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:

Background

There are no prediction models of stent outcomes for leaks after metabolic and bariatric surgery (MBS). The current study developed an artificial intelligence-based model to predict post-MBS stent failure.

Methods

Prospectively maintained database of patients with post-MBS leaks was used for model development (Center I, N = 250); external validation employed patients from another hospital (Center II, N = 150). Outcome definition was failure of the first (primary/initial) stent implantation to resolve the leak, i.e., lack of primary closure. Ranking of variables was performed, 11 machine learning algorithms were tested, the best model was selected, and a stent failure point-based risk scoring system was derived, with further external validation, calibration, and de

SUBMITTER: Hany M 

PROVIDER: S-EPMC12722429 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature