Introduction: Cardiovascular events have been linked with air pollution exposure, though most studies have used data from air quality monitors. Satellite-based predictions may more accurately predict exposure concentrations over purely monitored data, though few studies have compared results from both exposure assessment methods. Methods: We utilized a cohort of 5679 patients who had undergone a cardiac catheterization at Duke University between 2002-2009 and resided in NC. Exposure to PM2.5 was estimated using data from a) air quality monitors, b) 10km satellite-based models combined with monitored data, c) 1km satellite-based models that incorporate land use terms, meteorological variables, and monitored data, and d) 1km hybrid models that combine satellite data, GEOS-Chem predictions, monitored data, land use terms, and meteorological variables. PM2.5 predictions were matched to each patient’s address and averaged for the year prior to catheterization. The coronary artery disease (CAD) index was used to measure severity of CAD, and individuals with an index >23 were considered positive cases. Logistic regression was used to model odds of having CAD with each 1-unit (μg/m3) increase in annual average PM2.5, adjusting for gender, race, smoking status and socioeconomic status. Results: Positive associations were seen across all metrics, most significantly for the more spatially resolved 10km and 1km PM2.5 estimates. Specifically, we found a 3% (95%CI: 0.98-1.08) increase in the odds of significant CAD for each 1 μg/m3 increase in PM2.5 when using monitored estimates, 11% (95%CI: 1.04-1.19) increase for 10km PM2.5 estimates, 14% (95%CI: 1.07, 1.21) increase for 1km estimates, and 6% (95%CI: 1.00-1.12) increase for 1km hybrid estimates. Conclusions: Long-term air pollution exposure was associated with coronary artery disease for both satellite modeled and monitored data. This abstract does not necessarily reflect U.S. EPA policy.