Computed Tomography Angiographic Assessment of Acute Chest Pain

Approach to Imaging of Patients Presenting With Acute Coronary Syndrome With No Culprit Lesion Identified at Angiography

Chest pain is a common chief complaint among patients presenting to the emergency department. However, in the scenario where the clinical presentation is consistent with acute coronary syndrome and no culprit lesions are identified on angiography, clinicians and cardiac imagers should be informed of the differential diagnosis and appropriate imaging modalities used to investigate the potential causes. This review describes an imaging-based algorithm that highlights the diagnostic possibilities, their differentiating imaging features, and the important role of cardiovascular magnetic resonance imaging for narrowing the differential diagnosis.

Non-invasive fractional flow reserve (FFRCT) in the evaluation of acute chest pain - Concepts and first experiences

OBJECTIVE

To evaluate 30 day rate of major adverse cardiac events (MACE) utilizing cCTA and FFR_CT for evaluation of patients presenting to the Emergency Department (ED) with acute chest pain.

MATERIALS AND METHODS

Patients between the ages of 18-95 years who underwent clinically indicated cCTA and FFR_CT in the evaluation of acute chest pain in the emergency department were retrospectively evaluated for 30 day MACE, repeat presentation/admission for chest pain, revascularization, and additional testing.

RESULTS

A total of 59 patients underwent CCTA and subsequent FFR_CT for the evaluation of acute chest pain in the ED over the enrollment period. 32 out of 59 patients (54 %) had negative FFR_CT (>0.80) out of whom 18 patients (55 %) were discharged from the ED. Out of the 32 patients without functionally significant CAD by FFR_CT, 32 patients (100 %) underwent no revascularization and 32 patients (100 %) had no MACE at the 30-day follow-up period.

CONCLUSION

In this limited retrospective study, patients presenting to the ED with acute chest pain and with CCTA with subsequent FFR_CT of >0.8 had no MACE at 30 days; however, for many of these patients results were not available at time of clinical decision making by the ED physician.

Value of Machine Learning-based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain

Purpose

To evaluate the additional value of noninvasive artificial intelligence (AI)-based CT-derived fractional flow reserve (CT FFR), derived from triple-rule-out coronary CT angiography for acute chest pain (ACP) in the emergency department (ED) setting.

Materials and Methods

AI-based CT FFR from triple-rule-out CT angiography data sets was retrospectively obtained in 159 of 271 eligible patients (102 men; mean age, 57.0 years ± 9.7 [standard deviation]) presenting to the ED with ACP. The agreement between CT FFR (≤ 0.80) and stenosis at triple-rule-out CT angiography (≥ 50%), as well as downstream cardiac diagnostic testing, was investigated. Furthermore, the predictive value of CT FFR for coronary revascularization and major adverse cardiac events (MACE) was assessed over a 1-year follow-up period.

Results

CT FFR and triple-rule-out CT angiography demonstrated agreement in severity of coronary artery disease (CAD) in 52% (82 of 159) of all cases. CT FFR of 0.80 and less served as a better predictor for coronary revascularization and MACE than stenosis of 50% and greater at triple-rule-out CT angiography (odds ratio, 3.4; 95% confidence interval: 1.4, 8.2 vs odds ratio, 2.2; 95% confidence interval: 0.9, 5.3) (P < .01). In the subgroup of patients with additional noninvasive cardiac testing (94 of 159), there was higher agreement as to the presence or absence of significant disease with CT FFR (55%) than with coronary triple-rule-out CT angiography (47%) (P = .23).

Conclusion

CT FFR derived from triple-rule-out CT angiography was a better predictor for coronary revascularization and MACE and showed better agreement with additional diagnostic testing than triple-rule-out CT angiography. Therefore, CT FFR may improve the specificity in identifying patients with ACP with significant CAD in the ED setting and reduce unnecessary downstream testing.© RSNA, 2020See also the commentary by Ihdayhid and Ben Zekry in this issue.

In-Hospital Cost Comparison of Triple-Rule-Out Computed Tomography Angiography Versus Standard of Care in Patients With Acute Chest Pain

PURPOSE

The purpose of this study was to evaluate the utilization of invasive and noninvasive tests and compare cost in patients presenting with chest pain to the emergency department (ED) who underwent either triple-rule-out computed tomography angiography (TRO-CTA) or standard of care.

MATERIALS AND METHODS

We performed a retrospective single-center analysis of 2156 ED patients who presented with acute chest pain with a negative initial troponin and electrocardiogram for myocardial injury. Patient cohorts matched by patient characteristics who had undergone TRO-CTA as a primary imaging test (n=1139) or standard of care without initial CTA imaging (n=1017) were included in the study. ED visits, utilization of tests, and costs during the initial episode of hospital care were compared.

RESULTS

No significant differences in the diagnosis of coronary artery disease, pulmonary embolism, or aortic dissection were observed. Median ED waiting time (4.5 vs. 7.0 h, P<0.001), median total length of hospital stay (5.0 vs. 32.0 h, P<0.001), hospital admission rate (12.6% vs. 54.2%, P<0.001), and ED return rate to our hospital within 30 days (3.5% vs. 14.6%, P<0.001) were significantly lower in the TRO-CTA group. Moreover, reduced rates of additional testing and invasive coronary angiography (4.9% vs. 22.7%, P<0.001), and ultimately lower total cost per patient (11,783$ vs. 19,073$, P<0.001) were observed in the TRO-CTA group.

CONCLUSIONS

TRO-CTA as an initial imaging test in ED patients presenting with acute chest pain was associated with shorter ED and hospital length of stay, fewer return visits within 30 days, and ultimately lower ED and hospitalization costs.

Automated plaque analysis for the prognostication of major adverse cardiac events

OBJECTIVE

The purpose of this study is to assess the value of an automated model-based plaque characterization tool for the prediction of major adverse cardiac events (MACE).

METHODS

We retrospectively included 45 patients with suspected coronary artery disease of which 16 (33%) experienced MACE within 12 months. Commercially available plaque quantification software was used to automatically extract quantitative plaque morphology: lumen area, wall area, stenosis percentage, wall thickness, plaque burden, remodeling ratio, calcified area, lipid rich necrotic core (LRNC) area and matrix area. The measurements were performed at all cross sections, spaced at 0.5 mm, based on fully 3D segmentations of lumen, wall, and each tissue type. Discriminatory power of these markers and traditional risk factors for predicting MACE were assessed.

RESULTS

Regression analysis using clinical risk factors only resulted in a prognostic accuracy of 63% with a corresponding area under the curve (AUC) of 0.587. Based on our plaque morphology analysis, minimal cap thickness, lesion length, LRNC volume, maximal wall area/thickness, the remodeling ratio, and the calcium volume were included into our prognostic model as parameters. The use of morphologic features alone resulted in an increased accuracy of 77% with an AUC of 0.94. Combining both clinical risk factors and morphological features in a multivariate logistic regression analysis increased the accuracy to 87% with a similar AUC of 0.924.

CONCLUSION

An automated model based algorithm to evaluate CCTA-derived plaque features and quantify morphological features of atherosclerotic plaque increases the ability for MACE prognostication significantly compared to the use of clinical risk factors alone.