A functional outcome prediction model of acute traumatic spinal cord injury based on extreme gradient boost

Autor: Zhan Sizheng, Huang Boxuan, Xue Feng, Zhang Dianying
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of Orthopaedic Surgery and Research, Vol 17, Iss 1, Pp 1-9 (2022)
Druh dokumentu: article
ISSN: 1749-799X
DOI: 10.1186/s13018-022-03343-7
Popis: Abstract Objective 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) and explored the importance of predictors in predicting the functional outcome. 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 h 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. There is no significant difference between actual SCIM and nonlinear prediction model (t = 0.86, P = 0.394; Mean ± SD: 3.31 ± 2.8). The nonlinear model is superior to the traditional linear model (t = 6.57, P
Databáze: Directory of Open Access Journals
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