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Updated: Aug 7, 2025
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Development and evaluation of a simple predictive model for falls in acute care setting
Masae Satoh1, Takeshi Miura2, Tomoko Shimada3
1Department of Nursing, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.
Aims And Objectives:
To develop a simple and reliable assessment tool for predicting falls in acute care settings.
Background:
Falling injures patients, lengthens hospital stay and leads to the wastage of financial and medical resources. Although there are many potential predictors for falls, a simple and reliable assessment tool is practically necessary in acute care settings.
Design:
A retrospective cohort study.
Methods:
The current study was conducted for participants who were admitted to a teaching hospital in Japan. Fall risk was assessed by the modified Japanese Nursing Association Fall Risk Assessment Tool consisting of 50 variables. To create a more convenient model, variables were first limited to 26 variables and then selected by stepwise logistic regression analysis. Models were derived and validated by dividing the whole dataset into a 7:3 ratio. Sensitivity, specificity, and area under the curve for the receiver-operating characteristic curve were evaluated. This study was conducted according to the STROBE guideline.
Results:
Six variables including age > 65 years, impaired extremities, muscle weakness, requiring mobility assistance, unstable gait and psychotropics were chosen in a stepwise selection. A model using these six variables with a cut-off point of 2 with one point for each item, was developed. Sensitivity and specificity >70% and area under the curve >.78 were observed in the validation dataset.
Conclusions:
We developed a simple and reliable six-item model to predict patients at high risk of falling in acute care settings.
Relevance To Clinical Practice:
The model has also been verified to perform well with non-random partitioning by time and future research is expected to make it useful in acute care settings and clinical practice.
Patient Or Public Contribution:
Patients participated in the study on an opt-out basis, contributing to the development of a simple predictive model for fall prevention during hospitalisation that can be shared with medical staff and patients in the future.
