Predicting falls among patients with multiple sclerosis: Comparison of patient-reported outcomes and performance-based measures of lower extremity functions

Abstract

BackgroundAccurate fall screening tools are needed to identify those multiple sclerosis (MS) patients at high risk of falling. The present study aimed at determining the validity of a series of performance-based measures (PBMs) of lower extremity functions and patient-reported outcomes (PROs) in predicting falls in a sample of MS patients (n = 84), who were ambulatory independent. MethodsPatients were assessed using the following PBMs: timed up and go (TUG), timed 25-foot walk (T25FW), cognitive T25FW, 2-min walk (2MW), and cognitive 2MW. Moreover, a series of valid and reliable PROs were filled in by participants including the activities-specific balance confidence (ABC), 12-item multiple sclerosis walking scale (MSWS-12), fall efficacy scale international (FES-I), and modified fatigue impact scale (MFIS). The dual task cost (DTC) of 2MW and T25FW tests were calculated as a percentage of change in parameters from single to dual task conditions. Participants were classified as none-fallers and fallers (⩾1) based on their prospective fall occurrence. ResultsIn the present study, 41(49%) participants recorded ≥ 1 fall and were classified as fallers. The results of logistic regression analysis revealed that each individual test, except DTC of 2MW and T25FW, significantly predicted future falls. However, considering the area under the curves (AUCs), PROs were more accurate compared to PBMs. In addition, the results of multiple logistic regression with the first two factors extracted from principal component analysis revealed that both factor 1 (PROs) and factor 2 (PBMs) significantly predicted falls with a greater odds ratio (OR) for factor 1 (factor 1: P = <0.0001, OR = 63.41 (6.72–597.90)) than factor 2 (P <0.05, OR = 5.03 (1.33–18.99)). ConclusionsThe results of this study can be used by clinicians to identify and monitor potential fallers in MS patients.