Improving Risk Prediction for Chronic Disease Management 

For better chronic disease management, Boston University researchers recommend replacing the term “race” with underlying factors that indicate an increased risk for heart attacks and strokes. 

“If other factors (instead of race itself) determine the risk differences, then the prediction equations should incorporate those factors that cause the differences in predicted risk between the races, rather than race itself. If we do not change our prediction strategy, there is a risk of labeling (stereotyping) Black people as high risk purely based on the color of their skin,” corresponding author Vasan Ramachandran, MD, FACC, said in a press release.  

The American Heart Association/American College of Cardiology have created and endorsed equations that can be used to predict the risk that an individual will experience heart attacks or strokes over the next 10 years.  

Physicians can enter their patients’ data for seven risk factors including age, sex, race, values of blood pressure, cholesterol diabetes, and smoking status to determine their probability of developing heart or brain attacks within the next 10 years.  

In current prediction equations, Black and White patients with the same risk factor values will have different probabilities of developing heart attacks and strokes.  

“In these situations when the predicted risks are so different, doctors may treat their Black and white patients differently even when they have identical risk factors purely because of their race,” said Ramachandran, who also is a principal investigator and director of the Framingham Heart Study.  

The study team analyzed 50,000 potential risk factor combinations.  

“They asked, if Black and white patients had exactly the same (identical) risk factor combinations, by how much does the probability of heart and brain attacks predicted by the equations diverge so as to result in different treatment decisions across the two race groups. This analysis was done in men and women separately,” the press release stated.  

The researchers saw that differences in the risk prediction equations can result in different treatment decisions between Black and White patients.  

According to Ramachandran, by not treating the actual factors, physicians are at risk of treating the incorrect condition.  

“Since the equations are derived from historical cohort data, the Black-white differences in predicted risk probabilities may reflect underlying race-related differences in health care access, structural racism or social determinants of health,” Ramachandran said.  

According to co-author Edwin van den Heuvel, PhD, professor of statistics in the Department of Mathematics and Computer Science at the University of Technology Eindhoven, prediction equations should only use causal factors when guiding medical treatment decisions.  

“Furthermore, more research is needed to be able to determine if such causal prediction equations remain accurate after those at high risk are treated. In other words, we should investigate whether we can use the same prediction equations when risk factors are altered with interventions,” van den Heuvel said.