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dc.contributor.authorClegg, A.*
dc.contributor.authorBates, C.*
dc.contributor.authorYoung, J.*
dc.contributor.authorRyan, R.*
dc.contributor.authorNichols, L.*
dc.contributor.authorTeale, E.A.*
dc.contributor.authorMohammed, Mohammed A.*
dc.contributor.authorParry, J.*
dc.contributor.authorMarshall, T.*
dc.date.accessioned2016-03-17T12:16:11Z
dc.date.available2016-03-17T12:16:11Z
dc.date.issued2016
dc.identifier.citationClegg A, Bates C, Young J, Ryan R, Nichols L, Teale EA, Mohammed MA, Parry J and Marshall T (2016) Development and validation of an electronic frailty index using routine primary care electronic health record data. Age and ageing. Online before print, 3rd March 2016.
dc.identifier.urihttp://hdl.handle.net/10454/7929
dc.descriptionYes
dc.description.abstractfrailty is an especially problematic expression of population ageing. International guidelines recommend routine identification of frailty to provide evidence-based treatment, but currently available tools require additional resource. Objectives: to develop and validate an electronic frailty index (eFI) using routinely available primary care electronic health record data. Study design and setting: retrospective cohort study. Development and internal validation cohorts were established using a randomly split sample of the ResearchOne primary care database. External validation cohort established using THIN database. Participants: patients aged 65–95, registered with a ResearchOne or THIN practice on 14 October 2008. Predictors: we constructed the eFI using the cumulative deficit frailty model as our theoretical framework. The eFI score is calculated by the presence or absence of individual deficits as a proportion of the total possible. Categories of fit, mild, moderate and severe frailty were defined using population quartiles. Outcomes: outcomes were 1-, 3- and 5-year mortality, hospitalisation and nursing home admission. Statistical analysis: hazard ratios (HRs) were estimated using bivariate and multivariate Cox regression analyses. Discrimination was assessed using receiver operating characteristic (ROC) curves. Calibration was assessed using pseudo-R2 estimates. Results: we include data from a total of 931,541 patients. The eFI incorporates 36 deficits constructed using 2,171 CTV3 codes. One-year adjusted HR for mortality was 1.92 (95% CI 1.81–2.04) for mild frailty, 3.10 (95% CI 2.91–3.31) for moderate frailty and 4.52 (95% CI 4.16–4.91) for severe frailty. Corresponding estimates for hospitalisation were 1.93 (95% CI 1.86– 2.01), 3.04 (95% CI 2.90–3.19) and 4.73 (95% CI 4.43–5.06) and for nursing home admission were 1.89 (95% CI 1.63–2.15), 3.19 (95% CI 2.73–3.73) and 4.76 (95% CI 3.92–5.77), with good to moderate discrimination but low calibration estimates. Conclusions: the eFI uses routine data to identify older people with mild, moderate and severe frailty, with robust predictive validity for outcomes of mortality, hospitalisation and nursing home admission. Routine implementation of the eFI could enable delivery of evidence-based interventions to improve outcomes for this vulnerable group.
dc.language.isoenen
dc.rights© 2016 The Authors. Published open access by Oxford Journals. Reproduced in accordance with the publisher's self-archiving policy.
dc.subjectFrailty
dc.subjectPrimary care
dc.subjectElectronic frailty index
dc.subjectElectronic health record
dc.subjectCumulative deficit
dc.subjectOlder people
dc.titleDevelopment and validation of an electronic frailty index using routine primary care electronic health record data
dc.status.refereedYes
dc.date.Accepted2016-01-20
dc.typeArticle
dc.type.versionPublished version
dc.identifier.doihttps://doi.org/10.1093/ageing/afw039
refterms.dateFOA2018-07-25T14:51:04Z


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