Giuseppe Muscogiuri
Marco Guglielmo- 1Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Milan, Italy
- 2School of Medicine, University of Milano-Bicocca, Milan, Italy
- 3Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- 4Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
Editorial on the Research Topic
Multimodality imaging in the assessment of ischemic chronic coronary syndrome
Ischemic chronic coronary syndrome (ICCS) refers to a number of different conditions ranging from asymptomatic patients to ischemic cardiomyopathy (ICM) conditioning heart failure (1).
The diagnosis of ICCS is based on a combination of clinical history, physical examination, laboratory tests, and imaging tests (1).
The latter plays a critical role in the diagnostic work-up. Several imaging modalities are available, each with strengths and weaknesses. The choice of imaging modality depends on the patient's clinical presentation, the physician's preference, and equipment availability (1). The most commonly used noninvasive imaging modalities for the diagnosis of ICCS include echocardiography, nuclear medicine, cardiac magnetic resonance (CMR) and cardiac computed tomography angiography (CTCA).
In this special issue, El-Husseiny et al. use intraventricular pressure gradient (IVPG) as a noninvasive and sensitive approach to evaluate cardiac function. Specifically, their study aimed to examine IVPG's efficacy in predicting heart failure (HF) in a myocardial infarction (MI) rat model. The authors used transthoracic echocardiography (TTE) with color M-mode echocardiography to determine IVPG. Fifty healthy male rats were included, and MI was surgically induced in 35 of them. Based on echocardiographic parameters, the animals were categorized into two clusters: (heart failure positive, HF + or negative, HF-). After six months, the HF + group manifested the most pronounced echocardiographic, hemodynamic, anatomic, and histologic changes, and their basal IVPG was notably elevated compared to the HF- group. If confirmed by further research, IVPG obtained through echocardiography could be an auspicious noninvasive tool for detecting and predicting cardiac heart failure progression following chronic MI.
Islas et al. present interesting results regarding the prognostic role of epicardial adipose tissue (EAT) in patients suffering their first episode of MI and undergoing percutaneous coronary intervention (PCI). 41 patients (57.5 ± 10 years, 93% male) were enrolled. TTE and blood tests to measure galectin-3 (Gal-3) levels were performed within 24–48 h after PCI, while CMR was done 5–7 days after PCI. A clinical follow up of patients was performed after 1 and 5 years following MI.
Patients with EAT ≥ 4 mm measured on echocardiography showed lower left ventricular ejection fraction, abnormal global longitudinal strain, larger infarct size, and higher extracellular volume in the acute phase post-MI. Furthermore, the authors identified a cut-off value of EAT > 4 mm of thickness as an independent predictor of events 5 years after MI. EAT represents the visceral fat surrounding the heart and coronary arteries. In recent years, it has become the subject of increasing scientific investigation, with emerging evidence that it may be associated with the development of obstructive coronary artery disease and even acute coronary artery disease syndromes (2). 2D-Echocardiography does not allow for volumetric assessment of EAT, meaning it cannot accurately measure the total EAT volume like three-dimensional techniques such as CMR or CTCA. However, the study confirms that EAT thickness measured in 2D by a echocardiography can be used to predict patient outcomes and guide therapeutic interventions.
Focusing on CMR, Tat et al. showed that, in contrast to left ventricular ejection fraction, the extent and the location of late gadolinium enhancement (LGE) are independent predictors for the development of ventricular tachycardia. The study included 168 patients who underwent CMR imaging, either for ischemic cardiomyopathy (ICM) or non-ischemic cardiomyopathy (NICM). Of notice, the characteristics of LGE were found to have a more pronounced prognostic role for patients with NICM in comparison to subjects with ICM.
Unlike other methods, CMR has the great advantage of characterizing the myocardial tissue by identifying the presence of alterations, such as edema and focal and diffuse fibrosis (3). In particular, the LGE makes it possible to identify areas of focal fibrosis that often provide the substrate for threatening ventricular arrhythmias and sudden cardiac death. While CMR is a powerful diagnostic tool for evaluating ICCS, some potential disadvantages include costs, the use of contrast agents, and reduced availability.
Lala et al. wrote for this special issue an elegant mini-review on the multimodality approach in patients with ICC (3). The scope of the authors is to provide an overview of multimodality imaging for the diagnosis of chronic coronary syndrome in patients with new-onset HF and to suggest a diagnostic work-up based on the guidelines and data in the literature.
The algorithm of Lala et al. (3) for the diagnosis of new-onset and established HF includes the following steps (3):
1) performing a CTCA for rapid exclusion of CAD
2) if stenoses >50% are detected at CTCA, the next step is performing a stress CMR with LGE characterization. If CMR is unavailable, the authors propose a dobutamine stress echo as a second choice.
