757 Machine Learning-Driven Web Application for Enhanced Clinical Decision Support in Thoracolumbar Spinal Cord Injuries

Abstract

INTRODUCTION: Thoracolumbar spinal cord injuries (tlSCI) can result in negative short-term in-hospital outcomes, underscoring the importance of precise prediction tools. METHODS: This retrospective cohort study included 147,826 patients from the American College of Surgeons Trauma Quality Program database. XGBoost, LightGBM, CatBoost, and Random Forest algorithms were utilized in combination with the Optuna optimization library for hyperparameter tuning. Models were evaluated graphically with receiver operating characteristic (ROC) curves, precision-recall curves, and numerically with precision, recall, F1 score, accuracy, area under ROC (AUROC), and area under PRC. The best-performing models were integrated into an open-access web application for individualized predictions. RESULTS: The best-performing models for each outcome were as follows: for predicting mortality, Random Forest achieved the highest AUROC (0.865, 95% CI 0.848-0.879); in predicting non-home discharges, LightGBM had the highest AUROC (0.801, 95% CI 0.794-0.804); and for predicting major complications, Random Forest performed best with an AUROC of 0.733 (95% CI 0.642-0.808). Algorithms' receiver operator curves for the outcomes: (A) mortality, (B) non-home discharges, and (C) major complications. CONCLUSIONS: This study successfully developed ML models to predict critical in-hospital outcomes for patients with tlSCI. By incorporating the top-performing algorithms into a user-friendly web application, these models have the potential to improve clinical decision-making and management strategies for tlSCI patients, ultimately enhancing patient care and outcomes.