• Philippe Phan, Brandon Budhram, Qiong Zhang, Carly S Rivers, Vanessa K Noonan, Tova Plashkes, Eugene K Wai, Jérôme Paquet, Darren M Roffey, Eve Tsai, and Nader Fallah.
• Ottawa Combined Adult Spinal Surgery Program, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Clinical Epidemiology Program, The Ottawa Hospital,, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada. Electronic address: pphan@toh.ca.
• Spine J. 2019 Apr 1; 19 (4): 703-710.

Background ContextModels for predicting recovery in traumatic spinal cord injury (tSCI) patients have been developed to optimize care. Several models predicting tSCI recovery have been previously validated, yet recent findings question their accuracy, particularly in patients whose prognoses are the least predictable.PurposeTo compare independent ambulatory outcomes in AIS (ASIA [American Spinal Injury Association] Impairment Scale) A, B, C, and D patients, as well as in AIS B+C and AIS A+D patients by applying two existing logistic regression prediction models.Study DesignA prospective cohort study.Participant SampleIndividuals with tSCI enrolled in the pan-Canadian Rick Hansen SCI Registry (RHSCIR) between 2004 and 2016 with complete neurologic examination and Functional Independence Measure (FIM) outcome data.Outcome MeasuresThe FIM locomotor score was used to assess independent walking ability at 1-year follow-up.MethodsTwo validated prediction models were evaluated for their ability to predict walking 1-year postinjury. Relative prognostic performance was compared with the area under the receiver operating curve (AUC).ResultsIn total, 675 tSCI patients were identified for analysis. In model 1, predictive accuracies for 675 AIS A, B, C, and D patients as measured by AUC were 0.730 (95% confidence interval [CI] 0.622-0.838), 0.691 (0.533-0.849), 0.850 (0.771-0.928), and 0.516 (0.320-0.711), respectively. In 160 AIS B+C patients, model 1 generated an AUC of 0.833 (95% CI 0.771-0.895), whereas model 2 generated an AUC of 0.821 (95% CI 0.754-0.887). The AUC for 515 AIS A+D patients was 0.954 (95% CI 0.933-0.975) with model 1 and 0.950 (0.928-0.971) with model 2. The difference in prediction accuracy between the AIS B+C cohort and the AIS A+D cohort was statistically significant using both models (p=.00034; p=.00038). The models were not statistically different in individual or subgroup analyses.ConclusionsPreviously tested prediction models demonstrated a lower predictive accuracy for AIS B+C than AIS A+D patients. These models were unable to effectively prognosticate AIS A+D patients separately; a failure that was masked when amalgamating the two patient populations. This suggests that former prediction models achieved strong prognostic accuracy by combining AIS classifications coupled with a disproportionately high proportion of AIS A+D patients.Copyright © 2019. Published by Elsevier Inc.

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