Research article

Prediction of falls using a risk assessment tool in the acute care setting

Alexandra Papaioannou¹ , William Parkinson² , Richard Cook³ , Nicole Ferko^4,5 , Esther Coker⁶ and Jonathan D Adachi¹

¹Department of Medicine, McMaster University, Hamilton, Ontario, Canada

²School of Rehabilitation Sciences, McMaster University, Hamilton, Ontario, Canada

³Department of Statistics & Actuarial Sciences, University of Waterloo, Waterloo, Ontario, Canada

⁴Department of Clinical Health Sciences, McMaster University, Hamilton, Ontario, Canada

⁵Innovus Research Inc., Burlington, Ontario, Canada

⁶Hamilton Health Sciences, Hamilton, Ontario, Canada

author email corresponding author email

BMC Medicine 2004, 2:1doi:10.1186/1741-7015-2-1

Published:	21 January 2004

Abstract

Background

The British STRATIFY tool was previously developed to predict falls in hospital. Although the tool has several strengths, certain limitations exist which may not allow generalizability to a Canadian setting. Thus, we tested the STRATIFY tool with some modification and re-weighting of items in Canadian hospitals.

Methods

This was a prospective validation cohort study in four acute care medical units of two teaching hospitals in Hamilton, Ontario. In total, 620 patients over the age of 65 years admitted during a 6-month period. Five patient characteristics found to be risk factors for falls in the British STRATIFY study were tested for predictive validity. The characteristics included history of falls, mental impairment, visual impairment, toileting, and dependency in transfers and mobility. Multivariate logistic regression was used to obtain optimal weights for the construction of a risk score. A receiver-operating characteristic curve was generated to show sensitivities and specificities for predicting falls based on different threshold scores for considering patients at high risk.

Results

Inter-rater reliability for the weighted risk score indicated very good agreement (inter-class correlation coefficient = 0.78). History of falls, mental impairment, toileting difficulties, and dependency in transfer / mobility significantly predicted fallers. In the multivariate model, mental status was a significant predictor (P < 0.001) while history of falls and transfer / mobility difficulties approached significance (P = 0.089 and P = 0.077 respectively). The logistic regression model led to weights for a risk score on a 30-point scale. A risk score of 9 or more gave a sensitivity of 91% and specificity of 60% for predicting who would fall.

Conclusion

Good predictive validity for identifying fallers was achieved in a Canadian setting using a simple-to-obtain risk score that can easily be incorporated into practice.