Development and Evaluation of the Electronic Frailty Index+ (eFI+)

NCT04113174 | Unknown

• 1 other identifier: NIHR127905
• Study Type: observational
• Target: 1,000,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

Research questions i) How should electronic frailty index (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? 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? iii) How should risk predictions from the prognostic models be translated into a decision analytic model (DAM) to guide clinical management? iv) What is the potential cost-effectiveness of implementing interventions targeted at subgroups of older people with frailty in routine NHS care? Background Lead applicant Clegg led the eFI development, validation and national implementation. This has been translated into major UK health policy change through inclusion in the 2017/18 GP contract, which supports frailty stratification using the eFI, and UK National Health Service Long Term Plan. Aim 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). Methods Design 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. Population Patients ≥65 with moderate or severe frailty, defined by the existing eFI. Key outcomes 12-month outcomes for prognostic models:

• New/increased home care package
• Emergency Department (ED) attendance/hospitalisation with fall/fracture
• Nursing home admission
• All-cause mortality Statistical methods i) Prognostic modelling The investigators 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. Separately, the investigators 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). ii) Decision analytic model (DAM) The investigators will translate the prognostic models into a framework to support clinical decision-making, in co-production with stakeholders/PPI. The investigators 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. iii) Health economic evaluation 12-month and long-term cost effectiveness models will be developed, informed by the DAM.

Conditions

Frailty

Outcome Measures

Primary Outcomes (4)

• Number of participants admitted to nursing homes

  Incidence of new admission to a nursing home, identified by new nursing home residence in the routine dataset, based on address data

  12 months

• Number of participants requiring new/increased home care package

  Incidence of new or increased home care services, identified by coded evidence of new or increased use of home care services in the routine dataset

  12 months

• Number of participants experiencing emergency department (ED) attendance/hospitalisation with fall/fracture

  Incidence of emergency department (ED) attendance or hospitalisation with fall or fracture, identified using coded evidence of ED attendeance/hospitalisation with fall/fracture in the routine dataset

  12 months

• All-cause mortality

  Incidence of all-cause mortality, defined using Office for National Statistics death data, or coded evidence of death in the routine dataset

  12 months

Study Arms (4)

SAIL databank

Anonymised records from around 5 million people in Wales, with linked primary care, ED attendance, hospital admissions, outpatient data, social care, Welsh Care Homes Dataset, and ONS mortality data. SAIL includes eFI summary scores and individual components.

Other: Decision Analytic Modelling

ResearchOne

Nationally representative, de-identified data from around 6 million primary care electronic health records on the TPP SystmOne clinical system. ResearchOne includes eFI summary scores and individual components.

Other: Decision Analytic Modelling

Leeds Data Model

Anonymised, linked primary, secondary, community and social care data from 810,000 patients across 108 practices in Leeds, including eFI summary scores and individual components.

Other: Decision Analytic Modelling

CARE75+

National prospective cohort study (n≈1,200) collecting detailed sociodemographic information, frailty measures (including eFI scores), simple instruments suitable for use in primary care (e.g. gait speed, timed-up-and-go test; activities of daily living; informal care; loneliness), and key outcomes at six, 12, 24 and 48 months. CARE75+ is a very rich dataset that provides a highly efficient method to investigate how simple instruments might augment eFI performance.

Other: Decision Analytic Modelling

Interventions

Decision Analytic Modelling OTHER

Prognostic models will be translated into a framework to guide clinical decision making by identifying relevant thresholds of predicted risk, above which implementation of our stated interventions is warranted.

CARE75+; Leeds Data Model; ResearchOne; SAIL databank

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

SAIL databank: Anonymised records from around 5 million people in Wales, with linked primary care, ED attendance, hospital admissions, outpatient data, social care, Welsh Care Homes Dataset, and ONS mortality data. ResearchOne: Nationally representative, de-identified data from around 6 million primary care electronic health records on the TPP SystmOne clinical system. Leeds Data Model: Anonymised, linked primary, secondary, community and social care data from 810,000 patients across 108 practices in Leeds, UK. CARE75+: National UK prospective cohort study (n≈1,200), with key outcomes at six, 12, 24 and 48 months.

You may qualify if:

• Age ≥65 years
• Moderate frailty (eFI score 0.24 to 0.36) or severe frailty (eFI score \>0.36)
• Registered with a ResearchOne, SAIL or LDM practice on 1st April 2018
• CARE75+ participants with moderate frailty (eFI score 0.24 to 0.36) or severe frailty (eFI score \>0.36)

You may not qualify if:

• Age \<65 years
• Fit/mild frailty

Sponsors & Collaborators

• University of Leeds: lead
• National Institute for Health Research, United Kingdom: collaborator
• Keele University: collaborator
• University of Leicester: collaborator
• University College, London: collaborator
• Swansea University: collaborator
• University of Exeter: collaborator
• Bradford Teaching Hospitals NHS Foundation Trust: collaborator
• NHS Bradford Districts Clinical Commissioning Group: collaborator
• University of Nottingham: collaborator

MeSH Terms

Conditions

Frailty

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Andrew Clegg, MD

  University of Leeds

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: September 2, 2019
• First Posted: October 2, 2019
• Study Start: January 1, 2015
• Primary Completion: May 1, 2022
• Study Completion: May 1, 2022
• Last Updated: October 25, 2019

Record last verified: 2019-10

Data Sharing

• IPD Sharing: Will not share