Related Experiment Video
Updated: Jun 12, 2026
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Development of a tool for prediction of falls in rehabilitation settings (Predict_FIRST): a prospective cohort study
Catherine Sherrington1, Stephen R Lord, Jacqueline C T Close
1The George Institute for International Health, Missenden Road, Sydney NSW 2050, Australia. csherrington@george.org.au
Objective:
To develop and internally validate a simple falls prediction tool for rehabilitation settings.
Design:
Prospective cohort study.
Participants:
A total of 533 inpatients.
Methods:
Possible predictors of falls were collected from medical records, interview and physical assessment. Falls during inpatient stays were monitored.
Results:
Fourteen percent of participants fell. A multivariate model to predict falls included: male gender (odds ratio (OR) 2.70, 95% confidence interval (CI) 1.57-4.64), central nervous system medications (OR 2.50, 95% CI 1.47-4.25), a fall in the previous 12 months (OR 2.21, 95% CI 1.07-4.56), frequent toileting (OR 2.14, 95% CI 1.27-3.62) and tandem stance inability (OR 2.00, 95% CI 1.11-3.59). The area under the curve for this model was 0.74 (95% CI 0.68-0.80). The Predict_FIRST tool is a unit weighted adaptation of this model (i.e. 1 point allocated for each predictor) and its area under the curve was 0.73 (95% CI 0.68-0.79). Predicted and actual falls risks corresponded closely.
Conclusion:
This tool provides a simple way to quantify the probability with which an individual patient will fall during a rehabilitation stay.
