Guidelines-driven diagnostic investigations for suspicion of functionally relevant coronary artery disease (fCAD) are complex and expensive. Therefore, we evaluated whether a previously validated proteomic residual cardiovascular risk (RCVR) model could complement existing fCAD detection strategies by accurately identifying patients at high risk for an event and safely ruling out fCAD in low-risk patients. Clinical assessments, single-photon emission computed tomography myocardial perfusion imaging, and RCVR proteomic predictions were available in 4106 BASEL VIII study participants previously evaluated for suspicion of fCAD. The ability of each measure to predict the occurrence of major adverse cardiovascular (CV) events were compared separately in participants without (primary) and with (secondary) a history of CV disease. Primary participants with a negative fCAD diagnosis had an unstratified 4-year CV event rate of 9.4%, comparable to the overall event rate of 10.7%, however, the event rate dropped to only 2.3% (p<0.001) when using the RCVR model to identify “low” risk individuals. Similarly, in secondary participants the event rate in “low” risk individuals was only 9.9% (p<0.001) compared to 20.5% in fCAD negative participants and a 26.4% overall event rate. These results suggest RCVR could be used to rule out low risk individuals and potentially eliminate unnecessary testing in 42.2% of primary and 27.6% of secondary participants. RCVR was also able to identify individuals with a negative fCAD diagnosis that were still at high risk of a CV event. Within primary and secondary participants respectively, 7.1% and 14.6% of participants were identified as “high” risk with an observed event rate of 39.9% and 52.0%. Together these results suggest prognostic protein testing in combination with cardiac imaging or clinical diagnostic assessments may provide a more comprehensive assessment of patient risk and aid in medical management and monitoring.

Proteomics, Aptamers, Coronary heart disease, Cardiovascular disease, Cardiovascular imaging, Cardiovascular risk reduction, Cardiac biomarkers