Cardiovascular Risk Models Exclude Socioeconomic Factors; New Grant to Develop Enhanced Model

A team of Lerner researchers led by Jarrod Dalton, PhD, assistant staff in the Department of Quantitative Health Sciences, have shown that current cardiovascular risk prediction models systematically underestimate risk in individuals from low income and disadvantaged neighborhoods. With a newly awarded four-year, $2.2 million grant from the National Institute on Aging, part of the National Institutes of Health, the team will develop a more sophisticated model—one that incorporates both clinical and socioeconomic factors—to better predict risk for major cardiovascular disease-related health events such as stroke and heart attack. In addition to helping clinicians provide more targeted prevention and treatment options for patients, the team hopes that better understanding the underlying neighborhood-level risks associated with these events might ultimately inform community development initiatives to create healthier environments.

Current cardiovascular risk assessment tools, including the Pooled Cohort Equations Risk Model (PCERM) developed jointly by the American College of Cardiology and the American Heart Association, rely solely on clinical indicators of risk such as weight, age, blood pressure and health behaviors like smoking. But research suggests that socioeconomic status—which can be an indicator of things like environmental exposure to toxins and access to healthy food options and safe places to exercise—also play a role in risk for cardiovascular disease. In fact, Dr. Dalton's team found that patients from disadvantaged Cleveland neighborhoods had 2-3 times the rate of heart attacks and stroke as those from more affluent neighborhoods, even after controlling for differences in standard clinical risk factors.

In order to compare PCERM-predicted versus actual incidence of cardiovascular disease-related events, and thereby determine model accuracy, the team developed a neighborhood disadvantage index (NDI). The NDI is a single-factor representation of neighborhood socioeconomic position that takes several factors including race, educational attainment, insurance status, income and marital status into account.

After analyzing the electronic health records of over 100,000 Cleveland Clinic patients and classifying them using the NDI, Dr. Dalton discovered that PCERM significantly underestimates risk in people at the top of the NDI—those from the most disadvantaged neighborhoods—with major cardiovascular events sometimes occurring at 2-3 times the rates predicted. This work was recently published in the Annals of Internal Medicine.

To develop their new statistical model, the team will establish a geography-coded registry using electronic health records from 200,000 Cleveland Clinic and MetroHealth patients. They will supplement that data with neighborhood-level information from U.S. Census and other government organizations. Using principles from weather forecasting science, they will overlay this data to build a new comprehensive, systems-based tool for risk prediction.

At a time when cardiovascular disease continues to top the charts of all-cause mortality in the United States, it is imperative that clinicians have a complete and comprehensive understanding of risk. Dr. Dalton and his team hope that this more sensitive and accurate model will help deliver personalized cardiovascular care for all patient populations.