Efficacy of a machine learning-based approach in predicting neurological prognosis of cervical spinal cord injury patients following urgent surgery within 24 h after injury. (January 2023)

• Record Type: Journal Article
• Authors: Shimizu, Tomoaki; Suda, Kota; Maki, Satoshi; Koda, Masao; Matsumoto Harmon, Satoko; Komatsu, Miki; Ota, Masahiro; Ushirozako, Hiroki; Minami, Akio; Takahata, Masahiko; Iwasaki, Norimasa; Takahashi, Hiroshi; Yamazaki, Masashi
• Is Part Of: Journal of clinical neuroscience. Volume 107(2023)
• Page Start: 150
• Page End: 156
• Publication Date: 2023-01
• Subjects: Cervical spinal cord injury -- Machine learning -- Multiclass classification -- Prediction models -- Neurological outcomes -- Urgent surgery; Brain -- Surgery -- Periodicals; Neurosciences -- Periodicals; Nervous system -- Surgery -- Periodicals; Brain -- surgery -- Periodicals; Neurosurgical Procedures -- Periodicals; Neurosciences -- Periodicals; Electronic journals; 616.8
• DOI: 10.1016/j.jocn.2022.11.003
• Languages: English
• ISSNs: 0967-5868

Abstract

Highlights: The ML models successfully predicted neurological outcomes of spinal cord injury. AIS grade at admission was identified as the most important feature for the model. Preoperative MRI findings were also identified as important features for the model. Abstract: We aimed to develop a machine learning (ML) model for predicting the neurological outcomes of cervical spinal cord injury (CSCI). We retrospectively analyzed 135 patients with CSCI who underwent surgery within 24 h after injury. Patients were assessed with the American Spinal Injury Association Impairment Scale (AIS; grades A to E) 6 months after injury. A total of 34 features extracted from demographic variables, surgical factors, laboratory variables, neurological status, and radiological findings were analyzed. The ML model was created using Light GBM, XGBoost, and CatBoost. We evaluated Shapley Additive Explanations (SHAP) values to determine the variables that contributed most to the prediction models. We constructed multiclass prediction models for the five AIS grades and binary classification models to predict more than one-grade improvement in AIS 6 months after injury. Of the ML models used, CatBoost showed the highest accuracy (0.800) for the prediction of AIS grade and the highest AUC (0.90) for predicting improvement in AIS. AIS grade at admission, intramedullary hemorrhage, longitudinal extent of intramedullary T2 hyperintensity, and HbA1c were identified as important features for these prediction models. The ML models successfully predicted neurological outcomes 6 months after injury following urgent surgery in patients with CSCI.