Discriminative power of static posturography for identifying recurrent fallers in Parkinson's disease patients.

Abstract

To investigate the use of static posturography as a tool to distinguish between non-recurrent and recurrent Parkinson's disease (PD) fallers based on a 1-year follow-up. This prospective cohort study included 48 participants (35 men and 13 women) from different PD associations. Data collected included fall history, sociodemographic and clinical information, balance tests, and static posturography. Group comparisons were conducted using Chi-square, T-test, Mann-Whitney <i>U</i>, and Hedge's (g) effect size. Pearson's correlation assessed associations between posturographic parameters and clinical tests. A two-step binary logistic regression was used to identify predictors of future falls. Discriminative ability was analyzed using a ROC curve, with the optimal cutoff determined by the Youden index. The mean speed of the center of pressure (eyes-open condition), along with the Hoehn and Yahr and the history of falls, was identified as a significant predictor of recurrent fallers, reporting excellent discrimination values (area under the ROC curve = 0.81) and a cutoff point of 18.1 mm/s. Logistic regression modeling accurately classified 88.2% of participants. Static posturography, specifically the mean speed of the center of pressure (eyes-open condition), demonstrated excellent discriminative ability in identifying PD patients at risk of recurrent falls in this study.