Comparative Analysis of Islet Auto-Transplantation Outcome Classification Systems: Evaluating Concordance, Feasibility, and a Data-Driven Approach.

A standardized approach to assessing islet autotransplantation outcomes is crucial for evaluating graft function and guiding clinical decisions. This study compares the performance of existing classification systems-Milan, Minneapolis, Chicago, Leicester, Igls, and a novel Data-Driven approach-by evaluating their ability to differentiate transplant outcomes using metabolic and insulin secretion parameters. Our analysis shows strong concordance among Milan, Minneapolis, Chicago, and Igls, primarily due to minor variations in C-peptide thresholds. The Leicester and Data-Driven systems, however, exhibit greater divergence, with the Leicester system simplifying assessment by excluding severe hypoglycemic events and HbA1c, and the Data-Driven approach offering a more dynamic framework without predefined thresholds. Fasting C-peptide levels emerged as a highly reliable predictor of graft function, with the arginine test proving more effective than Mixed Meal Tolerance Test for additional evaluation. The Data-Driven approach provided superior stratification of outcomes, highlighting the importance of residual insulin secretion in metabolic control. These findings suggest that refining classification systems, particularly by considering insulin sensitivity and residual secretion, could enhance long-term patient monitoring and improve our understanding of beta-cell replacement therapies. Further validation across diverse cohorts is essential for broader clinical adoption.