Role of SPECT Myocardial Perfusion Imaging, in Assessment and Early Diagnosis of Asymptomatic Coronary Artery Disease; An Analytical Study Conducted in a Tertiary Care Hospital of Islamabad, Pakistan
DOI:
https://doi.org/10.14738/jbemi.95.13031Keywords:
Myocardial Perfusion Imaging (MPI); Exercise Tolerance Test (ETT); Target Heart Rate (THR); Coronary Artery Disease (CAD); Ischemic Heart Disease (IHD); Framingham scoring.Abstract
Exercise Tolerance Test (ETT) and Myocardial Perfusion Imaging (MPI) are known non-invasive modalities to detect coronary artery disease (CAD). Aim of this study was to find out better modality for early detection of asymptomatic CAD. Material and Methods: 25 asymptomatic individuals with one or more cardiac risk factors were enrolled. Probability of CAD was calculated, using Framingham scoring; patients classified as low, intermediate and high-risk group. Each (n=25) underwent ETT and MPI. ETT response was categorized as normal, ischemic or equivocal. MPI results as normal, mild, moderate to severe degree defects. 18 patients had follow-up angiograms. Results: Risk stratification showed 14 patients were in intermediate group, 4 had high and 7 had low risk of CAD. In intermediate group, 6 patients had ischemic ETT changes, 10 had perfusion defects on scan and all 10 patients had ≥ 70% stenosis on coronary angiograms. In high-risk group; 2 subjects had positive ETTs, 3 had positive scans and all 3 showed significant vascular involvement on coronary angiogram. In low risk group 3 patients had positive ETT and MPS, 2 had mild vascular stenosis. Considering angiography as gold standard, we observed that MPI had sensitivity (86.66%) and specificity (90%), whereas ETT had 66.6% and 70% respectively. Positive and negative predictive values of MPI were 92.8% and 81.8 % respectively. Post-test probability of MPI was i.e. 0.72-0.88; compared to ETT which was 0.55-0.78. Conclusion: MPI is a better modality than ETT for early diagnosis of occult CAD, especially in intermediate and high risk group.
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Copyright (c) 2022 Shagufta Zafar Qureshi, Shazia Naseem, Mamoon Qadir, Shahbaz A. Kureshi, Shabana Saeed
This work is licensed under a Creative Commons Attribution 4.0 International License.