Correlation between severity of coronary artery stenosis and perfusion defect assessed by SPECT myocardial perfusion imaging.

Numerous non-invasive techniques are developed to assess the severity of coronary artery disease (CAD). Coronary angiography (CAG) is an established method for the diagnosis and to quantify the severity of coronary artery stenosis. Single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) using Technetium-99m (Tc-99m) tetrofosmin is also a useful established technique for the assessment of severity of CAD. This prospective observational study was carried out in Bangabandhu Sheikh Mujib Medical University (BSMMU) to assess the severity of coronary artery stenosis using Tc-99m Single Photon Emission Computed Tomography myocardial perfusion imaging (SPECT-MPI) in comparison with CAG. Eighty two (82) consecutive patients with mean age 53.51(SD+/-7.08) years and Candian Cardiovascular Society (CCS) class I and II severity of chest pain, male: female ratio (4.8:1) was studied. Tc-99m SPECT-MPI was performed by one-day exercise stress and rest protocol. A total of two hundred and forty six coronary artery territories examined in this study. By CAG normal coronary arteries were found in seventy six, moderate stenosis in twenty four and severe stenosis in one hundred and forty-six coronary artery territories whereas SPECT-MPI found normal perfusion in twenty seven, mild perfusion abnormality in seventeen, moderate perfusion abnormality in thirty two and severe perfusion abnormality in one hundred and sixty five coronary artery territories. Sensitivity and specificity of SPECT-MPI using Tc-99m tetrofosmin in detecting coronary artery stenosis were 87.09% and 80.95% respectively. The positive predictive value, negative predictive value and accuracy of the test were 91.01%, 73.91% and 85.18% respectively. From this study it can be concluded that Tc-99m SPECT-MPI was a safe, effective and excellent non-invasive tool for the detection of severity of coronary artery lesion and can be used to predict severity of CAD.