A machine learning-based fall-risk score for severity of fall-related adverse outcomes in community older adults

Models that detect fall risk have been proposed. However, the value of an indicator derived from such models in fall-severity stratification is understudied. This study developed a machine learning (ML)-based fall classification model, constructed a fall-risk score, and explored its association with fall-related adverse outcomes.

We used the eXtreme Gradient Boosting algorithm to build a fall classification model using data from 15,457 community-dwelling adults aged 60 Years and older. Of the 216 fall-associated variables, the 15 most important variables were selected for modelling, and their directional relationships with falls were evaluated using the SHapley Additive exPlanation (SHAP) value. An ML-based fall-risk score (ML-FRS) was generated. Multilevel regression analysis was used to measure the associations between the ML-FRS and fall-related adverse outcomes, defined as recurrent falls or falls requiring treatment, in a subset of 3,514 participants.

Participants had a mean age of 85.4 Years, with 56.3% being women, and a 22.5% prevalence of a fall history. Women and older participants were more Likely to fall and experience fall-related adverse outcomes. Inability to stand up from sitting in a chair was the most important predictor of increased fall risk. A small calf circumference and a low plant-based diet score were associated with increased fall risk. The ML-based model had an area under the curve of 0.797. Compared with non-fallers, participants in the highest ML-FRS quartile had a significantly higher risk of one fall without treatment, recurrent falls without treatment, one fall with treatment, and recurrent falls with treatment.

The ML-FRS could be used to screen for fall risk and fall-related adverse outcomes in community-dwelling older adults.