Abstract：
OBJECTIVE: Neurological and functional recovery after traumatic spinal cord injury (SCI) is highly challenged by the level of the lesion and the high heterogeneity in severity (different degrees of in/complete SCI) and spinal cord syndromes (hemi-, ant-, central-, and posterior cord). So far outcome predictions in clinical trials are limited in targeting sum motor scores of the upper (UEMS) and lower limb (LEMS) while neglecting that the distribution of motor function is essential for functional outcomes. The development of data-driven prediction models of detailed segmental motor recovery for all spinal segments from the level of lesion towards the lowest motor segments will improve the design of rehabilitation programs and the sensitivity of clinical trials.
METHODS: This study used acute-phase International Standards for Neurological Classification of SCI exams to forecast 6-month recovery of segmental motor scores as the primary evaluation endpoint. Secondary endpoints included severity grade improvement, independent walking, and self-care ability. Different similarity metrics were explored for k-nearest neighbor (kNN) matching within 1267 patients from the European Multicenter Study about Spinal Cord Injury before validation in 411 patients from the Sygen trial. The kNN performance was compared to linear and logistic regression models.
RESULTS: We obtained a population-wide root-mean-squared error (RMSE) in motor score sequence of 0.76(0.14, 2.77) and competitive functional score predictions (AUCwalker = 0.92, AUCself-carer = 0.83) for the kNN algorithm, improving beyond the linear regression task (RMSElinear = 0.98(0.22, 2.57)). The validation cohort showed comparable results (RMSE = 0.75(0.13, 2.57), AUCwalker = 0.92). We deploy the final historic control model as a web tool for easy user interaction (https://hicsci.ethz.ch/).
CONCLUSIONS: Our approach is the first to provide predictions across all motor segments independent of the level and severity of SCI. We provide a machine learning concept that is highly interpretable, i.e. the prediction formation process is transparent, that has been validated across European and American data sets, and provides reliable and validated algorithms to incorporate external control data to increase sensitivity and feasibility of multinational clinical trials.
METHODS: This study used acute-phase International Standards for Neurological Classification of SCI exams to forecast 6-month recovery of segmental motor scores as the primary evaluation endpoint. Secondary endpoints included severity grade improvement, independent walking, and self-care ability. Different similarity metrics were explored for k-nearest neighbor (kNN) matching within 1267 patients from the European Multicenter Study about Spinal Cord Injury before validation in 411 patients from the Sygen trial. The kNN performance was compared to linear and logistic regression models.
RESULTS: We obtained a population-wide root-mean-squared error (RMSE) in motor score sequence of 0.76(0.14, 2.77) and competitive functional score predictions (AUCwalker = 0.92, AUCself-carer = 0.83) for the kNN algorithm, improving beyond the linear regression task (RMSElinear = 0.98(0.22, 2.57)). The validation cohort showed comparable results (RMSE = 0.75(0.13, 2.57), AUCwalker = 0.92). We deploy the final historic control model as a web tool for easy user interaction (https://hicsci.ethz.ch/).
CONCLUSIONS: Our approach is the first to provide predictions across all motor segments independent of the level and severity of SCI. We provide a machine learning concept that is highly interpretable, i.e. the prediction formation process is transparent, that has been validated across European and American data sets, and provides reliable and validated algorithms to incorporate external control data to increase sensitivity and feasibility of multinational clinical trials.
摘要：
目的：创伤性脊髓损伤（SCI）后的神经和功能恢复受到病变程度和严重程度（不同程度的脊髓/完全SCI）和脊髓综合征（半，ants-,central-,和后绳）。到目前为止，临床试验中的结果预测仅限于针对上肢（UEMS）和下肢（LEMS）的运动得分总和，而忽略了运动功能的分布对于功能结果至关重要。从病变水平到最低运动节段的所有脊柱节段的详细节段运动恢复的数据驱动预测模型的开发将改善康复计划的设计和临床试验的敏感性。
方法：本研究使用急性期国际SCI检查神经学分类标准来预测6个月的节段性运动评分恢复作为主要评价终点。次要终点包括严重性等级改善，独立行走,和自理能力。在Sygen试验的411例患者中进行验证之前，对来自欧洲多中心脊髓损伤研究的1267例患者中的k最近邻（kNN）匹配进行了探索。将kNN性能与线性和逻辑回归模型进行比较。
结果：我们在kNN算法的运动分数序列中获得了0.76（0.14，2.77）和竞争功能分数预测（AUCwalker=0.92，AUCself-carer=0.83）的全人群均方根误差（RMSE），改进超出线性回归任务(RMSElinear=0.98(0.22，2.57))。验证队列显示出可比的结果（RMSE=0.75（0.13，2.57），AUCwalker=0.92）。我们将最终的历史控制模型部署为Web工具，以便于用户交互（https://hicsci。艾思兹.ch/)。
结论：我们的方法是第一个提供独立于SCI水平和严重程度的所有运动部分的预测。我们提供了一个高度可解释的机器学习概念，即预测形成过程是透明的，这已经在欧洲和美国的数据集中得到了验证，并提供可靠和经过验证的算法，以纳入外部控制数据，以提高多国临床试验的敏感性和可行性。
方法：本研究使用急性期国际SCI检查神经学分类标准来预测6个月的节段性运动评分恢复作为主要评价终点。次要终点包括严重性等级改善，独立行走,和自理能力。在Sygen试验的411例患者中进行验证之前，对来自欧洲多中心脊髓损伤研究的1267例患者中的k最近邻（kNN）匹配进行了探索。将kNN性能与线性和逻辑回归模型进行比较。
结果：我们在kNN算法的运动分数序列中获得了0.76（0.14，2.77）和竞争功能分数预测（AUCwalker=0.92，AUCself-carer=0.83）的全人群均方根误差（RMSE），改进超出线性回归任务(RMSElinear=0.98(0.22，2.57))。验证队列显示出可比的结果（RMSE=0.75（0.13，2.57），AUCwalker=0.92）。我们将最终的历史控制模型部署为Web工具，以便于用户交互（https://hicsci。艾思兹.ch/)。
结论：我们的方法是第一个提供独立于SCI水平和严重程度的所有运动部分的预测。我们提供了一个高度可解释的机器学习概念，即预测形成过程是透明的，这已经在欧洲和美国的数据集中得到了验证，并提供可靠和经过验证的算法，以纳入外部控制数据，以提高多国临床试验的敏感性和可行性。
