Efficacy of a machine learning-based approach in predicting neurological prognosis of cervical spinal cord injury patients following urgent surgery

Abstract

Prediction of the neurological prognosis of cervical spinal cord injury (CSCI) is useful for setting treatment goals. This study aimed to develop a machine learning (ML) model for predicting neurological outcomes of CSCI. We retrospectively analyzed 135 patients with CSCI who underwent surgery within 24 hours after injury. American Spinal Injury Association impairment scales (AIS; grades A to E) were analyzed 6 months after injury as primary outcome measures. A total of 33 features extracted from demographic variables, surgical factors, laboratory variables, neurological status, and radiological findings were analyzed. The multiclass ML model was created using Light GBM, XGBoost, and CatBoost. The accuracy, recall, precision, and F1 score were calculated for each classification model. We evaluated Shapley Additive Explanations (SHAP) values to determine the variables that contributed the most to the prediction models. Of the ML models used, CatBoost had the highest accuracy (0.807), recall (0.574), precision (0.808), and F1 score (0.622). AIS grade at admission, intramedullary hemorrhage, longitudinal extent of intramedullary T2 hyperintensity, total motor index score of the lower extremity, and HbA1c were identified as important features for the prediction model. The ML models successfully predicted neurological outcomes for five grades of AIS 6 months after injury.