Development and External Validation of a Machine Learning–Based Risk Score for Stent Outcomes in Post–Bariatric Leak Management: The “Alexandria-Bari-Stent” Tool

Abstract

A machine learning model (Alexandria-Bari-Stent) can predict post-MBS stent failure. External validation displayed high accuracy, good sensitivity/specificity, and excellent negative predictive value indicating good discriminative ability. Clinically, the model is more reliable for ruling out stent failure than confirming it, making it especially useful in reassuring low-risk post-MBS leakage patients. Patient's general status, metabolic health, and systemic factors appeared to play a more critical role than previously recognized, complementary to, not in conflict with, established technical and local factors that influence successful stent outcomes for leak management. This prompts the need for a more holistic view of leak patients who are candidates for stenting. Prospective multicenter trials are needed to confirm the performance of the Alexandria‑Bari‑Stent model and the role of metabolic stabilization and medically optimizing the patient for better outcomes.