Utility of an obstacle-crossing test to classify future fallers and non-fallers at hospital discharge after stroke: A pilot study

Brief composite mobility index predicts post-stroke fallers after hospital discharge

Introduction

Community-dwelling, ambulatory stroke survivors fall at very high rates in the first 3-6 months. Current inpatient clinical assessments for fall risk have inadequate predictive accuracy. We found that a pre-discharge obstacle-crossing test has excellent specificity (83%) but lacks acceptable sensitivity (67%) for identifying would-be fallers and non-fallers post discharge.

Hypothesis

We assessed the hypothesis that combining the obstacle-crossing test with other highly discriminatory fall risk factors would compensate for the obstacle test's fair sensitivity and yield an instrument with superior prediction accuracy.

Methods

45 ambulatory stroke survivors (60 ± 11 years old, 15 ± 11 days post stroke) being discharged home completed a battery of physical performance-based and self-reported measures 1-5 days prior to discharge. After discharge, participants were prospectively followed and classified as fallers (≥1 fall) or non-fallers at 3 months. Pre-discharge measures with the largest effect sizes for differentiating fallers and non-fallers were combined into a composite index. Several variations of the composite index were examined to optimize accuracy.

Results

A 4-item discharge composite index significantly predicted fall status at 3-months. The goodness of fit of the regression model was significantly better than the obstacle-crossing test alone, χ ²(1) = 6.036, p = 0.014. Furthermore, whereas the obstacle-crossing test had acceptable overall accuracy (AUC 0.78, 95% CI, 0.60-0.90), the composite index had excellent accuracy (AUC 0.85, 95% CI, 0.74-0.96). Combining the obstacle-crossing test with only the step test produced a model of equivalent accuracy (AUC 0.85, 95% CI, 0.73-0.96) and with better symmetry between sensitivity and specificity (0.71, 0.83) than the 4-item composite index (0.86, 0.67). This 2-item index was validated in an independent sample of n = 30 and with bootstrapping 1,000 samples from the pooled cohorts. The 4-item index was internally validated with bootstrapping 1,000 samples from the derivation cohort plus n = 9 additional participants.

Conclusion

This study provides convincing proof-of-concept that strategic aggregation of performance-based and self-reported mobility measures, including a novel and demanding obstacle-crossing test, can predict post-discharge fallers with excellent accuracy. Further instrument development is warranted to construct a brief aggregate tool that will be pragmatic for inpatient use and improve identification of future post-stroke fallers before the first fall.