Future Innovations in Novel Detection of Heart Failure FIND-HF

Active, not recruiting

• Conditions: Heart Failure
• Interventions: Other: Observational - no intervention given

• Registration Number: NCT05756127
• Lead Sponsor: University of Leeds

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2023-01-31
• Study First Submitted QC: 2023-03-02
• Study First Posted: 2023-03-06
• Study Start: 2023-04-01
• Status Verified: 2025-03
• Last Update Submitted: 2025-03-25
• Last Update Posted: 2025-03-30
• Primary Completion: 2025-12
• Study Completion: 2025-12

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 14000

Inclusion Criteria

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

Exclusion Criteria

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
All eligible patients	Observational - no intervention given	Observational cohort using anonymized patient-level primary care data linked to secondary administrative data; CPRD-GOLD and CPRD-AURUM.

Primary Outcome Measures

Name	Time	Method
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.
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);

Trial Locations

Locations (1)

University of Leeds; 🇬🇧 Leeds, West Yorkshire, United Kingdom