Predicting falls within 3 months of emergency department discharge among community-dwelling older adults using self-report tools versus a brief functional assessment

Identifying older adults with risk for falls prior to discharge home from the Emergency Department (ED) could help direct fall prevention interventions, yet ED-based tools to assist risk stratification are under-developed. The aim of this study was to assess the performance of self-report and functional assessments to predict falls in the 3 months post-ED discharge for older adults. A prospective cohort of community-dwelling adults age 60 years and older were recruited from one urban ED (N = 134). Participants completed: a single item screen for mobility (SIS-M), the 12-item Stay Independent Questionnaire (SIQ-12), and the Timed Up and Go test (TUG). Falls were defined through self-report of any fall at 1- and 3-months and medical record review for fall-related injury 3-months post-discharge. We developed a hybrid-convolutional recurrent neural network (HCRNN) model of gait and balance characteristics using truncal 3-axis accelerometry collected during the TUG. Internal validation was conducted using bootstrap resampling with 1000 iterations for SIS-M, FRQ, and GUG and leave-one-out for the HCRNN. We compared performance of M-SIS, FRQ, TUG time, and HCRNN by calculating the area under the receiver operating characteristic area under the curves (AUCs). 14 (10.4%) of participants met our primary outcome of a fall or fall-related injury within 3-months. The SIS-M had an AUC of 0.42 [95% confidence interval (CI) 0.19–0.65]. The SIQ-12 score had an AUC of 0.64 [95% confidence interval (CI) 0.49–0.80]. The TUG had an AUC of 0.48 (95% CI 0.29–0.68). The HCRNN model using generated accelerometer features collected during the TUG had an AUC of 0.99 (95% CI 0.98–1.00). We found that self-report and functional assessments lack sufficient accuracy to be used in isolation in the ED. A neural network model using accelerometer features could be a promising modality but research is needed to externally validate these findings. •Self-reported screening tools lack sufficient accuracy to identify older adults who will fall.•Time to complete the Get Up and Go task lacks sufficient accuracy to identify older adults who will fall.•A neural network model of 3-axis accelerometer features collected during the TUG may be able to improve accuracy.