Approximately one in 200 to 500 people worldwide suffer from familial hypercholesterolemia (FH), an under-diagnosed cardiovascular disorder. Patients with FH have high levels of low-density lipoprotein (LDL) cholesterol in their blood. This “bad” cholesterol causes plaque on arterial walls, often resulting in blood clots, blocked arteries, heart attacks, stroke, and even premature death. Men with FH face a 50 percent chance of having a heart attack by age 50, and women have a 30 percent chance by age 60.
While preliminary treatment has the potential to significantly reduce these odds, most people with FH don’t know they have it; estimates indicate that only 10 percent of cases are diagnosed. The average age of diagnosis is 47, often after a heart attack or other cardiac complication.
Researchers at Stanford University’s School of Medicine are developing an algorithm to identify patients with undiagnosed FH. Cardiologist Joshua Knowles and Nigam Shah, a biomedical informatics professor, are leading the effort. They accessed the health records of 120 Stanford patients with FH and a pool of patients with high LDL levels who do not have FH – true positives and true negatives, respectively. Using machine learning, big data, and advanced software, the researchers are training a computer to recognize patterns based on the medical records of true positives. The resulting algorithm, which analyzes and categorizes patterns in age, cholesterol levels, and prescriptions, will scan Stanford’s health records for undiagnosed cases of FH. (more…)