The Predictive Abilities of Machine Learning Algorithms in Patients with Thoracolumbar Spinal Cord Injuries.

OBJECTIVES: The goal of this study is to implement machine learning (ML) algorithms to predict mortality, non-home discharges, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with thoracolumbar spinal cord injury (tlSCI), while creating a publicly accessible online tool.

METHODS: The American College of Surgeons Trauma Quality Program database was utilized to identify patients with tlSCI. Feature selection was performed with the LASSO algorithm. Five ML algorithms, including TabPFN, TabNet, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning.

RESULTS: 147,819 patients were included in the analysis. For each outcome, we determined the best model for deployment in our web application based on the area under the receiver operating characteristic (AUROC) values. The top performing algorithms were as follows: LightGBM for mortality with an AUROC of 0.885, TabPFN for non-home discharges with an AUROC of 0.801, LightGBM for prolonged LOS with an AUROC of 0.673, Random Forest for prolonged ICU-LOS with an AUROC of 0.664, LightGBM for major complications with an AUROC of 0.73.

CONCLUSIONS: ML models demonstrate good predictive ability for in-hospital mortality, and non-home discharges, fair predictive ability for major complications, and prolonged ICU-LOS, but poor predictive ability for prolonged LOS. We have developed a web application that allows these models to be accessed.