NCT05756127

Future Innovations in Novel Detection of Heart Failure FIND-HF

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

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 14,000 (estimated)
• Sponsor: University of Leeds · Academic / Other
• Sex: All
• Age: 16 Years – 120 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Heart Failure

Interventions

Type	Name	Description
OTHER	Observational - no intervention given	Observational - no intervention given

Timeline

• Start date: 2023-04-01
• Primary completion: 2025-12-01
• Completion: 2025-12-01
• First posted: 2023-03-06
• Last updated: 2025-03-30

Locations

1 site across 1 country: United Kingdom