"As the Magerstadt Professor of Cardiovascular Epidemiology, Khan studies the epidemiology of risk for heart failure. Using population-based cohorts and large electronic health record data analyses, she performs mechanistic studies that may enhance risk prediction and identify novel therapeutic agents for the prevention and treatment of cardiovascular disease. Khan and her team have developed a tool to predict risk and prevent cardiovascular disease such as heart failure, stroke, arrhythmia, coronary artery disease and many other conditions."
"The tool called the Predicting Risk of Cardiovascular Disease EVENTs ( PREVENT) is a set of equations that could help healthcare providers more accurately identify patients who have higher cardiovascular disease (CVD) risk and enhance preventive care efforts. PREVENT can be thought of as an umbrella of equations from which risk calculators have been developed, including a 10-year risk assessment in broad populations, a 30-year risk assessment using percentiles and a "heart age" assessment."
PREVENT is a set of risk-equation tools designed to predict and help prevent cardiovascular disease, including heart failure, stroke, arrhythmia, and coronary artery disease. The toolkit includes 10-year absolute risk calculations, 30-year percentile risk assessments, and a "heart age" estimate. The calculators use population-based cohorts and large electronic health record analyses and incorporate mechanistic study insights to enhance risk prediction and identify potential therapeutic targets. A Nature Medicine study, developed in partnership with the American Heart Association, demonstrated that PREVENT estimates CVD risk in diverse cohorts more accurately than the current Pooled Cohort Equations. Online implementation aims to present risks in digestible, actionable formats for patients and clinicians.
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