Validation of a fall risk index in stroke rehabilitation

Horgan

J Epidemiol Community Health

et al. 2016

What this study adds?This review identifies risk prediction models for falls after stroke that require validation and impact measurement. It will facilitate future researchers in the appropriate measurement of prognostic factors for model validation, a key step in the development process of models that can be incorporated into clinical decisionmaking post stroke. 4 ABSTRACTBackground: Falls are a significant cause of morbidity after stroke. The aim of this

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Systematic review of risk prediction models for falls after stroke

Horgan

J Epidemiol Community Health

et al. 2016

Discriminating High Fall Risk on an Inpatient Rehabilitation Unit

Gilewski

Roberts

Hirata

et al. 2007

Rehabilitation Nursing

The objective of this study was to identify on admission the most discriminating fall predictors for patients to an inpatient rehabilitation unit. Medical information from 34 patients who fell over a consecutive 7-month period and 102 controls (1:3 ratio) matched for diagnosis, age, and gender was analyzed to identify a set of best predictors. Admission mobility and problem solving FIM scores accounted for 17% of variance in whether a fall occurred during the admission. After statistically deriving optimal cutoff thresholds for decision making, high fall risk was retroactively assigned to patients. Logistic regression revealed increased odds of having fallen by 5.1 times for poorer mobility and 2.4 times for poorer problem solving. The practical benefits of the evidence-based risk assessment were discussed.

Predicting Falls Using the Stroke Assessment of Fall Risk Tool

Yang

Ghaedi

Campbell

et al. 2020

PM&R

Background Falls in the inpatient stroke population are common, resulting in increased morbidity and slow rehabilitation progress. Falls may result from stroke‐specific neurologic deficits; however, assessment of these deficits is lacking in many fall screening tools. Objective To compare the ability to predict falls of the Stroke Assessment of Fall Risk (SAFR) tool, which includes items related to stroke‐specific neurologic deficits, and the commonly used Morse Fall Scale, which does not include these items. Design Prospective cohort study. Setting Inpatient tertiary stroke rehabilitation unit. Participants Patients (N = 220) with acute stroke. Main Outcome Measures Falls were captured by the medical records from January 2017 to September 2018. Logistic regression analysis evaluated both screening tools for predicting falls by calculating sensitivity, specificity, area under the receiver operating characteristic (AUC‐ROC) curve, and odds ratio (OR). We compared SAFR and Morse mean scores between fallers and non‐fallers using t‐tests. Results Forty‐eight (21.8%) patients experienced ≥1 fall. SAFR, but not Morse, scores showed a statistically significant difference between fallers and non‐fallers (P = .001 vs P = .24, respectively). Higher SAFR score was associated with higher odds of falls (OR 1.36, 95% CI [1.12, 1.64]), whereas Morse was not (OR 1.04, 95% CI [0.97, 1.12]). SAFR showed a statistically significant difference in hemi‐neglect between fallers and non‐fallers (P = .03). Sensitivity and specificity of SAFR were 47.9% and 76.7%, vs 45.8% and 68.0% for Morse, respectively. SAFR positive predictive value and negative predictive value were 36.5% and 84.1%, respectively, similar to Morse (28.6% and 81.8%). The AUC‐ROC was 0.65 for SAFR and 0.56 for Morse. Conclusions SAFR was significantly associated with fall risk and had better discrimination between fallers and non‐fallers than Morse. The neurologic‐specific hemi‐neglect component of SAFR, a component not present on the Morse, was a fall risk factor. Further research evaluating the predictive value of fall scales that include neurologic deficits is needed.