• Joe Hollinghurst, Gemma Housley, Alan Watkins, Andrew Clegg, Thomas Gilbert, and Simon P Conroy.
• Health Data Research UK (HDR-UK), Data Science Building, Swansea University, Swansea SA2 8PP, UK.
• Age Ageing. 2021 Jun 28; 50 (4): 1208-1214.

BackgroundThe electronic Frailty Index (eFI) has been developed in primary care settings. The Hospital Frailty Risk Score (HFRS) was derived using secondary care data.ObjectiveCompare the two different tools for identifying frailty in older people admitted to hospital.Design And SettingRetrospective cohort study using the Secure Anonymised Information Linkage Databank, comprising 126,600 people aged 65+ who were admitted as an emergency to hospital in Wales from January 2013 up until December 2017.MethodsPearson's correlation coefficient and weighted kappa were used to assess the correlation between the tools. Cox and logistic regression were used to estimate hazard ratios (HRs) and odds ratios (ORs). The Concordance statistic and area under the receiver operating curves (AUROC) were estimated to determine discrimination.ResultsPearson's correlation coefficient was 0.26 and the weighted kappa was 0.23. Comparing the highest to the least frail categories in the two scores the HRs for 90-day mortality, 90-day emergency readmission and care home admissions within 1-year using the HFRS were 1.41, 1.69 and 4.15 for the eFI 1.16, 1.63 and 1.47. Similarly, the ORs for inpatient death, length of stay greater than 10 days and readmission within 30-days were 1.44, 2.07 and 1.52 for the HFRS, and 1.21, 1.21 and 1.44 for the eFI. AUROC was determined as having no clinically relevant difference between the tools.ConclusionsThe eFI and HFRS have a low correlation between their scores. The HRs and ORs were higher for the increasing frailty categories for both the HFRS and eFI.© The Author(s) 2020. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.

14 keywords...

hide…