Future Innovations in Novel Detection of Heart Failure FIND-HF (FIND-HF)

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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Heart Failure
• Intervention / Treatment: Other: Observational - no intervention given

• Study Type: Observational
• Enrollment (Estimated): 14000

Contacts and Locations

Study Locations

• United Kingdom
  • West Yorkshire
    • Leeds, West Yorkshire, United Kingdom, LS2 9JT
      • University of Leeds

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 16 years to 120 years (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population will comprise all available patients in CPRD-GOLD who were eligible for data linkage and had at least 1-year follow-up in the period between 2nd Jan 1998 and 28th February 2022. The outcome of interest is the first diagnosed HF, and will be identified using Read codes (for the CPRD patient profile) and ICD-10 codes (for HES events). Patients with less than one year of registration in CPRD, those who are under eighteen years of age at the date of the first registration in CPRD, those who were diagnosed with HF before 2nd Jan 1998, and those who were not eligible for data linkage will be excluded.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
To develop and validate a for predicting the risk of new onset HF	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);	Between 2nd Jan 1998 and 28 Feb 2022
To identify and quantify the magnitude of predictors of new onset HF	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.	Between 2nd Jan 1998 and 28 Feb 2022

Collaborators and Investigators

• Sponsor: University of Leeds
• Collaborators: Japan Foundation for Aging and Health
• Investigators: Principal Investigator: Chris P Gale, University of Leeds

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2023
• Primary Completion (Estimated): December 1, 2025
• Study Completion (Estimated): December 1, 2025

Study Registration Dates

• First Submitted: January 31, 2023
• First Submitted That Met QC Criteria: March 2, 2023
• First Posted (Actual): March 6, 2023

Study Record Updates

• Last Update Posted (Actual): March 30, 2025
• Last Update Submitted That Met QC Criteria: March 25, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Keywords: heart failure; Prediction model
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Heart Diseases; Heart Failure
• Other Study ID Numbers: FINDHF01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No