Department of Health Sciences and Technology (D-HEST), ETH Zurich, Universitätstrasse 2, 8092 Zürich, Switzerland; Schulthess Klinik, Lengghalde 2, 8008 Zürich, Switzerland; SIB Swiss Institute of Bioinformatics, Switzerland.
Spinal Cord Injury Center, Trauma Center Murnau, Murnau, Germany.
Exp Neurol. 2024 Oct;380:114918. doi: 10.1016/j.expneurol.2024.114918. Epub 2024 Aug 12.
Spinal cord injury (SCI) is a rare condition with a heterogeneous presentation, making the prediction of recovery challenging. However, serological markers have been shown to be associated with severity and long-term recovery following SCI. Therefore, our investigation aimed to assess the feasibility of translating this association into a prediction of the lower extremity motor scores (LEMS) at chronic stage (52 weeks after initial injury) in patients with SCI using routine serological markers. Serological markers, assessed within the initial seven days post-injury in the observational cohort study from the Trauma Hospital Murnau underwent diverse feature engineering approaches. These involved arithmetic measurements such as mean, median, minimum, maximum, and range, as well as considerations of the frequency of marker testing and whether values fell within the normal range. To predict LEMS scores at the chronic stage, eight different regression models (including linear, tree-based, and ensemble models) were used to quantify the predictive value of serological markers relative to a baseline model that relied on the very acute LEMS score and patient age alone. The inclusion of serological markers did not improve the performance of the prediction model. The best-performing approach including serological markers achieved a mean absolute error (MAE) of 6.59 (2.14), which was equivalent to the performance of the baseline model. As an alternative approach, we trained separate models based on the LEMS observed at the very acute stage after injury. Specifically, we considered individuals with an LEMS of 0 or an LEMS exceeding zero separately. This strategy led to a mean improvement in MAE across all cohorts and models, of 1.20 (2.13). We conclude that, in our study, routine serological markers hold limited power for prediction of LEMS. However, the implementation of model stratification by the very acute LEMS markedly enhanced prediction performance. This observation supports the inclusion of clinical knowledge in the modeling of prediction tasks for SCI recovery. Additionally, it lays the path for future research to consider stratified analyses when investigating the predictive power of potential biomarkers.
脊髓损伤 (SCI) 是一种罕见的疾病，表现多样，因此预测其恢复情况具有挑战性。然而，血清标志物已被证明与 SCI 后的严重程度和长期恢复有关。因此，我们的研究旨在评估使用常规血清标志物将这种关联转化为预测 SCI 患者慢性期（初始损伤后 52 周）下肢运动评分 (LEMS) 的可行性。在观察性队列研究中，创伤医院 Murnau 在损伤后最初七天内评估了血清标志物，并对其进行了多种特征工程处理。这些方法包括算术测量，如平均值、中位数、最小值、最大值和范围，以及考虑标记物检测的频率和值是否在正常范围内。为了预测慢性期的 LEMS 评分，我们使用了八种不同的回归模型（包括线性、基于树的和集成模型），以量化血清标志物相对于仅依赖非常急性 LEMS 评分和患者年龄的基线模型的预测价值。纳入血清标志物并没有提高预测模型的性能。包括血清标志物的最佳表现方法的平均绝对误差 (MAE) 为 6.59（2.14），与基线模型的性能相当。作为一种替代方法，我们根据损伤后非常急性阶段观察到的 LEMS 分别训练了单独的模型。具体来说，我们分别考虑 LEMS 为 0 或 LEMS 超过 0 的个体。这种策略导致所有队列和模型的 MAE 平均提高了 1.20（2.13）。我们得出结论，在我们的研究中，常规血清标志物对 LEMS 的预测能力有限。然而，通过非常急性 LEMS 对模型进行分层的实施显著提高了预测性能。这一观察结果支持在 SCI 恢复的预测任务建模中纳入临床知识。此外，它为未来的研究奠定了基础，当研究潜在生物标志物的预测能力时，可以考虑分层分析。
