We prospectively enrolled 249 patients with acute SCI from 5 primary orthopedic centers. Based on 6 predictors with three aspects (age, AIS at admission, baseline AMS, level of injury, BASIC score and surgical timing), we successfully constructed a nonlinear regression prediction model through XGBoost and verified the credibility.
Acute SCI has always been the focus of clinicians due to its high incidence and high disability rate. Much progress has been made in understanding the injury mechanism, clinical features, MR images, and treatment options. Early prediction of the functional prognosis of patients is conducive to guiding follow-up treatment and giving the patients and their families a realistic idea of long-term expectations. Wilson and Kaminski et al. constructed linear regression models based on similar clinical features and MR images in 2012 and 2017, respectively. However, from the current point of view, their research has several small problems: 1) Wilson’s study is a retrospective study, and some key data is missing; 2) the validity of the linear regression prediction model is limited; and 3) the influence of the treatment plan is not considered. Many clinical trials have shown that methylprednisolone [2,6] and early surgery [1,4,22,28] improve the prognosis of patients, and these measures have been included in the guidelines. As the most advanced technology in machine learning at present , XGBoost has been widely used in various fields, such as industry, commerce and environmental protection, to construct nonlinear regression models. For the above reasons, we used XGBoost technology to incorporate representative data into the analysis to construct a nonlinear regression prediction model for the functional outcome of patients with acute SCI.
In the process of constructing the predictive model, we counted the importance of each feature’s value in predicting the patient’s functional outcome. AMS has been found to play the most important role in predicting the functional outcome of patients, while AIS is relatively less important. In previous studies, both AMS and AIS were considered to be related to the improvement of patients’ neurological function [11,27]. We believe that there are broad differences in AMS in the same AIS grade. AIS is a hierarchical grading index, while AMS refers to the accumulation of key muscle group strength grading, which is a continuous variable . The SCIM score is composed of 19 items with three main domains: self-care, respiration and sphincter management and mobility . The realization of each function is closely related to the strength of key muscle groups, so AMS plays a more critical role in predicting functional outcome. This can also verify that AIS alone is less effective in judging the functional outcome of patients [10,29].
We found that age also played an important role in predicting functional outcome. Age has always been considered to be significantly related to the improvement of patients’ neurological function [17,18,26]. In Wilson’s prediction model, age was also included in the analysis as an important predictor. However, the surgical timing ranks last in importance, which suggests that the surgical timing may have a relatively low impact on the functional prognosis of patients with SCI. This shows that the functional recovery after SCI is more closely related to the severity of the injury and the age of the patient, while the timing of surgery can only have a small impact. This research conclusion does not mean that early surgery is not beneficial to the improvement of patients' neurological function and does not conflict with previous clinical studies.
Through correlation analysis, we found that there was a significant correlation between AIS grade, BASIC score and AMS. When patients have combined injuries, such as combined fractures, pain, and brain injuries, it is very important to assess the severity of the patient's SCI through MR. The BASIC score was the first axial MR grading system proposed by Talbott in 2015. Multiple clinical studies have proven that the BASIC score has a significant correlation with the severity of SCI and predicting functional improvement[7,15]. Our research also further supports the view that the higher the AIS grade is, the lower the severity of SCI is, the higher the AMS is, and the lower the BASIC score is.
1) The sample size data were insufficient. This is one of the largest prospective studies about constructing a model for predicting the functional outcome of acute SCI, but for machine learning, the sample size should be as large as possible. 2) The validation set data were collected retrospectively, while the model we built was based on a prospective study. 3) The constructed prediction model can only be stored in the form of an algorithm, which limits its promotion and extensive verification. 4) A small proportion of the clinical data were not collected within 72 hours.