Hicks Katharine E, Zhao Yichen, Fallah Nader, Rivers Carly S, Noonan Vanessa K, Plashkes Tova, Wai Eugene K, Roffey Darren M, Tsai Eve C, Paquet Jerome, Attabib Najmedden, Marion Travis, Ahn Henry, Phan Philippe
Ottawa Combined Adult Spinal Surgery Program, The Ottawa Hospital, Ottawa, ON K1Y 4E9, Canada.
Rick Hansen Institute, Blusson Spinal Cord Centre, 6400-818 W. 10th Ave, Vancouver, BC V5Z 1M9, Canada; The University of British Columbia, 2329 West Mall, Vancouver, BC V6T 1Z4, Canada.
Spine J. 2017 Oct;17(10):1383-1392. doi: 10.1016/j.spinee.2017.05.031. Epub 2017 Jul 14.
Traumatic spinal cord injury (SCI) is a debilitating condition with limited treatment options for neurologic or functional recovery. The ability to predict the prognosis of walking post injury with emerging prediction models could aid in rehabilitation strategies and reintegration into the community.
To revalidate an existing clinical prediction model for independent ambulation (van Middendorp et al., 2011) using acute and long-term post-injury follow-up data, and to investigatethe accuracy of a simplified model using prospectively collected data from a Canadian multicenter SCI database, the Rick Hansen Spinal Cord Injury Registry (RHSCIR).
Prospective cohort study.
The analysis cohort consisted of 278 adult individuals with traumatic SCI enrolled in the RHSCIR for whom complete neurologic examination data and Functional Independence Measure (FIM) outcome data were available.
The FIM locomotor score was used to assess independent walking ability (defined as modified or complete independence in walk or combined walk and wheelchair modality) at 1-year follow-up for each participant.
A logistic regression (LR) model based on age and four neurologic variables was applied to our cohort of 278 RHSCIR participants. Additionally, a simplified LR model was created. The Hosmer-Lemeshow goodness of fit test was used to check if the predictive model is applicable to our data set. The performance of the model was verified by calculating the area under the receiver operating characteristic curve (AUC). The accuracy of the model was tested using a cross-validation technique. This study was supported by a grant from The Ottawa Hospital Academic Medical Organization ($50,000 over 2 years). The RHSCIR is sponsored by the Rick Hansen Institute and is supported by funding from Health Canada, Western Economic Diversification Canada, and the provincial governments of Alberta, British Columbia, Manitoba, and Ontario. ET and JP report receiving grants from the Rick Hansen Institute (approximately $60,000 and $30,000 per year, respectively). DMR reports receiving remuneration for consulting services provided to Palladian Health, LLC and Pacira Pharmaceuticals, Inc ($20,000-$30,000 annually), although neither relationship presents a potential conflict of interest with the submitted work. KEH received a grant for involvement in the present study from the Government of Canada as part of the Canada Summer Jobs Program ($3,000). JP reports receiving an educational grant from Medtronic Canada outside of the submitted work ($75,000 annually). TM reports receiving educational fellowship support from AO Spine, AO Trauma, and Medtronic; however, none of these relationships are financial in nature. All remaining authors have no conflicts of interest to disclose.
The fitted prediction model generated 85% overall classification accuracy, 79% sensitivity, and 90% specificity. The prediction model was able to accurately classify independent walking ability (AUC 0.889, 95% confidence interval [CI] 0.846-0.933, p<.001) compared with the existing prediction model, despite the use of a different outcome measure (FIM vs. Spinal Cord Independence Measure) to qualify walking ability. A simplified, three-variable LR model based on age and two neurologic variables had an overall classification accuracy of 84%, with 76% sensitivity and 90% specificity, demonstrating comparable accuracy with its five-variable prediction model counterpart. The AUC was 0.866 (95% CI 0.816-0.916, p<.01), only marginally less than that of the existing prediction model.
A simplified predictive model with similar accuracy to a more complex model for predicting independent walking was created, which improves utility in a clinical setting. Such models will allow clinicians to better predict the prognosis of ambulation in individuals who have sustained a traumatic SCI.
创伤性脊髓损伤(SCI)是一种使人衰弱的疾病,神经或功能恢复的治疗选择有限。利用新兴的预测模型预测损伤后行走预后的能力有助于制定康复策略并重新融入社区。
使用损伤后急性和长期随访数据重新验证现有的独立行走临床预测模型(van Middendorp等人,2011年),并利用从加拿大多中心SCI数据库里克·汉森脊髓损伤登记处(RHSCIR)前瞻性收集的数据研究简化模型的准确性。
前瞻性队列研究。
分析队列由278名患有创伤性SCI的成年个体组成,这些个体被纳入RHSCIR,并有完整的神经学检查数据和功能独立性测量(FIM)结果数据。
FIM运动评分用于评估每位参与者在1年随访时的独立行走能力(定义为在行走或行走与轮椅组合方式上的改良或完全独立)。
基于年龄和四个神经学变量的逻辑回归(LR)模型应用于我们的278名RHSCIR参与者队列。此外,创建了一个简化的LR模型。使用Hosmer-Lemeshow拟合优度检验来检查预测模型是否适用于我们的数据集。通过计算受试者工作特征曲线(AUC)下的面积来验证模型的性能。使用交叉验证技术测试模型的准确性。本研究得到渥太华医院学术医疗组织的一项资助(两年内50,000美元)。RHSCIR由里克·汉森研究所赞助,并得到加拿大卫生部、加拿大西部经济多样化部以及艾伯塔省、不列颠哥伦比亚省、马尼托巴省和安大略省政府的资金支持。ET和JP报告从里克·汉森研究所获得资助(分别约为每年60,000美元和30,000美元)。DMR报告因向Palladian Health, LLC和Pacira Pharmaceuticals, Inc提供咨询服务而获得报酬(每年20,000 - 30,000美元),尽管这两种关系与提交的工作均不存在潜在利益冲突。KEH作为加拿大暑期工作计划的一部分,从加拿大政府获得参与本研究的资助(3,000美元)。JP报告在提交的工作之外从美敦力加拿大公司获得教育资助(每年75,000美元)。TM报告获得AO脊柱、AO创伤和美敦力的教育奖学金支持;然而,这些关系均非财务性质。其余所有作者均无利益冲突需要披露。
拟合的预测模型总体分类准确率为85%,敏感性为79%,特异性为90%。尽管使用了不同的结局指标(FIM与脊髓独立性测量)来界定行走能力,但与现有预测模型相比,该预测模型能够准确分类独立行走能力(AUC 0.889,95%置信区间[CI] 0.846 - 0.933,p <.001)。基于年龄和两个神经学变量的简化三变量LR模型总体分类准确率为84%,敏感性为76%,特异性为90%,与其五变量预测模型对应物具有可比的准确性。AUC为0.866(95% CI 0.816 - 0.916,p <.01),仅略低于现有预测模型。
创建了一个与更复杂的预测独立行走模型准确性相似的简化预测模型,提高了其在临床环境中的实用性。此类模型将使临床医生能够更好地预测创伤性SCI患者的行走预后。