ArticleAge and ageing2024
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.
Article in Age and ageing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development and evaluation of the electronic frailty index+ (eFI+) tool for older people: prognostic prediction modelling with integrated decision curve and health economic analysis.Health technology assessment (Winchester, England) · 2026Observational
- Personalizing fall fear prevention in knee osteoarthritis: an interpretable prediction framework via IGKSO synchronous optimization and SHAP-based risk stratification.Frontiers in public health · 2026Article
- Call to collaborate on data science for older people across Europe: an EuGMS Big Data Special Interest Group position paper.European geriatric medicine · 2025Article
- A novel electronic-health-record based, machine-learning model to predict 1-year risk of fall hospitalisation in older adults: a Hong Kong territory-wide cohort and modelling study.Age and ageing · 2025Article
- Primary care prediction of hip and knee replacement 1-5 years in advance using Temporal Graph-based Convolutional Neural Networks (TG-CNNs).Rheumatology (Oxford, England) · 2025Article
- Falls prevention in community-dwelling older adults and implementation of world falls guidelines: a call for action across Europe by the European Geriatric Medicine Society Special Interest Group on Falls and Fractures.European geriatric medicine · 2025Article
- Factors associated with hospitalization from a geriatric short-stay unit (OBI-GER): a retrospective cohort study.Aging clinical and experimental research · 2025Article
- An Electronic Frailty Index Based on Deficit Accumulation May Predict Glaucomatous Visual Field Progression.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
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17 authors.
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Abstract
backgroundFalls 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.
methodsData 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.
resultsThe 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.
conclusionThe 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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