A Self-Reported Clinical Tool Predicts Falls in People with Parkinson's Disease.

BACKGROUND

A 3-step clinical prediction tool including falling in the previous year, freezing of gait in the past month and self-selected gait speed <1.1 m/s has shown high accuracy in predicting falls in people with Parkinson's disease (PD). The accuracy of this tool when including only self-report measures is yet to be determined.

OBJECTIVES

To validate the 3-step prediction tool using only self-report measures (3-step self-reported prediction tool), and to externally validate the 3-step clinical prediction tool.

METHODS

The clinical tool was used with 137 individuals with PD. Participants also answered a question about self-reported gait speed, enabling scoring of the self-reported tool, and were followed-up for 6 months. An intraclass correlation coefficient (ICC) was calculated to evaluate test-retest reliability of the 3-step self-reported prediction tool. Multivariate logistic regression models were used to evaluate the performance of both tools and their discriminative ability was determined using the area under the curve (AUC).

RESULTS

Forty-two participants (31%) reported ≥1 fall during follow-up. The 3-step self-reported tool had an ICC of 0.991 (95% CI 0.971-0.997;  < 0.001) and AUC = 0.68; 95% CI 0.59-0.77, while the 3-step clinical tool had an AUC = 0.69; 95% CI 0.60-0.78.

CONCLUSIONS

The 3-step self-reported prediction tool showed excellent test-retest reliability and was validated with acceptable accuracy in predicting falls in the next 6 months. The 3-step clinical prediction tool was externally validated with similar accuracy. The 3-step self-reported prediction tool may be useful to identify people with PD at risk of falls in e/tele-health settings.