A Functional Outcome Prediction Model of Acute Traumatic Spinal Cord Injury Based on Extreme Gradient Boost

Abstract

Purpose: We aimed to construct a nonlinear regression model through Extreme Gradient Boost (XGBoost) to predict functional outcome 1 year after surgical decompression for patients with acute spinal cord injury (SCI).Methods: We prospectively enrolled 249 patients with acute SCI from 5 primary orthopedic centers from June 1, 2016, to June 1, 2020. We identified a total of 6 predictors with three aspects: 1) clinical characteristics, including age, American Spinal Injury Association (ASIA) Impairment Scale (AIS) at admission, level of injury and baseline ASIA motor score (AMS); 2) MR imaging, mainly including Brain and Spinal Injury Center (BASIC) score; 3) surgical timing, specifically comparing whether surgical decompression was received within 24 hours or not. We assessed the SCIM score at 1 year after the operation as the functional outcome index. XGBoost was used to build a nonlinear regression prediction model through the method of boosting integrated learning.Results: We successfully constructed a nonlinear regression prediction model through XGBoost and verified the credibility. The average absolute value of the difference between the predicted value and the actual value is 3.72 (t=1.29, P=0.203), ranging from 0 to 8.44. AMS and age ranked first and second in predicting the functional outcome.Conclusion: We verified the feasibility of using XGBoost to construct a nonlinear regression prediction model for the functional outcome of patients with acute SCI, and we found that age and AMS play the most important role in predicting the functional outcome.Trial registration: ClinicalTrials.gov identifier: NCT03103516.