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

PROSPERINI L; FORTUNA R; GIANNI E; LEONARDI C; POZZILLI C
NEUROREHABIL NEURAL REPAIR , 2013, vol. 27, n° 1, p. 45-52
Doc n°: 166103
Localisation : Documentation IRR

D.O.I. : http://dx.doi.org/DOI:10.1177/1545968312445638
Descripteurs : AE3 - SEP, DF11 - POSTURE. STATION DEBOUT

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.

Langue : ANGLAIS

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