External validation of the electronic Frailty Index using the population of Wales within the Secure Anonymised Information Linkage Databank
30 September 2019
Hollinghurst J, Fry R, Akbari A, Clegg A, Lyons RA, Watkins A, Rodgers SE.
Age and Ageing (2019), 48(6): 922–926
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.
to externally validate the eFI in Wales and assess new frailty-related outcomes.
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.
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.
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.
further validation of the eFI showed robust predictive validity and utility for new outcomes.
Health data research
Health Data Science is a discipline that combines maths, statistics and technology to study different types of health problems using data. It provides the tools to manage and analyse very large...
Associate Professor Population Data Science Research at Swansea University
Based at Swansea University since 2008, Ashley plays a substantive role in major research programmes & projects utilising population-scale data, predominantly using the SAIL Databank. Championing...