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External validation of the electronic Frailty Index using the population of Wales within the Secure Anonymised Information Linkage Databank.
Hollinghurst, Joe; Fry, Richard; Akbari, Ashley; Clegg, Andy; Lyons, Ronan A; Watkins, Alan; Rodgers, Sarah E.
Afiliação
  • Hollinghurst J; Health Data Research UK (HDR-UK), Data Science Building, Swansea University, Swansea SA2 8PP, UK.
  • Fry R; Health Data Research UK (HDR-UK), Data Science Building, Swansea University, Swansea SA2 8PP, UK.
  • Akbari A; National Centre for Population Health and Wellbeing Research, Swansea University Medical School, Swansea SA2 8PP, UK.
  • Clegg A; Health Data Research UK (HDR-UK), Data Science Building, Swansea University, Swansea SA2 8PP, UK.
  • Lyons RA; Administrative Data Research Centre Wales, Swansea University Medical School, Swansea, UK.
  • Watkins A; University of Leeds (Bradford Teaching Hospital), Bradford Institute for Health Research, Temple Bank House, Bradford Royal Infirmary, Bradford BD9 6RJ, UK.
  • Rodgers SE; Health Data Research UK (HDR-UK), Data Science Building, Swansea University, Swansea SA2 8PP, UK.
Age Ageing ; 48(6): 922-926, 2019 11 01.
Article em En | MEDLINE | ID: mdl-31566668
ABSTRACT

BACKGROUND:

frailty has major implications for health and social care services internationally. The development, validation and national implementation of the electronic Frailty Index (eFI) using routine primary care data has enabled change in the care of older people living with frailty in England.

AIMS:

to externally validate the eFI in Wales and assess new frailty-related outcomes. STUDY DESIGN AND

SETTING:

retrospective cohort study using the Secure Anonymised Information Linkage (SAIL) Databank, comprising 469,000 people aged 65-95, registered with a SAIL contributing general practice on 1 January 2010.

METHODS:

four categories (fit; mild; moderate and severe) of frailty were constructed using recognised cut points from the eFI. We calculated adjusted hazard ratios (HRs) from Cox regression models for validation of existing

outcomes:

1-, 3- and 5-year mortality, hospitalisation, and care home admission for validation. We also analysed, as novel outcomes, 1-year mortality following hospitalisation and frailty transition times.

RESULTS:

HR trends for the validation outcomes in SAIL followed the original results from ResearchOne and THIN databases. Relative to the fit category, adjusted HRs in SAIL (95% CI) for 1-year mortality following hospitalisation were 1.05 (95% CI 1.03-1.08) for mild frailty, 1.24 (95% CI 1.21-1.28) for moderate frailty and 1.51 (95% CI 1.45-1.57) for severe frailty. The median time (lower and upper quartile) between frailty categories was 2,165 days (lower and upper quartiles 1,510 and 2,831) from fit to mild, 1,155 days (lower and upper quartiles 756 and 1,610) from mild to moderate and 898 days (lower and upper quartiles 584 and 1,275) from moderate to severe.

CONCLUSIONS:

further validation of the eFI showed robust predictive validity and utility for new outcomes.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: Fragilidade Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male País/Região como assunto: Europa Idioma: En Revista: Age Ageing Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: Fragilidade Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male País/Região como assunto: Europa Idioma: En Revista: Age Ageing Ano de publicação: 2019 Tipo de documento: Article