Heart FailureFIND-HFNCT05756127
Future Innovations in Novel Detection of Heart Failure FIND-HF
An observational study of Heart Failure, run by University of Leeds at 1 site in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 16 to 120 years
- Conditions the study is for
- Heart Failure
- How many people
- 14,000 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Apr 2023to Dec 2025
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 to 120 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- Aged 16 years and older
- No history of heart failure
- A minimum of one year follow up
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
University of Leeds
Leeds, West Yorkshire, LS2 9JT
About this study
Official title: 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.
- Conditions
- Heart Failure
- Being tested
- Observational - no intervention given
- Keywords
- heart failure, Prediction model
- Medical subject headings
- Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Observational - no intervention givenOther
Observational - no intervention given
| Study group | Role | What they receive |
|---|---|---|
| All eligible patients | Observational cohort using anonymized patient-level primary care data linked to secondary administrative data; CPRD-GOLD and CPRD-AURUM. Receives: Other: Observational - no intervention given |
What is being measured
The primary outcome is the main question the study is designed to answer.
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);
Measured: 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.
Measured: Between 2nd Jan 1998 and 28 Feb 2022
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2023
- Study start1 Apr 2023
- Primary completionDec 2025 (estimated)
- Study completionDec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Heart FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 14,000 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- University of Leeds
- Sponsor type
- University, hospital or other
- Collaborators
- Japan Foundation for Aging and Health
- Responsible party
- Principal investigator, Dr Christopher Gale, University of Leeds
- Investigators
- Chris P Gale, Principal investigator, University of Leeds
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05756127
- Sponsor's protocol ID
- FINDHF01
- Acronym
- FIND-HF
- Status last verified
- Mar 2025
- First submitted
- 31 Jan 2023
- Last updated
- 30 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 38253453
Study at a glance
From the registry record, last updated by the sponsor on 30 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Incident Heart Failure from Population-based Nationwide Electronic Health Records: Protocol for a Model Development and Validation Study
- ClinicalTrials.gov ID
- NCT05756127
- Acronym / sponsor ID
- FIND-HF / FINDHF01
- Conditions
- Heart Failure
- Keywords
- heart failure, Prediction model
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 14,000 participants (estimated)
- Lead sponsor
- University of LeedsUniversity, hospital or other
- Collaborators
- Japan Foundation for Aging and Health
- Study start
- 1 Apr 2023 (actual)
- Primary completion
- Dec 2025 (estimated)
- Study completion
- Dec 2025 (estimated)
- First posted
- 6 Mar 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Other
- Target follow-up
- 1 Year
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| All eligible patients | – | Other: Observational - no intervention givenObservational cohort using anonymized patient-level primary care data linked to secondary administrative data; CPRD-GOLD and CPRD-AURUM. |
Who may qualify
- Ages
- 16 to 120 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Aged 16 years and older
- No history of heart failure
- A minimum of one year follow up
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Observational - no intervention givenObservational - no intervention given | Other | All eligible patients |
Primary outcome
To develop and validate a for predicting the risk of new onset HF
Time frame: 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);
To identify and quantify the magnitude of predictors of new onset HF
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Mar 2025.
- Derived from this study: PubMed 38253453Nakao 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Chris P Gale principal investigator
University of Leeds
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure, run by University of Leeds at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 30 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To develop and validate a for predicting the risk of new onset HF (time frame: Between 2nd Jan 1998 and 28 Feb 2022). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05756127, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Mar 2025.