Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults.

BACKGROUND: Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year. METHODS: Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups. RESULTS: The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration. CONCLUSION: The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.

PeRsOnaliSed care Planning for oldER people with frailty (PROSPER): protocol for a randomised controlled trial.

BACKGROUND: Frailty is common in older age and is characterised by loss of biological reserves across multiple organ systems. These changes associated with frailty mean older people can be vulnerable to sudden, dramatic changes in health because of relatively small problems. Older people with frailty are at increased risk of adverse outcomes including disability, hospitalisation, and care home admission, with associated reduction in quality of life and increased NHS and social care costs. Personalised Care Planning offers an anticipatory, preventative approach to supporting older adults to live independently for longer, but it has not been robustly evaluated in a population of older adults with frailty. METHODS: Following an initial feasibility study, this multi-centre, individually randomised controlled trial aims to establish whether personalised care planning for older people improves health-related quality of life. It will recruit 1337 participants from general practices across Yorkshire and Humber and Mid-Mersey in the North of England. Eligible patients will be aged 65 and over with an electronic frailty index score of 0.21 or above, living in their own homes, without severe cognitive impairment and not in receipt of end-of-life care. Following confirmation of eligibility, informed consent and baseline data collection, participants will be individually randomised to the PeRsOnaliSed care Planning for oldER people with frailty (PROSPER) intervention or usual care in a 2.6:1 allocation ratio. Participants will not be blinded to allocation, but data collection and analysis will be blinded. The intervention will be delivered over 12 weeks by a Personal Independence Co-ordinator worker based within a voluntary sector organisation, Age UK. The primary outcomes are health-related quality of life, measured using both the physical and mental components of the Short-Form 12 Item Health Questionnaire at 12 months after randomisation. Secondary outcomes comprise activities of daily living, self-management capabilities and loneliness, admission to care homes, hospitalisations, and health and social care resource use at 12 months post randomisation. Parallel cost-effectiveness and process evaluations will be conducted alongside the trial. DISCUSSION: The PROSPER study will evaluate the effectiveness and cost-effectiveness of a personalised care planning approach for older people with frailty and inform the process of its implementation. TRIAL REGISTRATION: ISRCTN16123291 .  Registered on  28 August 2020.

Dementia prevention and the GP's role: a qualitative interview study.

BACKGROUND: GPs play an increasingly important role in proactively preventing dementia. Dementia in 40% of patients could be prevented or delayed by targeting 12 modifiable risk factors throughout life. However, little is known about how GPs perceive their role in dementia prevention and the associated barriers. AIM: To explore the role of GPs in dementia prevention. DESIGN AND SETTING: Qualitative study among UK GPs. METHOD: Semi-structured online interviews were conducted with 11 UK GPs exploring their views regarding their role in dementia prevention. Data were analysed using thematic analysis. RESULTS: GPs reported that they never explicitly discuss dementia risk with patients, even when patients are presenting with risk factors, but acknowledge that dementia prevention should be part of their role. They advocate for adopting a whole team approach to primary care preventive practice, using long-term condition/medication reviews or NHS health checks as a platform to enable dementia risk communication targeting already at-risk individuals. Barriers included a lack of time and an absence of knowledge and education about the modifiable dementia risk factors, as well as a reluctance to use 'dementia' as a term within the appointment for fear of causing health anxiety. 'Brain health' was perceived as offering a more encouraging discursive tool for primary care practitioners, supporting communication and behaviour change. CONCLUSION: There needs to be a whole-systems shift towards prioritising brain health and supporting primary care professionals in their preventive role. Education is key to underpinning this role in dementia prevention.

Social care costs for community-dwelling older people living with frailty.

