Objective 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. Methods 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. Results The final model, which included the number of recalled falls (incidence rate ratio [IRR] = 1.13; 95% CI = 1.01–1.28) and Prosthetic Limb Users Survey of Mobility (PLUS-M) T-scores (IRR = 0.949; 95% CI = 0.90–1.01), was significantly better than a null model at predicting the number of falls over the next 6 months (χ22 = 9.76) and fit the observed prospective fall count data (χ256 = 54.78). Conclusion The number of recalled falls and Prosthetic Limb Users Survey of Mobility T-scores predicted the incidence of falls over the next 6 months 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.

Introduction Several personal characteristics have been associated with an increased risk of injurious falls by lower limb prosthesis (LLP) users. To date, however, none have been used to effectively predict the occurrence of injurious falls. Objective To develop a model that could predict the number of injurious falls over the next 6 months and identify fall‐related circumstances that may increase the odds of a fall being injurious in unilateral LLP users. Design A secondary analysis of a prospective observational study. Setting Research laboratory. Participants Sixty unilateral LLP users with a transtibial or transfemoral amputation. Intervention Not applicable. Main outcome measure(s) Participants' characteristics were recorded at baseline. Falls and their circumstances and consequences were collected prospectively over 6 months via monthly telephone calls. Multivariate negative binomial regression was used to predict the number of injurious falls over the next 6 months in LLP users. Incidence rate ratios (IRRs) were derived to determine the risk of an injurious fall. Bivariate logistic regression was used to identify the associations between injurious falls and fall‐related circumstances. Odds ratios (ORs) were derived to characterize the odds that a fall would be injurious. Results The final multivariate model, which included the number of falls recalled in the past year (IRR = 1.31, 95% confidence interval [CI]: 1.01–1.71, p = .045) and balance confidence (p = .120), predicted the number of injurious falls in the next 6 months (χ2 (2) = 8.15, p = .017). Two fall‐related circumstances were found to increase the odds that a fall would be injurious, fatigue due to activity (OR = 13.5, 95% CI: 3.50–52.3, p  = .001), and tiredness from a lack of sleep (OR = 5.36, 95% CI: 1.22–23.6, p = .026). Conclusion The results suggest that the number of falls recalled in the past year and balance confidence scores predict the number of injurious falls an LLP user will experience in the next 6 months.

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