Heart Failure
Conditions
Brief summary
Heart failure represents a growing public health problem within the UK and particularly within the North West of England. A major challenge is that heart failure is currently diagnosed too late. The researchers have previously developed a risk calculator that accurately identifies individuals at risk of heart failure admission or death before they have developed heart failure. Most risk calculators are never implemented into clinical practice. The researchers will l perform a pilot study to evaluate the risk calculator within primary care in Greater Manchester.
Detailed description
The researchers have previously developed and externally validated a novel multimodal risk calculator that accurately identifies individuals at-risk of heart failure admission or death before they have developed heart failure. This risk calculator includes key co-morbidities, circulating biomarkers and cardiac magnetic resonance imaging (CMR) measurements of cardiac structure and function. It identifies those individuals at highest risk of developing heart failure and therefore those who may most benefit from targeted cardiometabolic therapeutics in the future. The researchers will l perform a pilot study to evaluate the risk calculator within primary care in Greater Manchester. A qualitative and quantitative assessment of risk calculator uptake will be performed within local GP practices and primary care populations. The research team will determine how effectively they can recruit participants from socioeconomically deprived and ethnically diverse backgrounds. A preliminary analysis will be performed to determine risk calculator accuracy within a prospective primary care cohort, and dynamically refine the model aiming to improve performance. The study will also involve conducting an initial cost effectiveness analysis to determine the real-world economic impact of model implementation.
Interventions
OTHERNo intervention
No intervention is applied to participants. The risk calculator score is not released to the participants or care providers. Participants are followed up for risk calculator accuracy and recalibration.
Sponsors
Manchester University NHS Foundation Trust
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to No maximum
Inclusion criteria
* Written informed consent * Aged 50 and over * Two or more of the following conditions: type 2 diabetes, chronic obstructive pulmonary disease, ischaemic heart disease, atrial fibrillation, hypertension, chronic kidney disease stage 3, body mass index ≥ 30 kg/m2
Exclusion criteria
* Established diagnosis of one or more of the following: heart failure, cardiomyopathy, moderate or severe valvular heart disease, congenital heart disease, heart transplant, idiopathic, heritable or drug-induced pulmonary arterial hypertension, any medical condition, which in the opinion of the Investigator, may place the patient at higher risk from his/her participation in the study, or is likely to prevent the patient from complying with the requirements of the study or completing the study. * Contraindication to cardiovascular magnetic resonance (CMR) scanning, including pacemaker, defibrillator, intraocular metal, intracranial aneurysm clips, severe claustrophobia, estimated glomerular filtration rate \< 30 ml/min/1.73m2, previous severe allergic reaction or anaphylaxis to gadolinium-based contrast agent, pregnancy or breastfeeding.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Preliminary measures of risk calculator validation and accuracy in Greater Manchester | 5 years | Risk calculator will predict incident heart failure, first heart failure hospitalisation, cardiovascular death and all cause death |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Proportion of participants recruited from socioeconomically deprived and ethnically diverse backgrounds | 5 years | Ability to recruit participants from socioeconomically deprived and ethnically diverse backgrounds within Greater Manchester |
| Measures of prognostic model calibration and discrimination in a primary care population | 5 years | Examples include calibration slope, intercept and Harrell's C statistic |
| Qualitative measures of primary care uptake and engagement | 5 years | Number of participant identification sites, methods of participant identification, proportion of eligible participants contacted and recruited |
| Causal statistical analysis to determine effect of hypothetical interventions | 5 years | Mediation analysis to determine causative effects of hypothetical interventions |
| Measures of cost effectiveness of the model in Greater Manchester | 5 years | Examples include decision curve analysis |
| Measures of prognostic model optimisation and accuracy with iterative variable inclusion or exclusion | 5 years | Assess variable inclusion and exclusion using stepwise model selection and Wald statistic |
Countries
United Kingdom
Outcome results
None listed