International evidence indicates that older people with frailty are more likely to access social care services, compared to nonfrail older people. There is, however, no robust evidence on costs of social care provided for community-dwelling older people living with frailty in their own homes. The main objective of this study was to examine the relationship between community-dwelling older people living with frailty, defined using the cumulative deficit model, and annual formal social care costs for the 2012-2018 period. A secondary objective was to estimate formal social care spending for every 1% reduction in the number of older people who develop frailty over 1 year. Secondary analysis of prospective cohort data from two large nationally representative community-based cohort studies in England was performed. Respondents aged ≥75 were used in the main analysis and respondents aged 65-74 in sensitivity testing. We used regression tree modelling for formal social care cost analysis including frailty, age, gender, age at completing education and living with partner as key covariates. We employed a minimum node size stopping criteria to limit tree complexity and overfitting and applied 'bootstrap aggregating' to improve robustness. We assessed the impact of an intervention for every 1% decrease in the number of individuals who become frail over 1 year in England. Results show that frailty is the strongest predictor of formal social care costs. Mean social care costs for people who are not frail are £321, compared with £2,895 for individuals with frailty. For every 1% of nonfrail people not transitioning to frailty savings of £4.4 million in annual expenditures on formal social care in England are expected, not including expenditure on care homes. Given considerably higher costs for individuals classed as frail compared to nonfrail, a successful intervention avoiding or postponing the onset of frailty has the potential to considerably reduce social care costs.

Normative Estimates and Agreement Between 2 Measures of Health-Related Quality of Life in Older People With Frailty: Findings From the Community Ageing Research 75+ Cohort.

BACKGROUND: Previous studies have summarized evidence on health-related quality of life for older people, identifying a range of measures that have been validated, but have not sought to present results by degree of frailty. Furthermore, previous studies did not typically use quality-of-life measures that generate an overall health utility score. Health utility scores are a necessary component of quality-adjusted life-year calculations used to estimate the cost-effectiveness of interventions. METHODS: We calculated normative estimates in mean and standard deviation for EQ-5D-5L, short-form 36-item health questionnaire in frailty (SF-36), and short-form 6-dimension (SF-6D) for a range of established frailty models. We compared response distributions across dimensions of the measures and investigated agreement using Bland-Altman and interclass correlation techniques. RESULTS: The EQ-5D-5L, SF-36, and SF-6D scores decrease and their variability increases with advancing frailty. There is strong agreement between the EQ-5D-5L and SF-6D across the spectrum of frailty. Agreement is lower for people who are most frail, indicating that different components of the 2 instruments may have greater relevance for people with advancing frailty in later life. There is a greater risk of ceiling effects using the EQ-5D-5L rather than the SF-6D. CONCLUSIONS: We recommend the SF-36/SF-6D as an appropriate measure of health-related quality of life for clinical trials if fit older people are the planned target. In trials of interventions involving older people with increasing frailty, we recommend that both the EQ-5D-5L and SF36/SF6D are included, and are used in sensitivity analyses as part of cost-effectiveness evaluation.

Study protocol for a cluster randomised controlled feasibility trial evaluating personalised care planning for older people with frailty: PROSPER V2 27/11/18.

BACKGROUND: Frailty is characterised by increased vulnerability to falls, disability, hospitalisation and care home admission. However, it is relatively reversible in the early stages. Older people living with frailty often have multiple health and social issues which are difficult to address but could benefit from proactive, person-centred care. Personalised care planning aims to improve outcomes through better self-management, care coordination and access to community resources. METHODS: This feasibility cluster randomised controlled trial aims to recruit 400 participants from 11 general practice clusters across Bradford and Leeds in the north of England. Eligible patients will be aged over 65 with an electronic frailty index score of 0.21 (identified via their electronic health record), living in their own homes, without severe cognitive impairment and not in receipt of end of life care. After screening for eligible patients, a restricted 1:1 cluster-level randomisation will be used to allocate practices to the PROSPER intervention, which will be delivered over 12 weeks by a personal independence co-ordinator worker, or usual care. Following initial consent, participants will complete a baseline questionnaire in their own home including measures of health-related quality of life, activities of daily living, depression and health and social care resource use. Follow-up will be at six and 12 months. Feasibility outcomes relate to progression criteria based around recruitment, intervention delivery, retention and follow-up. An embedded process evaluation will contribute to iterative intervention optimisation and logic model development by examining staff training, intervention implementation and contextual factors influencing delivery and uptake of the intervention. DISCUSSION: Whilst personalised care planning can improve outcomes in long-term conditions, implementation in routine settings is poor. We will evaluate the feasibility of conducting a cluster randomised controlled trial of personalised care planning in a community population based on frailty status. Key objectives will be to test fidelity of trial design, gather data to refine sample size calculation for the planned definitive trial, optimise data collection processes and optimise the intervention including training and delivery. TRIAL REGISTRATION: ISRCTN12363970 - 08/11/18.