15 September 2020 : Clinical Research
A Novel Multiple-Cue Observational Clinical Scale for Functional Evaluation of Gait After Stroke – The Stroke Mobility Score (SMS)Dominik Raab1ABCDEFG*, Brigitta Diószeghy-Léránt2ABF, Meret Wünnemann2B, Christina Zumfelde2B, Elena Cramer3BE, Alina Rühlemann4B, Johanna Wagener45B, Silke Gegenbauer6BE, Francisco Geu Flores1G, Marcus Jäger45BEG, Dörte Zietz3BEG, Harald Hefter7ABCDEFG, Andres Kecskemethy1ACDEFG, Mario Siebler2ABCDEFG
Med Sci Monit 2020; 26:e923147
BACKGROUND: For future development of machine learning tools for gait impairment assessment after stroke, simple observational whole-body clinical scales are required. Current observational scales regard either only leg movement or discrete overall parameters, neglecting dysfunctions in the trunk and arms. The purpose of this study was to introduce a new multiple-cue observational scale, called the stroke mobility score (SMS).
MATERIAL AND METHODS: In a group of 131 patients, we developed a 1-page manual involving 6 subscores by Delphi method using the video-based SMS: trunk posture, leg movement of the most affected side, arm movement of the most affected side, walking speed, gait fluency and stability/risk of falling. Six medical raters then validated the SMS on a sample of 60 additional stroke patients. Conventional scales (NIHSS, Timed-Up-And-Go-Test, 10-Meter-Walk-Test, Berg Balance Scale, FIM-Item L, Barthel Index) were also applied.
RESULTS: (1) High consistency and excellent inter-rater reliability of the SMS were verified (Cronbach’s alpha >0.9). (2) The SMS subscores are non-redundant and reveal much more nuanced whole-body dysfunction details than conventional scores, although evident correlations as e.g. between 10-Meter-Walk-Test and subscore “gait speed” are verified. (3) The analysis of cross-correlations between SMS subscores unveils new functional interrelationships for stroke profiling.
CONCLUSIONS: The SMS proves to be an easy-to-use, tele-applicable, robust, consistent, reliable, and nuanced functional scale of gait impairments after stroke. Due to its sensitivity to whole-body motion criteria, it is ideally suited for machine learning algorithms and for development of new therapy strategies based on instrumented gait analysis.
Keywords: Gait Disorders, Neurologic, Stroke, Symptom Assessment, Treatment Outcome
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