Future Innovations in Novel Detection of Heart Failure FIND-HF

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.

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Key information

Conditions

Heart Failure Cardiovascular Diseases Heart Diseases

Age range

16 year–120 year

Sex eligibility

All sexes

Study type

Observational

Primary location

University of Leeds

Leeds, West Yorkshire, LS2 9JT, United Kingdom

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Aged 16 years and older
No history of heart failure
A minimum of one year follow up

Exclusion criteria

Treatment and study plan

Observational - no intervention given

Other

Observational - no intervention given

Primary outcomes

To develop and validate a for predicting the risk of new onset HF

Time frame: 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:
- sociodemographic variables: age, sex, ethnicity, index of multiple deprivation;
- lifestyle factors (e.g. smoking status, alcohol consumption);
To identify and quantify the magnitude of predictors of new onset HF

Time frame: 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.

Sponsors and collaborators

Lead sponsor

University of Leeds

Other

Collaborators

Japan Foundation for Aging and Health

Registry information

Official study title

Predicting Incident Heart Failure from Population-based Nationwide Electronic Health Records: Protocol for a Model Development and Validation Study

Acronym: FIND-HF

Important dates

Study start: 2023
Primary completion: 2025
Study completion: 2025
First posted: Mar 6, 2023
Registry last updated: Mar 30, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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