Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults

Abstract

Background: Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year.

Methods: Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups.

Results: The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration.

Conclusion: The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.

Keywords: falls; older people; prediction model; prevention; proactive; prognosis.

Figure 1

Calibration plots and decision curves of the eFalls model on internal validation (in the model development data) and external validation, before and after recalibration. The pre-defined region of clinical interest (threshold probabilities between 10% and 25%) is highlighted on the decision curves.

Figure 2

Variability in performance of the eFalls prediction model on internal validation, across GP practices in the model development data. Plots show calibration slope, calibration-in-the-large, observed/expected ratio and c-statistic plotted against their standard error within each practice. Bounds show 95% prediction intervals for the performance measure across possible standard errors.

Figure 3

Variability in performance of the eFalls prediction model on external validation across GP practices—prior to recalibration. Plots show calibration slope, calibration-in-the-large, observed/expected ratio and c-statistic plotted against their standard error within each practice. Bounds show 95% prediction intervals for the performance measure across possible standard errors.