The prognosis of the population with a new diagnosis of heart failure (HF) is poor, with 1 in 3 patients dying within 12 months of diagnosis, and this prognosis is as poor as some of the most life-threatening cancers. There is some evidence that like cancers, there may be time delays (of nearly 12 months) in a new HF diagnosis, which is often not made until an emergency admission to hospital. There are also pre-morbid disease factors, such as cardio-metabolic group of conditions and myocardial infarction, which are known to be associated with an increased risk of developing subsequent HF. Given that there are routinely collected clinical and care data across different care interfaces, between primary and hospital care, there is an opportunity to develop risk factor models for the earlier identification of HF.
Whilst some clinical and care factors are known to be at risk of future HF onset, which are identifiable through routinely recorded consultations, there remain delays in HF diagnosis. Furthermore, there have been no risk factor models been developed, which combine these factors, in the prediction of HF, especially in primary care or at the population level. The project will utilise the Clinical Practice Research Datalink (CPRD) which is a national epidemiological database that links primary care records to other data such as hospital and death data, to develop these risk models. The PhD is also linked to a broader informatics programme.