Derivation of a machine learning (ML) model to improve pre-test probability of identifying individuals with genetic variants linked to familial hypercholesterolemia (FH).
Approved Research ID: 67789
Approval date: January 27th 2021
Familial hypercholesterolaemia ("FH") is an inherited disease caused by variations in genes related to the clearance of LDL-cholesterol (the so-called "bad cholesterol"). As a result, FH increases the levels of LDL-cholesterol from birth within the blood stream. Over time, this continuous exposure to high LDL-cholesterol results in fat deposits ("atherosclerotic plaques") within the arteries, generally referred to as atherosclerosis. When these plaques become large and/or unstable, they can slow down the blood flow or generate blood clots obstructing the blood flow, causing heart disease or an acute heart attack. Cholesterol-lowering medications decrease levels of LDL-cholesterol, and, when administered early using effective doses, can prevent the development of atherosclerosis and heart diseases/attacks.
FH affects approximately 1 in every 311 individuals but is underdiagnosed, with less than 7% currently identified in the UK. Genetic testing is the most accurate tool for diagnosing FH, but it is expensive and not available everywhere due to a lack of resources. Diagnostic tools called clinical criteria are often used instead of genetic testing in daily clinical practice. These tools use patients' characteristics including cholesterol levels, age at onset of heart diseases and family history, to make a diagnosis. Unfortunately, these tools might not work well in different populations and often fail to accurately identify FH patients.
With our research, we aim to help find FH patients using a branch of Artificial Intelligence called Machine Learning (ML). ML consists of a set of techniques that allow the replication a specific human behaviour involving reading large amounts of information and making predictions based on the data. ML models are computer software and mathematical models derived from the data that can differentiate between disease-free individuals and affected patients. We believe that ML models can better identify FH patients than clinical diagnostic tools currently used in clinical practices.
The performance of ML models will be compared to the performance of traditional clinical diagnostic tools. This will be done by counting the number of patients who have been misclassified by current diagnostic tools and newly derived ML models. If our ML models outperform clinical diagnostic tools, they will help identify more FH patients, on a national scale and earlier in life, ultimately allowing clinicians to treat more patients and help prevent heart disease. The present proposal over 3 years would be expected to substantially improve the current detection rates of <7% UK and <5% globally, in a cost-effective, scalable fashion.