Department of Orthopedics, University of British Columbia, Vancouver, British Columbia, Canada.
Vancouver Spine Surgery Institute, Vancouver, British Columbia, Canada.
J Neurotrauma. 2019 Sep 15;36(18):2732-2742. doi: 10.1089/neu.2018.6335. Epub 2019 Apr 10.
Clinical trials of novel therapies for acute spinal cord injury (SCI) are challenging because variability in spontaneous neurologic recovery can make discerning actual treatment effects difficult. Unbiased Recursive Partitioning regression with Conditional Inference Trees (URP-CTREE) is a novel approach developed through analyses of a large European SCI database (European Multicenter Study about Spinal Cord Injury). URP-CTREE uses early neurologic impairment to predict achieved motor recovery, with potential to optimize clinical trial design by optimizing patient stratification and decreasing sample sizes. We performed external validation to determine how well a previously reported URP-CTREE model stratified patients into distinct homogeneous subgroups and predicted subsequent neurologic recovery in an independent cohort. We included patients with acute cervical SCI level C4-C6 from a prospective registry at a quaternary care center from 2004-2018 ( = 101) and applied the URP-CTREE model and evaluated Upper Extremity Motor Score (UEMS) recovery, considered correctly predicted when final UEMS scores were within a pre-specified threshold of 9 points from median; sensitivity analyses evaluated the effect of timing of baseline neurological examination. We included 101 patients, whose mean times from injury baseline and follow-up examinations were 6.1 days (standard deviation [SD] 17) and 235.0 days (SD 71), respectively. Median UEMS recovery was 7 points (interquartile range 2-12). One of the predictor variables was not statistically significant in our sample; one group did not fit progressively improving UEMS scores, and three of five groups had medians that were not significantly different from adjacent groups. Overall accuracy was 75%, but varied from 82% among participants whose examinations occurred at <12 h, to 64% at 12-24 h, and 58% at >24 h. A previous URP-CTREE model had limited ability to stratify an independent into homogeneous subgroups. Overall accuracy was promising, but may be sensitive to timing of baseline neurological examinations. Further evaluation of external validity in incomplete injuries, influence of timing of baseline examinations, and investigation of additional stratification strategies is warranted.
急性脊髓损伤（SCI）的新型治疗方法的临床试验具有挑战性，因为自发神经恢复的变异性可能难以确定实际的治疗效果。通过对大型欧洲 SCI 数据库（欧洲多中心脊髓损伤研究）的分析，开发了一种新的方法，即无偏递归分区回归与条件推理树（URP-CTREE）。URP-CTREE 使用早期神经损伤来预测获得的运动恢复，具有通过优化患者分层和减少样本量来优化临床试验设计的潜力。我们进行了外部验证，以确定以前报告的 URP-CTREE 模型在独立队列中对患者进行分层的效果，并预测随后的神经恢复。我们纳入了 2004 年至 2018 年在一家四级护理中心前瞻性登记处的急性颈段 C4-C6 SCI 患者（n=101），并应用 URP-CTREE 模型评估上肢运动评分（UEMS）的恢复情况，当最终 UEMS 评分与中位数相差 9 分以内时，认为预测正确；敏感性分析评估了基线神经检查时间的影响。我们纳入了 101 例患者，其损伤基线和随访检查的平均时间分别为 6.1 天（标准差 17）和 235.0 天（标准差 71）。UEMS 恢复的中位数为 7 分（四分位距 2-12）。在我们的样本中，有一个预测变量没有统计学意义；有一组 UEMS 评分没有呈逐渐改善的趋势，五组中有三组的中位数与相邻组没有显著差异。总体准确率为 75%，但在基线神经检查时间<12 小时的参与者中为 82%，在 12-24 小时的参与者中为 64%，在>24 小时的参与者中为 58%。以前的 URP-CTREE 模型在将独立样本分为同质亚组方面能力有限。总体准确率有希望，但可能对基线神经检查的时间敏感。需要进一步评估不完全损伤的外部有效性、基线检查时间的影响以及其他分层策略的研究。
