Five of the study co-authors are, from left, Dean Pascal J. Goldschmidt, Sylvia Daunert, Victor Andreev, Trajen Head, and Sapna Deo.

A multidisciplinary team of University of Miami researchers has developed a novel prediction model for sudden cardiac death that has the potential to substantially reduce the number of people who abruptly lose consciousness and die shortly after experiencing acute symptoms of cardiovascular disease.

Led by Miller School Dean Pascal J. Goldschmidt and Sylvia Daunert, professor and Lucille P. Markey Chair of Biochemistry and Molecular Biology, the team included Victor P. Andreev, associate professor of psychiatry and behavioral sciences, Sapna K. Deo, associate professor of biochemistry and molecular biology, Trajen Head, research assistant in the Department of Biochemistry and Molecular Biology, and Neil Johnson, professor of physics.

A sixth member of the team, physicist Neil Johnson developed the mathematical equation to model the growth of plaque.

SCD-Predict, the discrete simulation model they developed, was presented in a study published May 7 by Nature Scientific Reports, the Nature Publishing Group’s online publication dedicated to the rapid review and publication of original research. With simple mathematical computations, SCD-Predict simulates sudden cardiac death (SCD) by unraveling and quantifying the complicated chain of events associated with the formation, growth, and rupture of atheroma plaques—which account for 80 percent of sudden cardiac deaths—and the subsequent formation of blood clots and onset of arrhythmias.

In the study, “Discrete Event Simulation Model of Sudden Cardiac Death Predicts High Impact of Preventive Interventions,” the researchers demonstrated a correlation between a high number of plaques and sudden cardiac death in a simulated population and, in the most compelling finding, reduced the number of sudden cardiac deaths in that virtual population by eight fold with simple interventions. They also increased longevity by as much as two decades by identifying people with multiple lesions early and initiating treatment with preventive measures by the age of 45.

“SCD is responsible for about 250,000 deaths in the United States per year. It is a major problem for our fellow humans precisely because it is so sudden and the opportunity to intervene is often limited,” said Dean Goldschmidt, who conceived the idea for SCD-Predict with Daunert. “This model actually gives you a single window into the blood vessel of an individual and allows you to assess, based on the number of plaques, where they are on the curve for and their risk of sudden cardiac death.”

The advantages of the model, the researchers said, are its ability to predict the optimal method and time window for intervening early to avert SCD and increase longevity and its ability to integrate multiscale data from pathological, epidemiological, and clinical studies. Goldschmidt and his collaborators envision the day clinicians could use a refined version of the model, perhaps in a handheld app, as a tool for deciding on treatment and prevention strategies that would save lives and reduce the average $286 billion annual cost of sudden cardiac death in the U.S.

“What we observed is the acceleration of the process that gives people cardiovascular disease and leads to their sudden death,” Goldschmidt said. “At the time they die, their coronary vessels look like they belong to someone way older, so it looks like these individuals are on the ascending curve of atheroma formation that is different from the rest of the population. What we want to do is try to refine a reasonable mechanism to access the presence of lesions in blood vessels and then provide a much more accurate prediction model for that one individual, and derive management of the patient accordingly.”

A widely known cardiologist, Goldschmidt can count too many friends and patients who have died from sudden cardiac death. After the loss of another such friend, he and Daunert assembled the team, which created, populated, and validated the model with other pathological, epidemiological, and clinical studies.

“Everybody has plaque, even children, but the only studies we have to know the extent of the damage it causes are autopsies,” Daunert said. “So the idea was to see if we could develop a computer model that would predict, if an individual at a certain age has so many plaques, what their chances of dying are at a certain age, and what would happen if you take certain countermeasures. If you treat them with diet and exercise or medications, can you reduce the onset of sudden cardiac death and by how many years?”

Andreev, the first author on the study and a member of the Center for Computational Science, and Johnson, an expert on complex systems and how they respond to random events, designed the model with data that Head collected. Deo helped analyze and interpret the model’s output data.

The beauty of the discrete event simulation approach, Andreev and Johnson said, is its simplicity and applicability to other diseases. “It can be used to model any disease, not just sudden cardiac death, but any disease that involves complicated chains of events, like cancer,” Andreev said.

He wrote the code for the model based on the pathological study of just 50 deceased people that examined all three coronary arteries and counted the type and number of lesions present and whether they had ruptured or not. The researchers then used the progression of plaque, which Johnson modeled with a mathematical equation, to make predictions from epidemiological data of thousands of simulated people of different ages, numbers, and with different stages of lesions and many other factors.

Last, they validated the results from their simulated population by comparing them to the incidence of death from coronary heart disease in the Framingham Heart Study, the landmark study of residents of the town in Massachusetts that began in 1948.

“Although this was done in the context of sudden cardiac death, it is very much a blueprint on how you would model any disease,” Johnson said. “It’s like a car. If you could take it apart every so often, you could see where it might break down next. That’s impractical with cars, and impossible with people, but the model enables us to break down the disease in stages and make it more manageable.”