Incremental diagnostic performance of combined parameters in the detection of severe coronary artery disease using exercise gated myocardial perfusion imaging

Abstract

Purpose: Myocardial perfusion imaging (MPI) using gated single-photon emission tomography (gSPECT) may underestimate the severity of coronary artery disease (CAD). This study aimed to evaluate the significance of combined parameters derived from gSPECT, as well as treadmill stress test parameters, in the detection of severe CAD. Methods: A total of 211 consecutive patients referred for exercise MPI between June 2011 and June 2013 (who received invasive coronary angiography within six months after MPI) were retrospectively reviewed. Exercise MPI was performed with Bruce protocol and 201 Tl injected at peak exercise. Gated SPECT was performed using a cadmium-zinc-telluride camera and processed by QPS/QGS software. Perfusion defect abnormalities such as sum stress score (SSS); sum difference score, algorithm-derived total perfusion deficits, transient ischemic dilatation ratios of end-diastolic volumes and end-systolic volumes, post-stress changes in ejection fraction, and lung/heart ratio (LHR) were calculated. Treadmill parameters, including ST depression (STD) at the 1st and 3rd minutes of recovery stage (1'STD and 3'STD), maximal STD corrected by heart rate increment (ST/HR), heart rate decline in 1 st and 3 rd minutes of recovery stage, recovery heart rate ratio (HR ratio), systolic and mean blood pressure ratios (SBP ratio and MAP ratio) during recovery phase were recorded. Diagnostic performances of these parameters were analyzed with receiver operating characteristic (ROC) analysis and logistic regression for detection of left main (≥ 50%) or 3-vessel disease (all ≥ 70% luminal stenosis) on invasive angiography. Results: Among various MPI and treadmill parameters used for detection of severe CAD, SSS and ST/HR had the highest AUC (0.78, 0.73, p = NS) and best cut-off values (SSS > 6, ST/HR > 17.39 10 -2 mV/bpm), respectively. By univariate logistic regression, all parameters except 1'HRR, 3'HRR, SBP and MAP ratios increased the odds ratio of severe CAD. Only increased L/H ratio, 3'STD, and HR ratio remained significant after multivariate regression. The predicted values of combined MPI and treadmill parameters (LHR, 3'STD, and HR ratio) gave the best ROC (AUC: 0.91) than any individual parameter or parameter combination. Conclusions: Of all treadmill and gSPECT parameters, the combination of MPI and treadmill parameters can offer better diagnostic performance for severe CAD.