Using Conditional Inference Forests to Examine Predictive Ability for Future Falls and Syncope in Older Adults: Results from The Irish Longitudinal Study on Ageing

Abstract

Abstract Background The extent to which gait and mobility measures predict falls relative to other risk factors is unclear. This study examined the predictive accuracy of over 70 baseline risk factors, including gait and mobility, for future falls and syncope using conditional inference forest models. Methods Data from 3 waves of The Irish Longitudinal Study on Ageing (TILDA), a population-based study of community-dwelling adults aged ≥50 years were used (n = 4 706). Outcome variables were recurrent falls, injurious falls, unexplained falls, and syncope occurring over 4-year follow-up. The predictive accuracy was calculated using 5-fold cross-validation; as there was a class imbalance, the algorithm was trained using undersampling of the larger class. Classification rate, the area under the receiver operating characteristic curve (AUROC), and area under the precision recall curve (PRAUC) assessed predictive accuracy. Results Highest overall accuracy was 69.7% for recurrent falls in 50–64-year olds. AUROC and PRAUC were ≤0.69 and ≤0.39, respectively, for all outcomes indicating low predictive accuracy. History of falls, unsteadiness while walking, fear of falling, mobility, medications, mental health, and cardiovascular health and function were the most important predictors for most outcomes. Conclusions Conditional inference forest models using over 70 risk factors resulted in low predictive accuracy for future recurrent, injurious and unexplained falls, and syncope in community-dwelling adults. Gait and mobility impairments were important predictors of most outcomes but did not discriminate well between fallers and non-fallers. Results highlight the importance of multifactorial risk assessment and intervention and validate key modifiable risk factors for future falls and syncope.