Core Sets of Kinematic Variables to Consider for Evaluation of Gait Post-stroke.

Background: Instrumented gait analysis post-stroke is becoming increasingly more common in research and clinics. Although overall standardized procedures are proposed, an almost infinite number of potential variables for kinematic analysis is generated and there remains a lack of consensus regarding which are the most important for sufficient evaluation. The current aim was to identify a discriminative core set of kinematic variables for gait post-stroke.

Methods: We applied a three-step process of statistical analysis on commonly used kinematic gait variables comprising the whole body, derived from 3D motion data on 31 persons post-stroke and 41 non-disabled controls. The process of identifying relevant core sets involved: (1) exclusion of variables for which there were no significant group differences; (2) systematic investigation of one, or combinations of either two, three, or four significant variables whereby each core set was evaluated using a leave-one-out cross-validation combined with logistic regression to estimate a misclassification rate (MR).

Results: The best MR for one single variable was shown for the (MR 0.10) or (MR 0.11) , corresponding to an 89-90% probability of correctly classifying a person as post-stroke/control. Adding s to either of the variables or , alternatively adding to the , increased the probability of correctly classifying individuals to 93-94% (MR 0.06). Combining three variables decreased the MR further to 0.04, suggesting a probability of 96% for correct classification. These core sets contained: (1) a spatial () or a temporal variable ( or , (2) s or , and (3) or or a knee/shoulder joint angle variable. Adding a fourth variable did not further improve the MR.

Conclusion: A core set combining a few crucial kinematic variables may sufficiently evaluate post-stroke gait and should receive more attention in rehabilitation. Our results may contribute toward a consensus on gait evaluation post-stroke, which could substantially facilitate future diagnosis and monitoring of rehabilitation progress.