To find people who have one such disease — familial hypercholesterolemia (FH), a condition that causes high levels of LDL cholesterol — biomedical data specialist Nigam Shah, MBBS, PhD, and cardiologist Joshua Knowles, MD, PhD, are applying the powers of big data. Their work has been called a prime example of precision health.
A recent feature by FiveThirtyEight explains their work:
They started by identifying about 120 people known to have FH (true positives) from Stanford’s network of hospitals and doctors’ offices, and some people with high LDL who don’t have the genetic disorder (true negatives). Shah then began to train a computer to spot people with FH by letting it look through those patients’ files and to identify patterns in things like cholesterol levels, age, and the medicine patients were prescribed. The researchers then deployed this algorithm to look for undiagnosed FH within Stanford’s health records.
Using medical billing and lab data, the FH Foundation — Knowles is its chief medical officer — has developed a map to highlight the frequency of FH cases in the United States. Though imprecise, the map is intriguing, showing the condition is clustered on the East Coast, with a few notable exceptions such as a dense patch in eastern Oregon.
These efforts could improve current screening methods and allow affected families to obtain treatment and make life-extending changes in their diet and exercise patterns, the article states.
Previously: Big data used to help identify patients at risk of deadly high-cholesterol disorder, Could patients’ knowledge of their DNA lead to better outcomes? and Push-button personalized treatment guidance for patients not covered by clinical-trial results
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