A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry

Publication date: Available online 14 July 2017
Source:The Spine Journal
Author(s): Katharine E. Hicks, Yichen Zhao, Nader Fallah, Carly Rivers, Vanessa Noonan, Tova Plashkes, Eugene K. Wai, Darren M. Roffey, Eve Tsai, Jerome Paquet, Najmedden Attabib, Travis Marion, Henry Ahn, Philippe Phan
BackgroundContext: Traumatic spinal cord injury (SCI) is a debilitating condition with limited treatment options for neurological 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.PurposeTo re-validate an existing clinical prediction model for independent ambulation (van Middendorp et al. 2011) utilising acute and long-term post-injury follow-up data, and to investigate the accuracy of a simplified model using prospectively collected data from a Canadian multicenter SCI database, the Rick Hansen Spinal Cord Injury Registry (RHSCIR).Study DesignProspective cohort study.Participant SampleThe analysis cohort consisted of 278 adult individuals with traumatic SCI enrolled in the RHSCIR for whom complete neurological examination data and Functional Independence Measure (FIM) outcome data was available.Outcome MeasuresThe FIM locomotor score was used to assess independent walking ability (defined as modified or complete independence in walk or combined walk/wheelchair modality) at one-year follow-up for each participant.MethodsA logistic regression (LR) model based on age and four neurological 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 (TOHAMO) ($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.ResultsThe 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% CI 0.846-0.933, p<0.001) compared to the existing prediction model, despite the use of a different outcome measure (FIM versus SCIM) to qualify walking ability. A simplified, three-variable LR model based on age and two neurological variables had an overall classification accuracy of 84% with 76% sensitivity and 90% specificity, demonstrating comparable accuracy to its five-variable prediction model counterpart. The AUC was 0.866 (95% CI 0.816-0.916, p<0.01), only marginally less than that of the existing prediction model.ConclusionsA 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.



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