Artificial Intelligence Identifies Patients with Potentially Fatal Genetic Disease

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."