3) For advanced centers, Lala et al. suggest fractional flow reserve CT-based (FFR-CT) if >50% stenoses are identified with CTCA, followed by CMR with LGE tissue characterization.
Indeed, the diagnosis and management of ICCS often require a multimodality imaging approach that integrates information from various imaging techniques to comprehensively assess coronary anatomy, myocardial perfusion, and ventricular function (2, 4, 5).
By combining the information obtained from multiple imaging techniques, clinicians can provide patients with optimal care and improve their outcomes.
We believe that the application of artificial intelligence (AI) in the field of cardiac imaging will further improve the diagnostic workflows (4, 6).
By using AI algorithms to analyze imaging data, clinicians can obtain rapid, accurate, and detailed information about the cardiovascular system expedite correct diagnosis guide optimal treatment and improve prognosis.
In echocardiography, AI algorithms can improve the accuracy of measurements of left ventricular ejection fraction (LVEF) and other cardiac function parameters. AI algorithms can also be used to reduce the variability of measurements between different operators, leading to more consistent and accurate results (7).
AI algorithms can be used to analyze large CMR datasets to identify (8–11) areas of myocardial damage, quantify myocardial perfusion, and assess cardiac function. These algorithms can also be used to monitor changes in the cardiovascular system over time, providing clinicians with valuable information on disease progression and treatment response.
Given these premises, we expect a bright future for diagnosing ICCS using multimodality imaging.
Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
1. Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. (2020) 41(3):407–77. doi: 10.1093/eurheartj/ehz425
2. Guglielmo M, Lin A, Dey D, Baggiano A, Fusini L, Muscogiuri G, et al. Epicardial fat and coronary artery disease: role of cardiac imaging. Atherosclerosis. (2021) 321:30–8. doi: 10.1016/j.atherosclerosis.2021.02.008
3. Guglielmo M, Pontone G. Clinical implications of cardiac magnetic resonance imaging fibrosis. Eur Heart J Suppl. (2022) 24(Suppl I):I123–I6. doi: 10.1093/eurheartjsupp/suac085
4. Muscogiuri G, Guglielmo M, Serra A, Gatti M, Volpato V, Schoepf UJ, et al. Multimodality imaging in ischemic chronic cardiomyopathy. J Imaging. (2022) 8(2):35. doi: 10.3390/jimaging8020035
5. Muscogiuri G, Ricci F, Scafuri S, Guglielmo M, Baggiano A, De Stasio V, et al. Cardiac magnetic resonance tissue characterization in ischemic cardiomyopathy. J Thorac Imaging. (2022) 37(1):2–16. doi: 10.1097/RTI.0000000000000621
6. van Assen M, Muscogiuri G, Caruso D, Lee SJ, Laghi A, De Cecco CN. Artificial intelligence in cardiac radiology. Radiol Med. (2020) 125(11):1186–99. doi: 10.1007/s11547-020-01277-w
7. Narang A, Mor-Avi V, Prado A, Volpato V, Prater D, Tamborini G, et al. Machine learning based automated dynamic quantification of left heart chamber volumes. Eur Heart J Cardiovasc Imaging. (2019) 20(5):541–9. doi: 10.1093/ehjci/jey137
8. Penso M, Moccia S, Scafuri S, Muscogiuri G, Pontone G, Pepi M, et al. Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network. Comput Methods Programs Biomed. (2021) 204:106059. doi: 10.1016/j.cmpb.2021.106059
9. Knott KD, Seraphim A, Augusto JB, Xue H, Chacko L, Aung N, et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping. Circulation. (2020) 141(16):1282–91. doi: 10.1161/CIRCULATIONAHA.119.044666
10. Muscogiuri G, Martini C, Gatti M, Dell'Aversana S, Ricci F, Guglielmo M, et al. Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm. Int J Cardiol. (2021) 343:164–70. doi: 10.1016/j.ijcard.2021.09.012
Keywords: CAD, multimodality, CMR, cardiac CT, ischemic heart, echocardiagraphy
Citation: Muscogiuri G and Guglielmo M (2023) Editorial: Multimodality imaging in the assessment of ischemic chronic coronary syndrome. Front. Cardiovasc. Med. 10:1146050. doi: 10.3389/fcvm.2023.1146050
Received: 16 January 2023; Accepted: 28 March 2023;
Published: 11 April 2023.
Edited and Reviewed by: Christos Bourantas, Queen Mary University of London, United Kingdom
© 2023 Muscogiuri and Guglielmo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Giuseppe Muscogiuri Z2l1c2VwcGUubXVzY29naXVyaUB1bmltaWIuaXQ=
Specialty Section: This article was submitted to Cardiovascular Imaging, a section of the journal Frontiers in Cardiovascular Medicine