Characterization of hemodynamics in anomalous aortic origin of coronary arteries using patient-specific modeling

Evaluating the Association between Anomalous Aortic Origin of the Right Coronary Artery from the Left Sinus with Interarterial Course at Coronary CT Angiography and Sudden Cardiac Death

Purpose To investigate the association between the anomalous aortic origin of the right coronary artery (R-AAOCA) from the left coronary sinus with interarterial course (IAC) found at coronary CT angiography and sudden cardiac death using a large data set from five university hospitals. Materials and Methods From a total of 89 314 CCTA scans (January 2009 to December 2016) that were retrospectively collected, 316 patients with R-AAOCA from the left sinus with IAC were retrospectively collected. After excluding patients with less than 2 years of follow-up, patients who had already undergone cardiovascular surgery or intervention, and patients with arrhythmia or heart failure before undergoing coronary CT angiography, 224 patients were analyzed. Follow-up was terminated upon the occurrence of major adverse cardiovascular events (MACE). Logistic regression was used to identify clinical and radiologic information as independent predictors of MACE. Results The period prevalence of R-AAOCA from the left sinus with IAC was 0.354%. The mean age was 62.03 years, with a male-to-female ratio of 182:134. During follow-up, 19 of 224 patients (8.5%) experienced MACE, but none had sudden cardiac death. Of these cases, only seven (3.13%) were suspected of being due to R-AAOCA from the left sinus with IAC and all of them had unstable angina. Coronary artery disease was significantly associated with MACE (P < .001), while no significant correlation was observed with radiologic features. Conclusion Sudden cardiac death was not associated with R-AAOCA from the left sinus with IAC found at coronary CT angiography. The occurrence of MACE was low, with coronary artery disease being the sole significant predictor of a patient's prognosis. Keywords: Anomalous Aortic Origin of the Right Coronary Artery, Left Coronary Sinus with Interarterial Course, Coronary CT Angiography, Sudden Cardiac Death Supplemental material is available for this article. © RSNA, 2024.

Anomalous origin of the coronary artery: prevalence and coronary artery disease in adults undergoing coronary tomographic angiography

BACKGROUND

Anomalous aortic origin of a coronary artery (AAOCA) is a rare congenital coronary anomaly with the potential to cause adverse cardiac events. However, there is limited data on the association between AAOCA and coronary artery disease (CAD). Therefore, the aim of this study is to determine the prevalence and symptoms of patients with AAOCA, as well as investigate the correlation between AAOCA and CAD in a population referred for coronary computed tomographic angiography (CTA).

METHODS AND RESULTS

All consecutive patients who underwent CTA from 2010 to 2021 were included. Characteristics, symptoms, coronary related adverse events and CTA information were reviewed by medical records. Separate multivariable cumulative logistic regressions were performed, using the stenosis severity in each of the four coronaries as individual responses and as a combined patient clustered response. Finally, we identified 207 adult patients with AAOCA, the prevalence of AAOCA is 0.23% (207/90,501). Moreover, this study found no significant association between AAOCA and CAD. AAOCA did not contribute to higher rates of hospitalization or adverse cardiac events, including calcification.

CONCLUSION

AAOCA is a rare congenital disease that is not associated with increased presence of obstructive CAD in adults.

Automatic 3D Segmentation and Identification of Anomalous Aortic Origin of the Coronary Arteries Combining Multi-view 2D Convolutional Neural Networks

This work aimed to automatically segment and classify the coronary arteries with either normal or anomalous origin from the aorta (AAOCA) using convolutional neural networks (CNNs), seeking to enhance and fasten clinician diagnosis. We implemented three single-view 2D Attention U-Nets with 3D view integration and trained them to automatically segment the aortic root and coronary arteries of 124 computed tomography angiographies (CTAs), with normal coronaries or AAOCA. Furthermore, we automatically classified the segmented geometries as normal or AAOCA using a decision tree model. For CTAs in the test set (n = 13), we obtained median Dice score coefficients of 0.95 and 0.84 for the aortic root and the coronary arteries, respectively. Moreover, the classification between normal and AAOCA showed excellent performance with accuracy, precision, and recall all equal to 1 in the test set. We developed a deep learning-based method to automatically segment and classify normal coronary and AAOCA. Our results represent a step towards an automatic screening and risk profiling of patients with AAOCA, based on CTA.

