Highlighting Discrepancies in Walking Prediction Accuracy for Patients with Traumatic Spinal Cord Injury: an Evaluation of Validated Prediction Models using a Canadian Multi-centre Spinal Cord Injury Registry

Publication date: Available online 1 September 2018

Source: The Spine Journal

Author(s): Philippe Phan, Brandon Budhram, Qiong Zhang, Carly Rivers, Vanessa K. Noonan, Tova Plashkes, Eugene K. Wai, Jérôme Paquet, Darren M. Roffey, Eve Tsai, Nader Fallah, The RHSCIR Network

ABSTRACT

Background Context

Models 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.

Purpose

To 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 Design

Prospective cohort study.

Participant Sample

Individuals with tSCI enrolled in the pan-Canadian Rick Hansen SCI Registry (RHSCIR) between 2004-2016with complete neurologic examination and Functional Independence Measure (FIM) outcome data.

Outcome Measures

The FIM locomotor score was used to assess independent walking ability at 1-year follow-up.

Methods

Two validated prediction models were evaluated for their ability to predict walking one-year post-injury. Relative prognostic performance was compared with the area under the receiver operating curve (AUC). This study and the RHSCIR are supported by funding from Health Canada, Western Economic Diversification Canada, and the Governments of Alberta, British Columbia, Manitoba, and Ontario. The funders had no role in the study or study reporting.

Results

In 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% 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), while 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=0.00034;p=0.00038).The models were not statistically different in individual or subgroup analyses.

Conclusions

Previously tested prediction models demonstrated lower predictive accuracy for AIS B+C and 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.

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