BACKGROUND: The electronic frailty index (eFI) is nationally implemented into UK primary care electronic health record systems to support routine identification of frailty. The original eFI has some limitations such as equal weighting of deficit variables, lack of time constraints on variables known to resolve and definition of frailty category cut-points. We have developed and externally validated the eFI2 prediction model to predict the composite risk of home care package
hospital admission for fall/fracture
care home admission
or mortality within one year, addressing the limitations of the original eFI. METHODS: Linked primary, secondary and social care data from two independent retrospective cohorts of adults aged ≥65 in 2018 was used
the population of Bradford using the Connected Bradford dataset (development cohort, 78 760 patients) and the population of Wales, from the Secure Anonymised Information Linkage databank (external validation cohort, 660 417 patients). Candidate predictors included the original eFI variables, supplemented with variables informed by literature reviews and clinical expertise. The composite outcome was modelled using Cox regression. RESULTS: In internal validation the model had excellent discrimination (C-index = 0.803, Nagelkerke's R2 = 0.0971) with good calibration (Calibration slope = 1.00). In external validation, the model had good discrimination (C-index = 0.723, Nagelkerke's R2 = 0.064), with some evidence of miscalibration (Calibration slope = 1.104). CONCLUSIONS: The eFI2 demonstrates robust prediction for key frailty-related outcomes, improving on the original eFI. Our use of novel methodology to develop and validate the eFI2 will advance the field of frailty-related research internationally, setting a new methodological standard.