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.

NCT Number	NCT05756127
Lead Sponsor	University of Leeds Collaborators: Japan Foundation for Aging and Health
Conditions	Heart Failure
Enrollment	14,000 participants
Start Date	2023-04-01
Primary Completion	2025-12 (estimated)
Study Completion	2025-12 (estimated)
Updated on ClinicalTrials.gov	2025-03-30