ECG abnormalities associated with increased cardiovascular disease risk in REGARDS cohort analysis

Background: Resting electrocardiogram (ECG) is not currently recommended as part of cardiovascular disease (CVD) risk assessment, although accumulating evidence suggests a potential role. Objective: To examine the association between ECG abnormalities and incident CVD events as assessed by the 2023 Predicting Risk of Cardiovascular Disease Events (PREVENT) equations. Design: Secondary data analysis from the REasons for Geographic And Racial Differences in Stroke (REGARDS) prospective cohort, including study participants without a baseline CVD. Exposure: ECG abnormalities were classified by Minnesota Code (MC) as normal, any minor, or major abnormality at baseline (2003-2007). Outcome: Participants were followed for expert adjudicated incident CVD events through December 31, 2021. Results: Among 19,173 participants (mean age at baseline of 63.7 years; 57.8% were female). According to the PREVENT risk equations, 39.4% were classified as <7.5% 10-year risk CVD risk, 44.6% as 7.5-20% risk, and 16.0% as >20% risk. Overall, 47.0% had normal ECG, 44.0% had any minor abnormality, and 9.0% had any major abnormality. During follow-up, CVD events occurred in 12.4% of participants with normal ECG, 17.0% of those with any minor abnormality, and 25.4% of those with any major abnormality. Compared to those without ECG abnormality, the adjusted HR for incident CVD were 1.19 (95% CI 1.10-1.29) for any minor abnormality, and 1.53 (1.36-1.72) for any major ECG abnormality. In the <7.5% risk group, 43.6% had at least one ECG abnormality; in this risk group compared to those without ECG abnormality, the HR for incident CVD associated with any major ECG abnormality, present in 5.0% of the <7.5% risk group, was 1.87 (95% CI 1.34-2.62), The HR for any minor ECG abnormalities, present in 38.6% was 1.13 ( 95% CI 0.93 - 1.37). Conclusion: ECG abnormalities were associated with risk of CVD events across PREVENT risk groups. A substantial proportion of low-risk participants (according to the PREVENT equation) had ECG abnormalities and associated elevated risk. This supports the potential for using ECG to identify a subgroup of low-risk patients who may benefit from more aggressive primary prevention especially with major ECG abnormalities. Addition of electrocardiographic evaluation to the PREVENT risk equations may improves cardiovascular risk discrimination.