Developing, translating and evaluating risk growth charts for chronic diseases and multimorbidities using population-wide electronic health records

Lead Research Organisation: University of Cambridge
Department Name: Public Health and Primary Care

Abstract

Multimorbidity describes the presence of two or more chronic health conditions. Multimorbidities are particularly relevant in the context of older adults: it is estimated that 80% of adults over the age of 80 have two or more chronic conditions, often including one or more cardiovascular diseases (CVD). The proposed research project will aim to develop risk growth charts modelling dynamic risk of developing diseases to predict multimorbidities using population-wide electronic health records (EHRs). By leveraging full medical histories of millions of participants we will be able monitor and calculate their risk of developing one or multiple chronic diseases across the patient life course.
EHRs are a type of data with unique advantages. With the modernisation and digitalisation of primary care, EHRs have become more readily available, with regular up-to-date addition of new data.Furthermore, EHRs include repeated measurements of clinical features, such as blood pressure and LDL and HDL cholesterol measurements, which can be used to predict an "estimated current value" which is thought to be a less error-prone than single measures of risk factors. Moreover, the availability of a full medical history allows us to observe the general trend of a patient's health.
By using population health data and electronic health records, including the repeated measurements of epidemiological risk factors, we aim to develop multimorbidities risk prediction models. The way different chronic conditions are interconnected will be investigated by observing at the overlap of multiple diseases and their severity in the population. Data will be sourced from the UK Biobank and NHS Digital. The CVD-COVID-UK Consortium can also be used to explore the relevance of COVID severity as a risk factor for CVD and how it is associated with known risk factors of CVD. CVD will be considered on both an individual scale (for example just investigating myocardial infarction) and on a disease group scale investigating multiple CVDs at a time (for example investigating myocardial infarction and strokes).
Various statistical approaches will be used to develop risk prediction models including regression, clustering and machine learning techniques. PhD training will include attending Machine Learning and Artificial Intelligence lectures from the University of Cambridge and attending a three-day course from Keele University: "Statistical Methods for Risk Prediction and Prognostic Models".
Towards later stages of the project, we plan to explore future prospects of combining EHRs and genomic data, in the form of polygenic risk scores, to predict cardiovascular disease risk.
In the future, multimorbidities risk prediction models based on EHRs can be used in clinical practices to monitor patients and their chronic diseases risk by considering their full medical history. Being able to acknowledge multiple chronic conditions at once will shift primary care from a disease-focused approach to considering the patient and their health as a whole. Risk prediction models will assist clinicians in detecting early signs of increased chronic diseases risk, prioritise which patients should undergo further risk assessments or screening and encourage lifestyle changes in populations at higher risk.

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