Validation of a fall rate prediction model for community-dwelling older adults: a combined analysis of three cohorts with 1850 participants

Abstract

Abstract Background Fragility fractures in older adults are often caused by fall events. The estimation of an expected fall rate might improve the identification of individuals at risk of fragility fractures and improve fracture prediction. Methods A combined analysis of three previously developed fall rate models using individual participant data (n = 1850) was conducted using the methodology of a two-stage meta-analysis to derive an overall model. These previously developed models included the fall history as a predictor recorded as the number of experienced falls within 12 months, treated as a factor variable with the levels 0, 1, 2, 3, 4 and ≥ 5 falls. In the first stage, negative binomial regression models for every cohort were fit. In the second stage, the coefficients were compared and used to derive overall coefficients with a random effect meta-analysis. Additionally, external validation was performed by applying the three data sets to the models derived in the first stage. Results The coefficient estimates for the prior number of falls were consistent among the three studies. Higgin’s I 2 as heterogeneity measure ranged from 0 to 55.39%. The overall coefficient estimates indicated that the expected fall rate increases with an increasing number of previous falls. External model validation revealed that the prediction errors for the data sets were independent of the model to which they were applied. Conclusion This analysis suggests that the fall history treated as a factor variable is a robust predictor of estimating future falls among different cohorts.

Keywords

• Falls
• Fragility fractures
• Older adults
• Model validation
• Count regression