Researchers at the Queen Mary University of London developed and tested an algorithm that detects familial hypercholesterolemia. They named the algorithm 'FAMCAT', designed exclusively to detect familial hypercholesterolemia in young people.
The study is an observational cross-sectional one and is based on the urban population.
Familial hypercholesterolemia is a condition in which low-density lipoprotein (LDL) levels elevate in the blood. The complications related to this disorder include atherosclerosis and heart disease at an early age. It is a genetic disorder that affects 320,000 people in the UK.
The researchers retrospectively applied the 'FAMCAT' (Familial Hypercholesterolemia Case Assertation Tool) algorithm to patients' data in primary care aged 18-65 years. The data included blood test results and family history, the indicators of likelihood to have FH.
Out of the 777,128 participants, 1.5%-3.1% individuals were likely to have FH. The algorithm generated a list of people who showed a higher likelihood to develop Familial Hypercholesterolemia. These people can be evaluated by General practitioners at first and then assessed by genetic testing.
"It is unclear whether the algorithm performs equally well at detecting FH in different ethnic groups. We are now planning further research with east London data to investigate this," John Robson added.