Future Innovations in Novel Detection of Heart Failure FIND-HF

NCT ID: NCT05756127

Last Updated: 2025-03-30

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 14000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-04-01
• Study Completion Date: 2025-12-31

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.

Conditions

Heart Failure

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: OTHER

Study Groups

All eligible patients

Observational cohort using anonymized patient-level primary care data linked to secondary administrative data; CPRD-GOLD and CPRD-AURUM.

Observational - no intervention given

Intervention Type: OTHER

Observational - no intervention given

Interventions

Observational - no intervention given

Observational - no intervention given

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Aged 16 years and older

2. No history of heart failure

3. A minimum of one year follow up

Exclusion Criteria

-

• Minimum Eligible Age: 16 Years
• Maximum Eligible Age: 120 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Japan Foundation for Aging and Health

OTHER

Sponsor Role: collaborator

University of Leeds

OTHER

Sponsor Role: lead

Responsible Party

Dr Christopher Gale

Professor of Cardiovascular Medicine

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Chris P Gale

Role: PRINCIPAL_INVESTIGATOR

University of Leeds

Locations

University of Leeds

Leeds, West Yorkshire, United Kingdom

Countries

United Kingdom

References

Nakao YM, Nadarajah R, Shuweihdi F, Nakao K, Fuat A, Moore J, Bates C, Wu J, Gale C. Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study. BMJ Open. 2024 Jan 22;14(1):e073455. doi: 10.1136/bmjopen-2023-073455.

Reference Type: DERIVED

PMID: 38253453 (View on PubMed)

Other Identifiers

FINDHF01

Identifier Type: -

Identifier Source: org_study_id