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. 2013 Jan;27(1):45-52.
doi: 10.1177/1545968312445638. Epub 2012 May 15.

The diagnostic accuracy of static posturography in predicting accidental falls in people with multiple sclerosis

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The diagnostic accuracy of static posturography in predicting accidental falls in people with multiple sclerosis

Luca Prosperini et al. Neurorehabil Neural Repair. 2013 Jan.

Abstract

Background: Quantitative posturography has been reported as a reliable tool to measure balance in people with multiple sclerosis (MS). However, data on its diagnostic accuracy in predicting the occurrence of falls are lacking.

Objective: To determine sensitivity, specificity, predictive values, and accuracy of posturography in detecting falls in MS subjects over a 3-month follow-up period.

Methods: . One hundred consecutive patients with MS were tested by the Berg Balance Scale (BBS) and by static posturography on a monoaxial platform. Participants recorded the occurrence of accidental falls for the next 3 months. Abnormal cutoff values for static standing balance measures were set at 2 standard deviations above the mean values obtained from 50 healthy controls (HC). The diagnostic accuracy of the BBS and static posturography was analyzed with respect to the prospectively collected data on the occurrence of falls.

Results: Posturometric measures in participants with MS were significantly worse than in HC (all P values <.0001); however, only the center of pressure (COP) path with open eyes condition had substantial test-retest reliability. Static posturography was more sensitive (88% vs 37%) and accurate (75% vs 63%), but slightly less specific (67% vs 81%), than the BBS in predicting accidental falls. A logistic regression analysis revealed that the worse the COP path, the greater the risk for accidental falls (odds ratio = 1.08; P < .0001), even after adjusting for sex, age, disease duration, body mass index, MS subtype, Expanded Disability Status Scale, and BBS score.

Conclusion: The COP path measurement in static position is a sensitive and accurate tool to identify people with MS who are at risk of accidental falls.

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