GLIM Achieves Best Diagnostic Performance in Non-Cancer Patients with Low BMI: A Hierarchical Bayesian Latent-Class Meta-Analysis.

This article focuses on a meta-analysis comparing the diagnostic accuracy of two malnutrition assessment tools: the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA). Using a hierarchical Bayesian latent class model to address the absence of a gold standard, the study analyzed data from 45 studies involving over 20,000 individuals for GLIM and 11,000 for PG-SGA. Results showed that both tools have moderately high diagnostic performance, with GLIM demonstrating higher specificity and diagnostic odds ratio, particularly effective in non-cancer patients with low body mass index (BMI) or older age, while PG-SGA showed higher sensitivity and better performance in younger cancer patients. These findings suggest that GLIM may be more suitable for non-cancer populations, whereas PG-SGA is preferable for cancer patients, providing guidance for clinicians in personalized nutritional assessment and management.