Recalled Number of Falls in the Past Year-Combined With Perceived Mobility-Predicts the Incidence of Future Falls in Unilateral Lower Limb Prosthesis Users.

Abstract

Falls are a frequent and costly concern for lower limb prosthesis (LLP) users. At present, there are no models that clinicians can use to predict the incidence of future falls in LLP users. Assessing who is at risk for falls, therefore, remains a challenge. The purpose of this study was to test whether easily accessible clinical attributes and measurements predict the incidence of future falls in LLP users. In this prospective observational study, a secondary analysis of data from 60 LLP users was conducted. LLP users reported the number of falls that they recalled over the past year before prospectively reporting falls over a 6-month observation period via monthly telephone calls. Additional candidate predictor variables were recorded at baseline. Negative binomial regression was used to develop a model intended to predict the incidence of future falls. The final model, which included the number of recalled falls (incidence rate ratio = 1.13; 95% CI = 1.01 to 1.28) and Prosthetic Limb Users Survey of Mobility T-scores (incidence rate ratio = 0.949; 95% CI = 0.90 to 1.01), was significantly better than a null model at predicting the number of falls over the next 6months (χ22 = 9.76) and fit the observed prospective fall count data (χ256 = 54.78). The number of recalled falls and Prosthetic Limb Users Survey of Mobility T-scores predicted the incidence of falls over the next 6months in established, unilateral LLP users. The success and simplicity of the final model suggests that it may serve as a screening tool for clinicians to use for assessing risk of falls. Additional research to validate the proposed model in an independent sample of LLP users is needed. Owing to its simplicity, the final model may serve as a suitable screening measure for clinicians to ascertain an initial evaluation of fall risk in established unilateral LLP users. Analyzing falls data as counts rather than as a categorical variable may be an important methodological consideration for falls prevention research.