Researchers from the Johns Hopkins Bloomberg School of Public Health have developed a single “universal risk prediction model” for cardiovascular disease that, in initial tests, works well for patients who already have cardiovascular disease as well as patients who do not but who may be at risk for developing it. Clinicians currently use two separate risk models to assess patients’ chances of having heart attacks, strokes, and other major cardiovascular events.
The new model uses a set of 10 factors including age, cigarette smoking status, diabetes status, and blood levels of several cardiac biomarkers, to gauge the risk of a new cardiovascular event, regardless of whether the patient has had one before. The researchers used a dataset covering nearly 10,000 participants over two decades to develop the new model, and a separate dataset to validate its accuracy.
The new model, which ultimately could change how cardiovascular disease risk is assessed in doctors’ offices around the world, is described in a paper published online January 29 in the Journal of the American College of Cardiology. Parameters of the new model are available in the paper supplements.
The study’s first author was Yejin Mok, PhD, MPH, a research associate who is also in the Bloomberg School’s Department of Epidemiology.
In the study, the researchers examined the performance of established primary-prevention risk predictors in an existing dataset covering 9,138 participants from a long-term study called the Atherosclerosis Risk in Communities study (ARIC). The ARIC participants included some cases with established atherosclerotic cardiovascular disease and some without.
The scientists calculated the statistical links between these predictors and new major cardiovascular-related events—heart attacks, strokes, and heart failure—over a median follow-up period of 19.5 years. They determined that the new universal model performed well, and in most cases was not significantly different for participants who started out with cardiovascular disease compared to those who started out without it.
The researchers also used the ARIC dataset to identify the new universal risk model’s set of 10 strongly predictive and measurable factors that include age, smoking status, diabetes status, and three blood biomarkers.
The model predicted future risk accurately, the researchers found, and its risk predictions suggested a substantial overlap in risk levels between participants with cardiovascular disease and those without it.
Using a dataset from a different study, the Multi-Ethnic Study of Atherosclerosis, the team validated the accuracy of their model and overall findings.
Johns Hopkins Bloomberg School of Public Health release on Newswise
