Calculator Uses Predictive Analytics to Forecast Stroke Risk

August 18, 2020 - A predictive analytics tool can accurately forecast patients’ risk of ischemic stroke based on the severity of their metabolic syndrome, a collection of conditions that includes high blood pressure and abnormal cholesterol levels, according to a study published in Stroke.

Ischemic strokes occur when blood flow to the brain is obstructed by blood clots or clogged arteries. The condition has previously been associated with metabolic syndrome using dichotomous criteria, the researchers noted, but these criteria display racial and ethnic discrepancies.

Metabolic syndrome can consist of high blood pressure, abnormal cholesterol levels, and excess body fat around the abdomen and waist, the group said. Individuals with metabolic syndrome are more likely to develop several other diseases, including higher risk of heart attack, stroke, diabetes, and a liver condition known as fatty liver disease.

To further evaluate the relationship between ischemic stroke risk and metabolic syndrome, a team from University of Virginia Health System reviewed more than 13,000 participants in prior studies and their stroke outcomes. Among that group, there were 709 ischemic strokes in an average period of 18.6 years assessed in the studies.  

Researchers used a predictive analytics tool to calculate Z scores measuring the severity of metabolic syndrome among the study participants. Previous research had shown that the tool can effectively forecast coronary heart disease and diabetes, the team noted.

“We had previously shown that the severity of metabolic syndrome was linked to future coronary heart disease and type 2 diabetes,” said UVA’s Mark DeBoer, MD. “This study showed further links to future ischemic strokes.”

After analyzing the relationship between metabolic syndrome and ischemic stroke risk, researchers found that the subgroup with the highest association between the two conditions was white women. In this group, the team was able to identify relationships between individual contributors to metabolic syndrome, including high blood pressure and stroke risk.

The results also showed that race and sex didn’t make a major difference in overall stroke risk, and they believe that the increased risk observed in white women could be the result of chance alone. However, the findings are significant enough to warrant future studies on the impact of race and sex on stroke risk, the team stated.

The strong relationship between metabolic syndrome and stroke risk indicates that people with metabolic syndrome can make changes to reduce that risk – from exercising more to choosing healthier foods, researchers said.

The group expects that the tool, which is freely available online, will help providers guide patients in reducing their risk of stroke and improving their health and well-being.

“In case there are still individuals out there debating whether to start exercising or eating a healthier diet, this study provides another wake-up call to motivate us all toward lifestyle changes,” said DeBoer.

Researchers have previously applied predictive analytics tools to measure stroke risk in patients. A recent study funded by the National Institute on Minority Health and Health Disparities described a simple predictive risk model that could determine the likelihood of stroke in adult patients who have migraine with aura.

“People who have migraine with aura are at increased risk for an ischemic stroke,” said Souvik Sen, MD, MPH, study co-author, and professor and chair of the neurology department at the University of South Carolina School of Medicine in Columbia, South Carolina. “With our new risk-prediction tool, we could start identifying those at higher risk, treat their risk factors and lower their risk of stroke.”