Predictors of Falls with Injuries in People with Parkinson's Disease.

Background: Falls are frequent in Parkinson's disease (PD), but there is lack of information about predictors of injurious falls. Objectives: To determine predictors of falls with injuries in people with PD; to compare circumstances and consequences of falls in single and recurrent fallers. Methods: Participants (n = 225) were assessed by disease-specific, self-report, and balance measures, and followed-up for 12 months with a diary to record falls, their circumstances, and injuries. Univariate and multivariate analyses were performed. Circumstances and consequences of falls presented by single and recurrent fallers were compared. Results: A total of 805 falls were analyzed, 107 (13%) were falls with injuries. Multivariate logistic regression model revealed that greater PD duration and higher balance confidence were protective factors; better balance during gait, outdoor falls, and falls related to extrinsic factors were risk factors for falls with injuries, when compared to falls with no injuries. Multivariate multinomial regression model revealed that, when compared to zero fall, past falls and daily levodopa equivalent dose were predictors of falls with injuries; these predictors together with disability were predictors of falls with no injuries. Single falls (n = 27; 3%) were more common outdoors because of extrinsic factors, whereas recurrent falls (n = 778; 97%) were more common indoors because of intrinsic factors. Single falls led to more injuries than recurrent falls (P < 0.05). Conclusions: Different predictors of falls with injuries were obtained when different outcomes were compared. It should be noted that falls with injuries might be influenced by fall-related activities and environmental factors. Single and recurrent falls differed on circumstances and consequences.

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 (ICC2,1) 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 ICC2,1 of 0.991 (95% CI 0.971-0.997; P < 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.