Angela Vratsistas-Curto, Anne Tiedemann, Daniel Treacy, Stephen R. Lord, Cathie Sherrington
Musculoskeletal Health Sydney, School of Public Health, The University of Sydney, 2000 Sydney, Australia. E-mail: angela.vratsistas@sydney.edu.au
DOI: 10.2340/16501977-2290

Objectives: To test the external validity of 4 approaches to fall prediction in a rehabilitation setting (Predict_FIRST, Ontario Modified STRATIFY (OMS), physiotherapists’ judgement of fall risk (PT_Risk), and falls in the past year (Past_Falls)), and to develop and test the validity of a simpler tool for fall prediction in rehabilitation (Predict_CM2).
Participants: A total of 300 consecutively-admitted rehabilitation inpatients.
Methods: Prospective inception cohort study. Falls during the rehabilitation stay were monitored. Potential predictors were extracted from medical records.
Results: Forty-one patients (14%) fell during their rehabilitation stay. The external validity, area under the receiver operating characteristic curve (AUC), for predicting future fallers was: 0. 71 (95% confidence interval (95% CI): 0. 61–0. 81) for OMS (Total_Score); 0. 66 (95% CI: 0. 57–0. 74) for Predict_FIRST; 0. 65 (95% CI 0. 57–0. 73) for PT_Risk; and 0. 52 for Past_Falls (95% CI: 0. 46–0. 60). A simple 3-item tool (Predict_CM2) was developed from the most predictive individual items (impaired mobility/transfer ability, impaired cognition, and male sex). The accuracy of Predict_CM2 was 0. 73 (95% CI: 0. 66–0. 81), comparable to OMS (Total_Score) (p = 0. 52), significantly better than Predict_FIRST (p = 0. 04), and Past_Falls (p < 0. 001), and approaching significantly better than PT_Risk (p = 0. 09).
Conclusion: Predict_CM2 is a simpler screening tool with similar accuracy for predicting fallers in rehabilitation to OMS (Total_Score) and better accuracy than Predict_FIRST or Past_Falls. External validation of Predict_CM2 is required.

Predicting which patients are most likely to fall can be difficult for health staff working in a hospital rehabilitation ward. We studied the accuracy of four different approaches to predicting falls in rehabilitation (the Predict_FIRST screening tool, the Ontario Modified STRATIFY screening tool, physiotherapists’ judgement of fall risk and a single question about falls in past year). We also developed and proposed a simpler screening tool which we named the Predict_CM2. We tested the four approaches and developed the new tool using data from 300 patients admitted to a rehabilitation ward. We found that Predict_CM2 had similar accuracy to the Ontario Modified STRATIFY tool and better accuracy than the Predict_FIRST tool or a single question about falls in past year at predicting falls. We concluded that the Predict_CM2 can be used for predicting falls in rehabilitation and recommended it be tested in other rehabilitation settings.

Supplementary Material