Miller School Researchers Developing a Clinical Predictive Algorithm for Sudden Cardiac Death

Reading Time: 3 minutes

Two University of Miami Miller School of Medicine researchers are developing a predictive algorithm to identify adults at high risk for sudden cardiac death (SCD).

“We are making significant progress toward building a life-saving screening tool for clinicians,” said Jeffrey J. Goldberger, M.D., M.B.A., chief of the Cardiovascular Division. “Our goal is to use the principles of precision medicine to treat those individuals with the highest risk for SCD. For instance, aggressive treatment of hypertension can decrease an individual’s risk as measured by our model.”

Dr. Goldberger is working closely with Leonardo Tamariz, M.D., M.P.H., professor of medicine, to refine earlier clinical models by incorporating de-identified patient electronic health records (EHRs) from the University of Miami Health System.  They are also looking at social determinants of health such as race, ethnicity, income and neighborhood to improve the predictive capability for SCD.

“Our population health studies indicate about 30,000 adults in Miami-Dade County are at risk for SCD, including a large number of minorities,” said Dr. Tamariz. “It isn’t possible to test them all for heart abnormalities, but our model is getting steadily better at identifying adults with the high-risk factors.”

Screening Can Save Lives

About 50 percent of victims of sudden cardiac death have no symptoms of underlying cardiovascular disease, but currently there is no practical method for screening the millions of Americans who might have an undiagnosed heart disorder.

Supported by a pilot grant from U54 Precision Medicine collaborative, Drs. Goldberger and Tamariz have already examined a sample of high-risk asymptomatic individuals using cardiac stress tests and magnetic resonance imaging (MRI). “We were surprised to find that two-thirds of these patients had either myocardial fibrosis or another predisposition for sudden death,” said Dr. Tamariz.

The two researchers are also searching for biomarkers and examining genetic factors to further enhance the predictive algorithm. The Miller School has a partnership with Vanderbilt University that includes a shared genetics database of more than 155,000 patients, according to Dr. Tamariz.

Developing a model

Dr. Goldberger began working on a predictive algorithm at Northwestern University before joining the Miller School in 2015.  With his colleagues, Dr. Goldberger developed a predictive model using variables of age, sex, total cholesterol, lipid-lowering and hypertension medication use, blood pressure, smoking status, diabetes, and body mass index.  The study, “Simple Community-Based Risk-Prediction Score for Sudden Cardiac Death” in the American Journal of Medicine, found the majority of sudden cardiac deaths occurred in the highest 20 percent of predicted risk.

More recently, Dr. Goldberger was co-author of a study, “Sudden Cardiac Death Risk Distribution in the United States Population,” published in the American Journal of Cardiology in January 2019.  Drawing on 10 years of data from participants in the National Health and Nutrition Examination Survey (NHANES) and the Atherosclerosis Risk in Communities (ARIC) study, Dr. Goldberger and his co-authors found the majority of SCD risk appears to occur in 10 to 20 percent of U.S. adults free of cardiovascular disease.

“Over the years, we have found that this predictive model has been very effective in identifying individuals at low, moderate, and high risk for SCD,” said Dr. Goldberger. “Now, we are exploring the next steps to eventually bring this screening tool to clinicians in the U.S. and around the world.”

 

 

[recaptcha]