Abstract
BACKGROUND: Falls risk assessment tools are used in hospital inpatient settings to identify patients at increased risk of falls to guide and target interventions for fall prevention. In 2022, Western Health, Melbourne, Australia, introduced a new falls risk assessment tool, the Western Health St. Thomas' Risk Assessment Tool (WH-STRATIFY), which adapted The Northern Hospital's risk tool (TNH-STRATIFY) by adding non-English speaking background and falls-risk medication domains to reflect patient demographics. WH-STRATIFY replaced Peninsula Health Risk Screening Tool (PH-FRAT) previously in use at Western Health. This study compared the predictive accuracy of the three falls risk assessment tools in an older inpatient high-risk population. AIMS: To determine the predictive accuracy of three falls risk assessment tools (PH-FRAT, TNH-STRATIFY, and WH-STRATIFY) on admission to Geriatric Evaluation Management (GEM) units (subacute inpatient wards where the most frail and older patients rehabilitate under a multi-disciplinary team). METHOD: A retrospective observational study was conducted on four GEM units. Data was collected on 54 consecutive patients who fell during admission and 62 randomly sampled patients who did not fall between December 2020 and June 2021. Participants were scored against three falls risk assessment tools. The event rate Youden (Youden Index(ER)) indices were calculated and compared using default and optimal cut points to determine which tool was most accurate for predicting falls. RESULTS: Overall, all tools had low predictive accuracy for falls. Using default cut points to compare falls assessment tools, TNH-STRATIFY had the highest predictive accuracy (Youden Index(ER) = 0.20, 95% confidence interval CI = 0.07, 0.34). The PH-FRAT (Youden Index(ER) = 0.01 and 95% CI = -0.04, 0.05) and WH-STRATIFY (Youden Index(ER) = 0.00 and 95% CI = -0.04, 0.03) were statistically equivalent and not predictive of falls compared to TNH-STRATIFY. When calculated optimal cut points were applied, predictive accuracy improved for PH-FRAT (Cut point 17, Youden Index(ER) = 0.14 and 95% CI = 0.01, 0.29) and WH-STRATIFY (Cut point 7, Youden Index(ER) = 0.18 and 95% CI = 0.00, 0.35). CONCLUSIONS: TNH-STRATIFY had the highest predictive accuracy for falls. The predictive accuracy of WH-STRATIFY improved and was significant when the calculated optimal cut point was applied. The optimal cut points of falls risk assessment tools should be determined and validated in different clinical settings to optimise local predictive accuracy, enabling targeted fall risk mitigation strategies and resource allocation.