Dr. John Robson, Reader in Primary Care at the Queen Mary University of London, said: “There is an urgent need for better methods to detect people who might have FH. We have demonstrated the FAMCAT algorithm can be applied to whole boroughs or cities, using the data we already have in the system to help find those undiagnosed cases.”
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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.
Source: Medindia
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