Heart Failure
Conditions
Keywords
heart failure, Prediction model
Brief summary
Heart failure (HF) is increasingly common and associated with excess morbidity, mortality and healthcare costs. New medications are now available which can alter the disease trajectory and reduce clinical events. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Earlier identification and treatment of HF could reduce downstream healthcare impact, but predicting HF incidence is challenging due to the complexity and varying course of HF. The investigators will use routinely collected hospital-linked primary care data and focus on the use of artificial intelligence methods to develop and validate a prediction model for incident HF. Using clinical factors readily accessible in primary care, the investigators will provide a method for the identification of individuals in the community who are at risk of HF, as well as when incident HF will occur in those at risk, thus accelerating research assessing technologies for the improvement of risk prediction, and the targeting of high-risk individuals for preventive measures and screening.
Interventions
OTHERObservational - no intervention given
Observational - no intervention given
Sponsors
Japan Foundation for Aging and Health
University of Leeds
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
16 Years to 120 Years
Healthy volunteers
No
Inclusion criteria
1. Aged 16 years and older 2. No history of heart failure 3. A minimum of one year follow up
Exclusion criteria
\-
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To develop and validate a for predicting the risk of new onset HF | Between 2nd Jan 1998 and 28 Feb 2022 | Predictive factors will be identified using Read codes (diagnoses), All variables will be considered as potential predictors, and may include: 1. sociodemographic variables: age, sex, ethnicity, index of multiple deprivation; 2. lifestyle factors (e.g. smoking status, alcohol consumption); |
| To identify and quantify the magnitude of predictors of new onset HF | Between 2nd Jan 1998 and 28 Feb 2022 | The proposed model can extract informative risk factors from EHR data. Specifically we will fit multivariable Cox proportional hazard models with backwards elimination approach to retain predictors of incident HF within each prediction window. |
Countries
United Kingdom
Outcome results
None listed