Article

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.

Langue : ANGLAIS