Predicting Fallers in a Community-Based Sample of People with Parkinson’s Disease

Background: The risk of people with Parkinson’s disease (PD) falling is greater than that of the general population but to date, disease-specific predictors of falling have not been identified. Objectives: To identify one or more features, which would predict individuals at risk of falling during a 3-month prospective follow-up study. Method: A battery of standardised tests administered in the home and the laboratory with a 3-month follow-up telephone interview. Results: Sixty-three people with PD were recruited from GP practices. Eleven interview variables and six gait laboratory variables were used with subsamples (55 and 44 subjects, respectively) to fit predictive models for identifying future fallers. The number of falls in the previous year was the most important variable, without exception, to be selected as a predictor in various logistic regression models. A history of two or more falls had a sensitivity of 86.4% (95% CI 67.3–96.2%) and a specificity of 85.7% (95% CI 71.2–94.2%) in predicting falling in the next 3 months. Conclusion: Healthcare workers should be asking their patients with PD regularly and carefully about falling, and should consider instigating programmes of fall management for patients with PD who have fallen two or more times in the previous 12 months.