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.

Inclusion Criteria:

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

Exclusion Criteria:

-

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.