Title: 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
Funder: Department of Health and Social Care

Abstract: 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 an infection or new medication. _x000D_ _x000D_ 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. _x000D_ _x000D_ 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._x000D_ _x000D_ In this study, we will develop an improved version of the eFI, the eFI+, to help health and social care practitioners know which people with moderate and severe frailty are most likely to benefit from treatments. To do this, we will use anonymous patient information from three very large databases that include detailed health and social care information, and data from a national study - the Community Ageing Research 75+ (CARE75+) study._x000D_ _x000D_ The first step in eFI+ development will be to use information from the three very large databases to predict, in the next 12 months, which older people with moderate or severe frailty are at risk of:_x000D_ 1. Needing new or increased home care services._x000D_ 2. Hospital admission with a fall or fracture._x000D_ 3. Nursing home admission._x000D_ 4. Dying._x000D_ _x000D_ We will also use CARE75+ data to see if simple tests like measuring walking speed can help us identify people with frailty at risk of losing independence._x000D_ _x000D_ The second step will be 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'._x000D_ _x000D_ The third step will be to test whether offering such treatments is likely to be cost-effective - important information for the NHS and social care._x000D_ _x000D_ We will work closely with older people, carers, policy makers, doctors, nurses, therapists, and commissioners of services to make sure that our work is useful for the NHS and social care. We will also work with companies that provide software to the NHS and social care to ensure that the eFI+ is made available at no extra cost._x000D_ _x000D_ Our research to develop the eFI+ will enable identification of older people with moderate or severe frailty who are most likely to benefit from treatments to improve health and wellbeing. 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.

Lay abstract: Research questions_x000D_ i) How should eFI components be combined with additional routine primary care data to develop prognostic models for predicting key outcomes of requirement for home care, falls/fractures, nursing home admission and mortality in older people with moderate or severe frailty?_x000D_ _x000D_ ii) Can model predictive performance be improved through addition of data from measures that are practical for primary care use, but not available in routine data?_x000D_ _x000D_ iii) How should risk predictions from the prognostic models be translated into a decision analytic model (DAM) to guide clinical management?_x000D_ _x000D_ iv) What is the potential cost-effectiveness of implementing interventions targeted at subgroups of older people with frailty in routine NHS care?_x000D_ _x000D_ Background_x000D_ Lead applicant Clegg led the eFI development, validation and national implementation. This has been translated into major NHS policy change through inclusion in the 2017/18 GP contract, which supports frailty stratification using the eFI, and NHS Long Term Plan._x000D_ _x000D_ Aim_x000D_ To develop and evaluate the eFI+, a prognostic tool supplementing the original eFI including 4 integrated prognostic-decision models. The eFI+ will stratify older people with moderate or severe frailty into subgroups most likely to benefit from key interventions (community rehabilitation; falls prevention; comprehensive geriatric assessment; advance care planning)._x000D_ _x000D_ Methods_x000D_ Design_x000D_ Prognostic model development, internal validation and external validation using large datasets (ResearchOne, SAIL databank, Leeds Data Model) and cohort study data (CARE75+), with linked DAM and health economic analysis._x000D_ _x000D_ Population_x000D_ Patients 65+ with moderate or severe frailty, defined by the existing eFI._x000D_ _x000D_ Key outcomes_x000D_ 12-month outcomes for prognostic models:_x000D_ • New/increased home care package_x000D_ • ED attendance/hospitalisation with fall/fracture_x000D_ • Nursing home admission_x000D_ • All-cause mortality_x000D_ _x000D_ Statistical methods_x000D_ i) Prognostic modelling_x000D_ We will build 4 separate prognostic models for our 4 key outcomes by combining the eFI with additional individual-level routine data, informed by reviews to identify prognostic factors. Each model will be developed and internally validated in one large dataset, to adjust for potential overfitting, with subsequent external validation of predictive performance in a second large dataset._x000D_ _x000D_ Separately, we will use CARE75+ (n=1,200) to investigate additional predictive value of clinical measures practical for primary care (e.g. gait speed, activities of daily living, loneliness)._x000D_ _x000D_ ii) Decision analytic model (DAM)_x000D_ We will translate the prognostic models into a framework to support clinical decision-making, in co-production with stakeholders/PPI. We will integrate prognostic models with effect size estimates from systematic reviews/meta-analyses to identify relevant thresholds of predicted risk, above which implementation of our key interventions would be warranted._x000D_ _x000D_ iii) Health economic evaluation_x000D_ 12-month and long-term cost effectiveness models will be developed, informed by the DAM._x000D_ _x000D_ Timelines_x000D_ -6m-0m: Obtain datasets_x000D_ 1-5m: Data preparation, imputation_x000D_ 6-23m: Prognostic model development, internal validation, external validation; economic model development_x000D_ 24-29m: DAM development; examination of net-benefit/cost-effectiveness_x000D_ 30-32m: Write-up/dissemination_x000D_ _x000D_ Impact_x000D_ Potential for major impact, building on our track record of eFI national implementation and NHS policy impact.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: GENERIC HEALTH RELEVANCE