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Findings of Single-Photon Emission Computed Tomography and Its Relation with Quantitative Coronary Angiography in Patients with Significant Stenosis of the Left Main Coronary Artery.

ABSTRACT: Objective:Unrecognized left main coronary artery disease (LMCD) is often fatal; however, accuracy of non-invasive tests for diagnosing LMCD is still unsatisfactory. This study was performed to elucidate single-photon emission computed tomography (SPECT) detection of LMCD using quantitative coronary angiography (QCA) data. Materials and Methods:Fifty-five patients (39 men; mean age, 68.1 ± 10.9 years) diagnosed with significant left main (LM) stenosis (? 50%) by invasive coronary angiography (ICA) were retrospectively reviewed. All study patients underwent SPECT with pharmacologic stress within 30 days of ICA. All coronary lesions were quantified via QCA, and SPECT findings were compared with QCA results. Results:Only four patients (7.3%) had isolated LMCD; all others had combined significant stenosis (? 70%) of one or more other epicardial coronary arteries. Patients with more severe coronary artery disease tended to have higher values for summed difference scores in a greater number of regions, but the specific pattern was not clearly defined. Summed stress score of SPECT did not differ according to LM stenosis severity. Only three patients (5.4%) had a typical LM pattern of reversible perfusion defect on SPECT. A significant negative linear correlation between stenosis severity and stress perfusion percent was found in the left anterior descending artery region (r = -0.455, p < 0.001) but not in the left circumflex artery. Conclusion:Single-photon emission computed tomography findings were heterogeneous, not specific and poorly correlated to QCA data in patients with significant LMCD. This may be due to highly prevalent significant stenosis of other epicardial coronary arteries.

SUBMITTER: Kim HL

PROVIDER: S-EPMC5768490 | biostudies-literature | 2018 Jan-Feb

REPOSITORIES: biostudies-literature

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Findings of Single-Photon Emission Computed Tomography and Its Relation with Quantitative Coronary Angiography in Patients with Significant Stenosis of the Left Main Coronary Artery.

Kim Hack-Lyoung HL Oh So Won SW Lee Hyunjong H Kim Hee Jun HJ Kim You Nui YN Lim Woo-Hyun WH Seo Jae-Bin JB Kim Sang-Hyun SH Kim Myung-A MA Zo Joo-Hee JH

Korean journal of radiology 20180102 1

<h4>Objective</h4>Unrecognized left main coronary artery disease (LMCD) is often fatal; however, accuracy of non-invasive tests for diagnosing LMCD is still unsatisfactory. This study was performed to elucidate single-photon emission computed tomography (SPECT) detection of LMCD using quantitative coronary angiography (QCA) data.<h4>Materials and methods</h4>Fifty-five patients (39 men; mean age, 68.1 ± 10.9 years) diagnosed with significant left main (LM) stenosis (≥ 50%) by invasive coronary a ...[more]

PMID: 29354006

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Project description:BackgroundAlthough coronary computed tomography angiography (CCTA) is currently utilized as the frontline test to accurately diagnose coronary artery disease (CAD) in clinical practice, there are still debates regarding its use as a screening tool for the asymptomatic population. Using deep learning (DL), we sought to develop a prediction model for significant coronary artery stenosis on CCTA and identify the individuals who would benefit from undergoing CCTA among apparently healthy asymptomatic adults.MethodsWe retrospectively reviewed 11,180 individuals who underwent CCTA as part of routine health check-ups between 2012 and 2019. The main outcome was the presence of coronary artery stenosis of ≥70% on CCTA. We developed a prediction model using machine learning (ML), including DL. Its performance was compared with pretest probabilities, including the pooled cohort equation (PCE), CAD consortium, and updated Diamond-Forrester (UDF) scores.ResultsIn the cohort of 11,180 apparently healthy asymptomatic individuals (mean age 56.1 years; men 69.8%), 516 (4.6%) presented with significant coronary artery stenosis on CCTA. Among the ML methods employed, a neural network with multi-task learning (19 selected features), one of the DL methods, was selected due to its superior performance, with an area under the curve (AUC) of 0.782 and a high diagnostic accuracy of 71.6%. Our DL-based model demonstrated a better prediction than the PCE (AUC, 0.719), CAD consortium score (AUC, 0.696), and UDF score (AUC, 0.705). Age, sex, HbA1c, and HDL cholesterol were highly ranked features. Personal education and monthly income levels were also included as important features of the model.ConclusionWe successfully developed the neural network with multi-task learning for the detection of CCTA-derived stenosis of ≥70% in asymptomatic populations. Our findings suggest that this model may provide more precise indications for the use of CCTA as a screening tool to identify individuals at a higher risk, even in asymptomatic populations, in clinical practice.

Dataset Information

Findings of Single-Photon Emission Computed Tomography and Its Relation with Quantitative Coronary Angiography in Patients with Significant Stenosis of the Left Main Coronary Artery.

Publications

Findings of Single-Photon Emission Computed Tomography and Its Relation with Quantitative Coronary Angiography in Patients with Significant Stenosis of the Left Main Coronary Artery.

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