Future Innovations in Novel Detection of Heart Failure FIND-HF

Study Overview
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Brief Summary
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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.

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

Conditions
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Heart Failure

Intervention / Treatment
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* Other: Observational - no intervention given


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Aged 16 years and older No history of heart failure A minimum of one year follow up Exclusion Criteria: -

Ages Eligible for Study
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Minimum Age: 16 Years
Maximum Age: 120 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

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

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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heart failure, Prediction model