Development and evaluation of the electronic frailty index+ (eFI+) tool: integrated prognostic-decision modelling to target interventions for older people with moderate or severe frailty.


Frailty is common in older age. It develops because as we get older our bodies change, and can lose their inbuilt reserves, for example we lose muscle strength. These changes mean that older people with frailty can experience sudden, dramatic changes in their health as a result of seemingly small problems, such as infection or new medication.

People with frailty are at risk of losing their independence and help from home care services may be needed. They are also at higher risk of falling, admission to nursing homes and death. These problems can reduce quality of life and are costly for the NHS and social care.

Previous research has shown that treatments such as community rehabilitation, falls prevention programmes and comprehensive geriatric assessment (provided by a team of doctors, nurses, therapists and social workers) can improve independence, reduce falls and reduce nursing home admission for people with frailty. Also, advance care planning (which is a conversation between people, their families and those looking after them to decide on future wishes) can increase quality of care and reduce hospital admission for people nearing end of life. We have developed a tool called the electronic frailty index (eFI), which uses routine information from the GP record to help identify frailty. However, the problem at the moment is that we do not know which older people living with moderate or severe frailty are most likely to benefit from these treatments.

In this study, we have developed an improved version of the eFI, the eFI+, to help health and social care practitioners know which people are most likely to benefit from treatments.

We used anonymous patient information from two very large databases that include detailed health and social care information (the Secure Anonymised Information Linkage (SAIL) databank and the Connected Bradford dataset), and data from a national study – the Community Ageing Research 75+ (CARE75+) study.

We used information from the two very large databases to predict, in the next 12 months, which older people are at risk of:

  1. Needing new or increased home care services.
  2. Hospital admission with a fall or fracture.
  3. Nursing home admission.
  4. Dying.

We then used CARE75+ data to see if simple tests like measuring walking speed or grip strength can help us identify people with frailty at risk of falling. The next step was to use this data on risk prediction to find out how much benefit we might expect from the treatments, using a process called ‘decision modelling’. The final step was to test whether offering treatments is likely to be cost-effective – important information for the NHS and social care.

All four risk prediction tools performed very well, with accurate identification of people likely to experience the four outcomes over the next 12 months. The addition of walking speed and grip strength measures did not make a notable improvement to the falls prediction model. We found that the greatest cost-saving for individuals was if falls prevention interventions were provided to people at higher risk of falling (over 40% risk of falling in next 12 months). If this was done nationally, we projected that savings of around £10M could be generated.

Our research to develop the eFI+ will enable identification of older people who are most likely to benefit from treatments to improve health and wellbeing. Through our work we expect to have major positive impact on the health and wellbeing of older people living with frailty, their families and carers along with major benefits to the NHS and social care.


Currently Underway

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