Coronary Computed Tomography Angiography for the Diagnosis of Vasospastic Angina: Comparison with Invasive Coronary Angiography and Ergonovine Provocation Test

Assessment of coronary spasms with transluminal attenuation gradient in coronary computed tomography angiography

Purpose

To evaluate the imaging features of coronary spasm, including transluminal attenuation gradient (TAG) on coronary computed tomography angiography (CCTA), in patients with vasospastic angina (VA).

Methods

A total of 43 patients with a high clinical likelihood of VA were included in the study. All the subjects underwent double CCTA acquisition: CCTA without a vasodilator (‘baseline CT’) and CCTA during continuous intravenous nitrate infusion (‘IV nitrate CT’). A catheterized ergonovine provocation test was used to determine true VA patients. Coronary spasm is classified into focal- and diffuse-types according to morphological differences. We measured TAG and contrast enhancement of the proximal ostium (ProxHU) of each coronary artery for both the baseline and IV nitrate CT.

Results

Twenty-four patients (55.8%) showed positive results of coronary vasospasm on the provocation test. Thirty-eight vessels showed coronary spasms (29.5%): Focal-type in nine vessels (24%), and diffuse-type in 29 (76%). In the baseline CT, LCX showed significantly lower (steeper) TAG in spasm(+) vessels than in spasm(-) vessels, while LAD and RCA showed no significant differences in TAG. The ProxHU of LAD showed significantly lower values in spasm(+) vessels than in spasm(-) vessels, while the other vessels did not show significant differences in ProxHU. For IV nitrate CT, there were no significant differences in either the TAG and ProxHU between spasm(+) and (-) vessels for all the three vessel types. In subgroup analysis for spasm(+) vessels, diffuse spasms showed significantly lower TAG than focal spasms, while the ProxHU did not differ between the two types of spasm.

Conclusions

A relatively large percentage of coronary spasms present as diffuse type, and the TAG values significantly differed according to the morphological type of the coronary spasm.

Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience

Background

To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.

Methods

We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1–Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of the six readers were calculated at the patient and vessel levels. Additionally, we evaluated the interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6.

Results

The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than that of Reader 5 and Reader 6. AI-assisted Reader 3 and Reader 4 had higher sensitivity (range + 7.2–+ 16.6% and + 5.9–+ 16.1%, respectively) and NPVs (range + 3.7–+ 13.4% and + 2.7–+ 4.2%, respectively) than Reader 1 and Reader 2 without AI. Good interobserver consistency was found between Reader 3 and Reader 4 in interpreting ≥ 50% stenosis (Kappa value = 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 showed poor interobserver consistency (Kappa value = 0.25 and 0.37). Reader 5 and Reader 6 showed moderate agreement (Kappa value = 0.55 and 0.61).

Conclusions

Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve the consistency of coronary stenosis diagnosis via CCTA.

Trial registration Clinical trial registration number: ChiCTR1900021867. Name of registry: Diagnostic performance of artificial intelligence-assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00756-y.

CT Imaging Findings in Non-Atherosclerotic Coronary Artery Disease

급성 관동맥 증후군(acute coronary syndrome)은 대부분 죽상경화 관상동맥 질환(atherosclerotic coronary artery disease)에 의해 발생하지만, 비죽상경화 관상동맥 질환에서도 발생할 수 있다. 고식적 관상동맥 혈관조영술은 동맥 내강의 협착이나 확장 등의 형상에 대한 정보만을 제공하고, 동맥경화반이나 동맥벽에 대한 평가가 어려워 관상동맥 이상의 원인 질환의 진단에 낮은 특이도를 보인다. 반면, 관상동맥 전산화단층촬영 혈관조영술은 혈관경화반의 특징, 혈관벽의 조영증강뿐 아니라 연접한 대동맥이나 폐동맥의 변화 등도 함께 관찰할 수 있어, 비죽상경화 관상상동맥질환의 진단 및 다양한 원인 감별에 도움이 된다. 따라서 이 종설에서는 다양한 비죽상경화 관상동맥 질환들을 소개하고, 이의 병태생리 및 대표적인 관상동맥 전산화단층촬영 혈관조영술의 영상 소견에 대해 설명하고자 한다.

Troponin-Positive Non-Obstructive Coronary Arteries and Myocardial Infarction with Non-Obstructive Coronary Arteries: Definition, Etiologies, and Role of CT and MR Imaging

In approximately 10% of patients with acute myocardial infarction (MI), angiography does not reveal an obstructive coronary stenosis. This is known as myocardial infarction with non-obstructive coronary arteries (MINOCA), which has complex and multifactorial causes. However, this term can be confusing and open to dual interpretation, because MINOCA is also used to describe patients with acute myocardial injury caused by ischemia-related myocardial necrosis. Therefore, with regards to this specific context of MINOCA, the generic term for MINOCA should be replaced with troponin-positive with non-obstructive coronary arteries (TpNOCA). The causes of TpNOCA can be subcategorized into epicardial coronary (causes of MINOCA), myocardial, and extracardiac disorders. Cardiac magnetic resonance imaging can confirm MI and differentiate various myocardial causes, while cardiac computed tomography is useful to diagnose the extracardiac causes.