Non ECG gated supine to prone left ventricular volume ratio: a novel marker for myocardial ischemia.
Min Sen YewWei Sheng Jonathan OngSee Jin Jesse OngPublished in: The international journal of cardiovascular imaging (2020)
Transient ischemic dilation (TID), a marker of severe coronary artery disease (CAD), is the post-stress to rest left ventricular (LV) volume ratio quantified using non ECG gated single photon emission computerized tomography (SPECT). Although prone positioning causes physiological reduction of LV volume in normal subjects, we hypothesize this may not occur in TID with underlying severe CAD as cardiac hemodynamics worsen when prone. We aim to evaluate the utility of the non ECG gated supine to prone LV volume ratio (SPLVr) for identifying severe CAD. Retrospective data analysis from 130 patients with TID ratio ≥ 1.21 and both post-stress supine and prone images. SPLVr had a significant negative correlation with summed stress (r = - 0.221, p = 0.011) and rest (r = - 0.292, p = 0.001) scores. Of the 129 cases with follow-up invasive or computed tomography coronary angiography, 52 (40.3%) had severe CAD (left main ≥ 50% stenosis, 3-vessel with ≥ 70% stenosis or 2-vessel with proximal left anterior descending ≥ 70% stenosis). Mean SPLVr was significantly lower in severe CAD cases (1.05 ± 0.14 vs 1.12 ± 0.17, p = 0.012). SPLVr predicted severe CAD on univariate [OR 0.12 (95% CI 0.00-0.35) p = 0.01] but not in multivariate analysis. SPLVr is a novel marker that negatively correlates with extent of perfusion abnormalities and is lower amongst TID patients with severe CAD. Larger studies are needed to assess if SPLVr can reliably identify underlying severe CAD amongst TID cases.
Keyphrases
- coronary artery disease
- left ventricular
- early onset
- computed tomography
- data analysis
- percutaneous coronary intervention
- cardiovascular events
- coronary artery bypass grafting
- aortic stenosis
- heart rate variability
- mitral valve
- magnetic resonance imaging
- heart rate
- acute myocardial infarction
- machine learning
- pet ct
- stress induced
- heat stress
- convolutional neural network
- cerebral ischemia
- electronic health record
- clinical decision support
- subarachnoid hemorrhage