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Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
Published on: July 24, 2013
The electronic frailty index indicates mortality risk in end stage kidney disease patients on haemodialysis:
Samuel D Relton1, Jenny Hewison1, Zoe Rogers1
1Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK.
Background:
Over the last 30 years, with increasing comorbidity and frailty among people with end-stage kidney disease, there is recognition that dialysis may carry little benefit in some patient groups. We examined the utility of the electronic frailty index (eFI), modelling the survival time of patients starting dialysis at a single renal unit in northern England, hypothesising this would give useful prognostic survival information.
Methods:
Two datasets describing the same population of patients receiving dialysis over a 226-month period (2000-2019) at a single dialysis unit were used (n = 599 and 553 participants). One dataset was derived from primary care (linked to UK Renal Registry submissions) and the other from the UK Renal Registry (linked with primary care data to facilitate eFI calculation). Retrospective survival analysis was undertaken with hazard ratios for the impact of eFI and other covariates on survival.
Results:
The frailty score at dialysis start has a strong effect on subsequent survival time using either the primary care-derived or UK Renal Registry dataset, but there were differences in overall survival and the relation between eFI score and survival between datasets.
Conclusions:
This represents the first study of eFI utility within tertiary renal care, demonstrating a strong association between frailty measured by eFI and survival time for patients on dialysis. However, significant differences between primary care-derived and UK Renal Registry-derived survival at different frailty scores were demonstrated. These differences indicate a need for greater understanding and analysis of large datasets before using them to discuss patient treatment choices, service planning and delivery.
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