Inoue Tomoo, Ichikawa Daisuke, Ueno Taro, Cheong Maxwell, Inoue Takashi, Whetstone William D, Endo Toshiki, Nizuma Kuniyasu, Tominaga Teiji
Department of Neurosurgery, National Health Organization Sendai Medical Center, Sendai, Miyagi, Japan.
SUSMED, Inc., Tokyo, Japan.
Neurotrauma Rep. 2020 Jul 23;1(1):8-16. doi: 10.1089/neur.2020.0009. eCollection 2020.
The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.
由于颈椎脊髓损伤（SCI）患者在特征、治疗策略和影像学表现方面存在异质性，准确预测其神经功能预后具有一定难度。尽管机器学习算法可能会提高各个领域预后预测的准确性，但关于其在SCI治疗中的疗效信息有限。我们分析了165例颈椎SCI患者的数据，并提取了预测预后的重要因素。应用极限梯度提升（XGBoost）作为机器学习模型，与逻辑回归或决策树等传统方法相比，评估机器学习算法预测神经功能预后的可靠性。我们将常规可获取的数据用作预测指标，如人口统计学数据、磁共振变量和治疗策略。将包括XGBoost、逻辑回归和决策树在内的预测工具应用于预测损伤后6个月功能运动状态（美国脊髓损伤协会[ASIA]损伤量表[AIS] D级和E级）的神经功能改善情况。我们评估了预测性能，包括准确性和受试者操作特征曲线下面积（AUC）。在颈椎SCI患者神经功能改善的预测方面，XGBoost的准确性最高（81.1%），其次是逻辑回归（80.6%）和决策树（78.8%）。在AUC方面，逻辑回归为0.877，其次是XGBoost（0.867）和决策树（0.753）。XGBoost能够可靠地预测颈椎SCI患者的神经功能变化。使用预测性机器学习算法可能会通过对患者进行治疗前分类来增强个性化管理选择。
