Joe Hollinghurst1, Gemma Housley2, Alan Watkins1, Andrew Clegg3, Thomas Gilbert4, Simon P Conroy5. 1. Health Data Research UK (HDR-UK), Data Science Building, Swansea University, Swansea SA2 8PP, UK. 2. East Midlands Academic Science Health Network, Nottingham, UK. 3. University of Leeds, Leeds, UK. 4. Department Geriatric Medicine, Lyon University Teaching Hospital, Lyon, France. 5. Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH.
Abstract
BACKGROUND: The electronic Frailty Index (eFI) has been developed in primary care settings. The Hospital Frailty Risk Score (HFRS) was derived using secondary care data. OBJECTIVE: Compare the two different tools for identifying frailty in older people admitted to hospital. DESIGN AND SETTING: Retrospective 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. METHODS: Pearson'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. RESULTS: Pearson'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. CONCLUSIONS: The 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.
BACKGROUND: The electronic Frailty Index (eFI) has been developed in primary care settings. The Hospital Frailty Risk Score (HFRS) was derived using secondary care data. OBJECTIVE: Compare the two different tools for identifying frailty in older people admitted to hospital. DESIGN AND SETTING: Retrospective 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. METHODS: Pearson'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. RESULTS: Pearson'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. CONCLUSIONS: The 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.
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