. Sarcopenia was diagnosed using the Asian Working Group for Sarcopenia 2019 criteria, which are based on handgrip strength, physical performance, and appendicular skeletal muscle mass measured by bioelectrical impedance analysis. Patient data, including age, sex, height, weight, and 20 functional measures, were used as predictors. The AITIS model was applied to predict sarcopenia, and its performance, explainability, and clinical usefulness were comprehensively analyzed. Results: The study included 129 men and 107 women (median age = 54.5 years). Sarcopenia was diagnosed in 62 patients (26.3%). The three most common functional limitations reported were jogging 1 km ( Conclusion: The AITIS model demonstrates strong generalizability and performance in predicting sarcopenia in patients with stage IV-V CKD. These findings may enhance clinical decision-making and facilitate the development of novel strategies for managing sarcopenia in CKD patients.