Comparison of the predictive validity of three fall risk assessment tools and analysis of fall‐risk factors at a tertiary teaching hospital
Eun Hee Cho, Yun Jung Woo, Arum Han, Yoon Chung Chung, Yeon Hee Kim, Hyeoun‐Ae Park
Abstract
AIMS AND OBJECTIVES: The main purpose of this study was to identify the best fall-risk assessment tool, among the Morse Fall Scale, the Johns Hopkins fall-risk Assessment Tool and the Hendrich II fall-risk Model, for a tertiary teaching hospital. The study also analysed fall-risk factors in the hospital, focusing on the items of each fall assessment tool. METHODS: Data on falls were obtained from the patient safety reports and electronic nursing records of a tertiary teaching hospital. A retrospective study was conducted to compare the sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, Youden index and accuracy of the Morse Fall Scale, the Johns Hopkins fall-risk Assessment Tool and the Hendrich II fall-risk Model. This study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology guideline for reporting case-control studies. RESULTS: By analysing the association between falls and the items included in the three tools, we identified significant fall-risk factors such as gait, dizziness or vertigo, changes in mental status, impulsivity, history of falling, elimination disorder, drugs affecting falls, and depression. CONCLUSIONS: The Hendrich II fall-risk Model had the best predictive performance for falls of the three tools, considering the highest in the area under the curve and the Youden index that comprehensively analysed sensitivity and specificity, while the Johns Hopkins fall-risk Assessment Tool had the highest accuracy. The most significant fall-risk predictors are gait, dizziness or vertigo, change in mental state, and history of falling. RELEVANCE TO CLINICAL PRACTICE: To improve the fall assessment performance of the Morse Fall Scale at the study hospital, we propose that it be supplemented with four most significant fall-risk predictors identified in this study.