Future Innovations in Novel Detection of Heart Failure FIND-HF

NCT05756127 | Active Not Recruiting

• 1 other identifier: FINDHF01
• Study Type: observational
• Target: 14,000
• Locations: 1 country
• Sites: 1

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

Keywords

heart failure; Prediction model

Outcome Measures

Primary Outcomes (2)

• 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: 1. sociodemographic variables: age, sex, ethnicity, index of multiple deprivation; 2. 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

Study Arms (1)

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

Interventions

Observational - no intervention given OTHER

Observational - no intervention given

All eligible patients

Eligibility Criteria

• Age: 16 Years - 120 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• Aged 16 years and older
• No history of heart failure
• A minimum of one year follow up

Sponsors & Collaborators

• University of Leeds: lead
• Japan Foundation for Aging and Health: collaborator

Study Sites (1)

University of Leeds

Leeds, West Yorkshire, LS2 9JT, United Kingdom

Location

Related Publications (1)

• 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.

  PMID: 38253453 DERIVED

MeSH Terms

Conditions

Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Study Officials

• Chris P Gale

  University of Leeds

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Cardiovascular Medicine

Study Record Dates

• First Submitted: January 31, 2023
• First Posted: March 6, 2023
• Study Start: April 1, 2023
• Primary Completion: December 1, 2025
• Study Completion: December 1, 2025
• Last Updated: March 30, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)