Predictors of Recurrent Falls in People with Parkinson's Disease and Proposal for a Predictive Tool

Background: Falls are a debilitating problem for people with Parkinson's disease (PD).

Objectives: To compare clinical and functional characteristics of non-fallers, single and recurrent fallers (≥2 falls); to determine predictors of time to second fall; and to develop a predictive tool for identifying people with PD at different categories of falls risk.

Methods: Participants (n = 229) were assessed by disease-specific, self-report and balance measures and followed up for 12 months. Area under the receiver operating characteristic curves (AUC), Kaplan-Meier curves and log-rank test were performed. Selected predictors with p < 0.10 in univariate analysis were chosen to be entered into the Cox regression model.

Results: Eighty-four (37%) participants had ≥2 falls during the follow-up. Recurrent fallers significantly differed from single fallers. The final Cox model included history of ≥2 falls in the past year (Hazard Ratio [HR] = 3.94; 95% confidence interval [CI] 2.26-6.86), motor fluctuations (HR = 1.91; 95% CI 1.12-3.26), UPDRS activities of daily living (ADL) (HR = 1.10 per 1 point increase; 95% CI 1.06-1.14) and levodopa equivalent dose (LED) (HR = 1.09 per 100 mg increase; 95% CI 1.02-1.16). A 3-predictor tool included history of ≥2 falls in the past year, motor fluctuations and UPDRS ADL >12 points (AUC = 0.84; 95% CI 0.78-0.90). By adding LED >700 mg/day and Berg balance scale ≤49 points, a 5-predictor tool was developed (AUC = 0.86; 95% CI 0.81-0.92).

Conclusions: Two predictive tools with moderate-to-high accuracy may identify people with PD at low, medium and high risk of falling recurrently within the next year. However, future studies to address external validation are required.