Does the postural variable affect the determination of balance compensation level in vestibular schwannoma patients?. (September 2017)

• Record Type: Journal Article
• Authors: Frère, Julien; Hoffmann, Charles P.; Gauchard, Gérome C.; Parietti-Winkler, Cécile
• Is Part Of: Medical engineering & physics. Volume 47(2017)
• Page Start: 214
• Page End: 217
• Publication Date: 2017-09
• Subjects: Balance control -- Centre of pressure fluctuations -- Sensory organization test -- Unilateral vestibular deafferentation; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2017.06.021
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Four postural variable to assess balance control pre- and post-surgery. Whatever the postural variable used, a significant balance compensation was found. Equilibrium score tended to overestimate the balance compensation. Abstract: Previous sensory organization test (SOT) outcomes provided evidences that the vestibular schwannoma (VS) removal led to acute and slow changes in the ability to use vestibular cues for balance control. However, the assumptions behind the equilibrium score (ES) are somewhat controversial within the literature. Therefore, we compared the time-course of balance control in eighteen VS patients across different postural variables. Analyses of variance and unsupervised clustering (k-means) were used to compare the whole dataset of four postural variables: ES, a modified ES which factored in the time before a fall (cES), the mean amplitude (MA) and the mean velocity (MV) of center of pressure displacements. Early after surgery, postural performances significantly decreased ( p < 0.01), especially when vestibular cues are predominant, with lower ES (12.5 ± 22.8%) and cES (26.6 ± 16.7%) and higher MA (27.7 ± 7.1 mm) and MV (95.2 ± 53.2 mm.s −1 ) than before or three months after surgery. For each postural variable, the k-means clustering divided the whole dataset into two clusters: cluster #1 corresponded to a SOT with low sway amplitude and cluster #2 with high sway amplitude and falls. Overall, whatever the selected postural variable, the main recovery profile of VS patients was highlighted, thus surpassing the ambiguities inherent of the ES calculation.