Archer, Lucinda; Relton, Samuel D; Akbari, Ashley; Best, Kate; Bucknall, Milica; Conroy, Simon; Hattle, Miriam; ... The eFI+ investigators; + view all Archer, Lucinda; Relton, Samuel D; Akbari, Ashley; Best, Kate; Bucknall, Milica; Conroy, Simon; Hattle, Miriam; Hollinghurst, Joe; Humphrey, Sara; Lyons, Ronan A; Richards, Suzanne; Walters, Kate; West, Robert; van der Windt, Danielle; Riley, Richard D; Clegg, Andrew; The eFI+ investigators; - view fewer (2024) 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. Age and Ageing , 53 (3) , Article afae057. 10.1093/ageing/afae057.

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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.

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