Abstract 16483: Electrocardiographic Abnormalities and Coronary Artery Calcium for Prediction and Reclassification of Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis (mesa)

Abstract

BACKGROUND: Electrocardiographic (ECG) abnormalities are associated with increased risk for coronary heart disease (CHD). We sought to determine whether ECG abnormalities can improve risk prediction for CHD and if the addition of ECG to a risk prediction model containing coronary artery calcium (CAC) further improves risk prediction.

METHODS: We stratified MESA participants (ppts) into the following categories based on ST, Q, and T wave abnormalities defined by the Minnesota Code: any major, any minor/no major, no major/minor. We calculated a risk prediction model for CHD in using MESA-specific coefficients for Framingham risk factors (RFs) at year 0. CAC was defined as present or absent. We calculated category-free net reclassification improvement (NRI) and NRI based on the following 10-year risk categories for CHD: < 5%, 5-10%, ≥ 10%.

RESULTS: Among 6406 ppts, 385 had major and 897 had minor ECG abnormalities. The CHD event rates per 1000 person-years in ppts with major, minor, and no major/minor ECG abnormalities were 16.1, 11.9, and 5.5, respectively. The multivariable-adjusted hazards ratios for CHD events in ppts with major and minor abnormalities compared to no major/minor were 1.86 (95% CI, 1.32-2.67) and 1.69 (1.29-2.23), respectively. The addition of ECG abnormalities to the standard risk prediction model for CHD resulted in a category-free NRI of 0.34 (P < 0.001) and categorical NRI of 0.011 (P = 0.69). As shown in the Table, addition of ECG to a CHD risk prediction model containing Framingham RFs and CAC resulted in a categorical NRI of 0.052 (P = 0.03) and category-free NRI of 0.32 (P < 0.001).

CONCLUSION: Major and minor ECG abnormalities were both associated with increased incidence of CHD. Addition of categories of ECG abnormalities to a CHD risk prediction model based on Framingham RFs and CAC resulted in significantly improved reclassification. These results suggest that ECG may capture certain higher-risk individuals not identified by CAC.