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
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Le résumé fourni par la source
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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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- 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
- Date Crossref
- 01/03/2024
- Éditeur
- Oxford University Press (OUP)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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NIHR Birmingham Biomedical Research Centre pays non établi dans la noticeStructure de recherche
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University of Birmingham Institute for Applied Health Research pays non établi dans la noticeUniversité ou école supérieure
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University of Leeds Leeds Institute of Health Sciences pays non établi dans la noticeUniversité ou école supérieure
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Swansea University Population Data Science pays non établi dans la noticeUniversité ou école supérieure
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Bradford Teaching Hospitals NHS Foundation Trust pays non établi dans la noticeÉtablissement de santé
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Keele University pays non établi dans la noticeUniversité ou école supérieure
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University College London Institute of Cardiovascular Science pays non établi dans la noticeUniversité ou école supérieure
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Bradford District Care NHS Foundation Trust pays non établi dans la noticeOrganisation à but non lucratif
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School of Medicine pays non établi dans la noticeUniversité ou école supérieure
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Bradford District and Craven Health and Care Partnership pays non établi dans la noticeInstitution
NIHR Birmingham Biomedical Research Centre, Institute for Applied Health Research — University of Birmingham et Leeds Institute of Health Sciences — University of Leeds, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.