Optimizing Temporal Waveform Analysis: A Novel Pipeline for Efficient Characterization of Left Coronary Artery Velocity Profiles

Continuously measured arterial blood velocity can provide insight into physiological parameters and potential disease states. The efficient and effective description of the temporal profiles of arterial velocity is crucial for both clinical practice and research. We propose a pipeline to identify the minimum number of points of interest to adequately describe a velocity profile of the left coronary artery. This pipeline employs a novel operation that "stretches" a baseline waveform to quantify the utility of a point in fitting other waveforms. Our study introduces a comprehensive pipeline specifically designed to identify the minimal yet crucial number of points needed to accurately represent the velocity profile of the left coronary artery. Additionally, the only location-dependent portion of this pipeline is the first step, choosing all of the possible points of interest. Hence, this work is broadly applicable to other waveforms. This versatility paves the way for a novel non-frequency domain method that can enhance the analysis of physiological waveforms. Such advancements have potential implications in both research and clinical treatment of various diseases, underscoring the broader applicability and impact.

Application of Patient-Specific Computational Fluid Dynamics in Anomalous Aortic Origin of Coronary Artery: A Systematic Review

Anomalous aortic origin of a coronary artery (AAOCA) is a rare congenital heart condition with fixed and dynamic stenotic elements, potentially causing ischemia. Invasive coronary angiography under stress is the established method for assessing hemodynamics in AAOCA, yet it is costly, technically intricate, and uncomfortable. Computational fluid dynamics (CFD) simulations offer a noninvasive alternative for patient-specific hemodynamic analysis in AAOCA. This systematic review examines the role of CFD simulations in AAOCA, encompassing patient-specific modeling, noninvasive imaging-based boundary conditions, and flow characteristics. Screening articles using AAOCA and CFD-related terms prior to February 2023 yielded 19 publications, covering 370 patients. Over the past four years, 12 (63%) publications (259 patients) employed dedicated CFD models, whereas 7 (37%) publications (111 patients) used general-purpose CFD models. Dedicated CFD models were validated for fixed stenosis but lacked dynamic component representation. General-purpose CFD models exhibited variability and limitations, with fluid-solid interaction models showing promise. Interest in CFD modeling of AAOCA has surged recently, mainly utilizing dedicated models. However, these models inadequately replicate hemodynamics, necessitating novel CFD approaches to accurately simulate pathophysiological changes in AAOCA under stress conditions.

Patient-Specific Fluid-Structure Simulations of Anomalous Aortic Origin of Right Coronary Arteries

Objectives

Anomalous aortic origin of the right coronary artery (AAORCA) may cause ischemia and sudden death. However, the specific anatomic indications for surgery are unclear, so dobutamine-stress instantaneous wave-free ratio (iFR) is increasingly used. Meanwhile, advances in fluid–structure interaction (FSI) modeling can simulate the pulsatile hemodynamics and tissue deformation. We sought to evaluate the feasibility of simulating the resting and dobutamine-stress iFR in AAORCA using patient-specific FSI models and to visualize the mechanism of ischemia within the intramural geometry and associated lumen narrowing.

Methods

We developed 6 patient-specific FSI models of AAORCA using SimVascular software. Three-dimensional geometries were segmented from coronary computed tomography angiography. Vascular outlets were coupled to lumped-parameter networks that included dynamic compression of the coronary microvasculature and were tuned to each patient's vitals and cardiac output.

Results

All cases were interarterial, and 5 of 6 had an intramural course. Measured iFRs ranged from 0.95 to 0.98 at rest and 0.80 to 0.95 under dobutamine stress. After we tuned the distal coronary resistances to achieve a stress flow rate triple that at rest, the simulations adequately matched the measured iFRs (r = 0.85, root-mean-square error = 0.04). The intramural lumen remained narrowed with simulated stress and resulted in lower iFRs without needing external compression from the pulmonary root.

Conclusions

Patient-specific FSI modeling of AAORCA is a promising, noninvasive method to assess the iFR reduction caused by intramural geometries and inform surgical intervention. However, the models’ sensitivity to distal coronary resistance suggests that quantitative stress-perfusion imaging may augment virtual and invasive iFR studies.