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A functional outcome prediction model of acute traumatic spinal cord injury based on extreme gradient boost.
- Source :
- Journal of Orthopaedic Surgery & Research; 10/12/2022, Vol. 17 Issue 1, p1-9, 9p
- Publication Year :
- 2022
-
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 < 0.001). AMS and age played the most important roles in constructing predictive models. There is an obvious correlation between AIS, AMS and BASIC score. 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 proved that the predictive performance of the nonlinear model is better than the traditional linear regression prediction model. Age and baseline AMS play the most important role in predicting the functional outcome. We also found a significant correlation between AIS at admission, baseline AMS and BASIC score. Trial registration: ClinicalTrials.gov identifier: NCT03103516. [ABSTRACT FROM AUTHOR]
- Subjects :
- RESEARCH
SPINAL cord injuries
CONFIDENCE intervals
RESEARCH evaluation
PREDICTIVE tests
FUNCTIONAL status
CHAOS theory
MACHINE learning
REGRESSION analysis
TREATMENT effectiveness
T-test (Statistics)
HUMAN services programs
DESCRIPTIVE statistics
QUESTIONNAIRES
PREDICTION models
DATA analysis software
STATISTICAL correlation
LONGITUDINAL method
EVALUATION
Subjects
Details
- Language :
- English
- ISSN :
- 1749799X
- Volume :
- 17
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Orthopaedic Surgery & Research
- Publication Type :
- Academic Journal
- Accession number :
- 159632151
- Full Text :
- https://doi.org/10.1186/s13018-022-03343-7