A Stanford University-led team of scientists has developed a machine learning tool that can analyse electronic healthcare records (EHR) to identify individuals who are likely to have familial hypercholesterolemia (FH), an underdiagnosed genetic cause of elevated low-density lipoprotein (LDL) cholesterol, which puts patients at a 20-fold increased risk of coronary artery disease. In separate test runs the classifier, described today in npj Digital Medicine, correctly identified more than 80% of cases--its positive predictive value (PPV)--and demonstrated 99% specificity. The team says the classifier could help to flag up patients who are most likely to have FH, so that they and their families can undergo further genetic testing. "Theoretically, when someone comes into the clinic with high cholesterol or heart disease, we would run this algorithm," said Nigam Shah, MBBS, PhD, Stanford University associate professor of medicine and biomedical data science. "If they're flagged, it means there's an 80% chance that they have FH. Those few individuals could then get sequenced to confirm the diagnosis and could start an LDL-lowering treatment right away."

Using large healthcare encounter datasets, a machine learning algorithm is able to identify patients with a common genetic disorder that carries a high risk for early heart attacks and strokes. While individuals with familial hypercholesterolaemia (FH) have 20 times the risk of developing cardiovascular disease than the general population, fewer than 10 percent of the 1.3 million Americans born with the genetic disease are diagnosed. "People born with familial hypercholesterolemia develop cardiovascular damage by puberty, often culminating in early heart attacks or the need for surgery as young or middle-aged adults," says Katherine Wilemon, founder and CEO of the FH Foundation, a non-profit research and advocacy organization. "Since diagnosis of this deadly but treatable condition has stalled in the American medical system, the FH Foundation harnessed artificial intelligence and big data to accelerate identification of those most likely to have FH." In a new study, a machine learning model created by the FH Foundation successfully leveraged healthcare encounter databases to identify individuals with the genetic disorder.