Improvement of the gait deviation index for spinal cord injury to broaden its applicability: the reduced gait deviation index for spinal cord injury (rSCI-GDI).

Background: The SCI-GDI is an accurate and effective metric to summarize gait kinematics in adults with SCI. It is usually computed with the information registered with a photogrammetry system because it requires accurate information of pelvic and hip movement in the three anatomic planes, which is hard to record with simpler systems. Additionally, due to being developed from the GDI, the SCI-GDI is built upon nine joint movements selected for a pediatric population with cerebral palsy, for which the GDI was originally developed, but those nine movements are not necessarily as meaningful for adults with SCI. Nevertheless, pelvic movement and hip rotation have been proven to have low reliability even when acquired with gold-standard photogrammetry systems. Additionally, the use of photogrammetry is limited in real-life scenarios and when used with rehabilitation technologies, which limits the use of the SCI-GDI to evaluate gait in alternative scenarios to gait laboratories and to evaluate technologies for gait assistance. This research aimed to improve the SCI-GDI to broaden its applicability beyond the use of photogrammetry.

Methods: An exploration of the mathematical relevance of each joint movement included in the original GDI for the performance of the metric is performed. Considering the results obtained and the clinical relevance of each of the 9 joints used to compute the SCI-GDI in the gait pattern of the SCI population, a more adaptable SCI-GDI is proposed using four joint movements that can be precisely captured with simpler systems than photogrammetry: sagittal planes of hip, knee and ankle and hip abduction/adduction.

Results: The reduced SCI-GDI (rSCI-GDI) effectively represents gait variability of adults with SCI as does the SCI-GDI, while providing more generalizable results and equivalent or stronger correlations with clinical tests validated in the population. During the derivation of the improved index, it was demonstrated that pelvic movements, hip rotation, and foot progression angle introduce high variability to the dataset of gait patterns of the adult population with SCI, but they have low relevance to characterize gait kinematics of this population. The rSCI-GDI can be calculated using the 14-feature vectorial basis included in the electronic addendum provided.