141 Using Random Forest Plots to Examine Predictive and Discriminative Ability of Mobility Measures for Future Falls (Age and Ageing)

AbstractBackgroundSlow gait speed and Timed Up-and Go (TUG) are often independent predictors of falls in regression analysis, although their ability to discriminate between fallers and non-fallers is questionable. Random forest plots can model complex interactions between predictor variables and have high classification accuracy. We compare the predictive and discriminative ability of UGS and TUG in predicting recurrent falls over 4 years using poisson regression and random forest plots.MethodsData from the first three waves of The Irish Longitudinal Study on Ageing (TILDA), a population-based study of community-dwelling adults aged >=50 years were used. Baseline physical, neuro-cognitive, sensory and behavioural health were assessed. The outcome was recurrent (>=2) falls at Wave 2 or Wave 3. Poisson regression analysis was used to examine associations between UGS, TUG and falls adjusting for covariates. Random forest models were used. Predictive accuracy was calculated using 5 fold cross-validation and as there was class imbalance, the algorithm was trained using under-sampling of the larger class. Classification rate, area under the receiver operating characteristic curve (AUC) and area under the precision recall curve (PRROC) were obtained to assess predictive accuracy.ResultsIn poisson regression analysis (n=4918), TUG predicted recurrent falls independent of covariates including UGS (IRR=1.06, 95% CI: 1.01, 1.12, p<0.05). The random forest model predicted 60.82% of participants correctly (61.85% of non-fallers; 55.37% of fallers). AUC was 0.63 and PRROC was 0.28.ConclusionImpaired mobility i.e. slower TUG performance, is an independent predictor of future recurrent falls, however it does not discriminate well between fallers and non-fallers. This is highlighted by the PRROC which provides a more conservative estimate of predictive accuracy than AUC as it accounts for the ability to identify both fallers and non-fallers. The analysis highlights the multifactorial nature and complexity of falls, and supports the need for comprehensive falls assessment.