External Validation and Further Exploration of Fall Prediction Models based on Questionnaires and Daily-life Trunk Accelerometry

Background Ambulatory measurements of trunk accelerations can provide valuable insight into the amount and quality of daily life activities. Such information has been used to create models that aim to identify individuals at high risk of falls. However, external validation of such prediction models is lacking, yet crucial for clinical implementation. We externally validated three previously described fall prediction models (van Schooten et al., 2015a). Methods Complete questionnaires and one week of trunk acceleration data were obtained in 263 community-dwelling people (mean age 71.8 years, 68.1% female). To validate models, we first used the coefficients and optimal cut-offs from original cohort, then recalibrated the original models, as well as optimized parameters based on our new cohort. Results Among all participants, 39.9% experienced falls during 6-month follow-up. All models showed poor precision (0.20-0.56), poor sensitivity (0.22-0.58), and good specificity (0.59-0.89). Calibration of the original models had limited effect on model performance. Using coefficients and cut-offs optimized on the external cohort also had limited benefit. Last, the odds ratios in our cohort were different from those in the original cohort and indicated that gait characteristics, except for index of harmonicity ML, were not statistically significantly associated with falls. Conclusions Prediction of fall risk in our cohort was not as effective as in the original cohort. Recalibration as well as optimized model parameters resulted in limited increase in accuracy. Fall prediction models are highly specific to the cohort studied. This highlights the need for large representative cohorts, preferably with an external validation cohort.