#### **2. Materials and Methods**

We conducted a scoping review in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

#### *2.1. Data Sources and Searches*

A literature search was conducted on PubMed, Scopus, and Embase by two authors (YS, NK) based on the following search terms: ("Artificial intelligence" OR "machine learning" OR "Deep learning") AND ("Pediatric cardiology" OR "congenital heart disease"). The search was also refined using the MeSH Major Topic term "artificial intelligence".

#### *2.2. Article Selection*

Duplicate studies were eliminated after the initial search, and two reviewers (NK, YS) independently evaluated the title and abstract to see if the articles qualified for a full-text review. An adjudicator (NP) overcame any disagreements. Both reviewers further evaluated the entire article in accordance with the inclusion and exclusion criteria for each potentially eligible study. A comprehensive review was ensured by screening the references to include additional studies (Figure 1).

**Figure 1.** Flow diagram showing selection process of studies included.

#### *2.3. Inclusion and Exclusion Criteria*

The initial search was narrowed down by limiting the search to the English language and excluding animal studies. We limited our search to studies published between January 2002 and March 2022 because of the rise in interest in AI in the field of pediatric cardiology over the last 20 years. The inclusion criteria included observational studies (case–control, case series, and cross-sectional studies), experimental studies (randomized control trials), reviews, and expert opinions on the application of AI in pediatric cardiology. Studies were excluded if they lacked direct relevance to AI or were aimed at a population other than the pediatric (18 years) age group. Studies with no full text available, conference abstracts, papers, and book chapters were also excluded.

#### **3. Results**

The studies describing the role of AI in pediatric cardiology are compiled in Table 1, while Table 2 describes AI algorithms in pediatric cardiology.

**Table 1.** Pediatric Heart diseases and the role of AI.

**Table 2.** AI algorithms

 in Pediatric Cardiology.

#### *3.1. AI and Heart Murmurs*

AI has the potential to improve the validity of auscultatory findings for diagnosing CHD [87–89]. The limitations of objective performance data have restricted wide acceptability so far [90–92]. The auscultatory findings of CHD are integral to the clinical diagnosis and have the benefit of being a low-cost tool, but are subject to clinical expertise, which becomes a limitation in resource-limited countries and creates a need for support to the clinician [93–96] that is objective and reportable by even peripheral health workers. The lack of trained cardiologists at the peripheral level leads to an unavoidable miss of a timely clinical diagnosis of CHD, contributing to delayed intervention and thus a poor prognosis [97]. The emergence of AI-based digital stethoscopes and cloud reporting for telemedicine has helped with the timely diagnosis and early intervention of CHD in reported samples [68,70]. The AI-based technologies also seem affordable, helping the current interest. The use of an intelligent diagnostic system based on AI algorithms such as wavelet analysis and artificial neural networks (Figure 2) has shown a specificity of 70.5% and a sensitivity of 64.7% [71,72]. The developments in AI for detecting cardiac murmurs have shown promise in terms of sensitivity but still require clinical validation before wide clinical recommendation [73,98].

**Figure 2.** A model of application of neural networks in pediatric cardiology.

#### *3.2. Image Processing with AI*

#### 3.2.1. Chest X-ray

CHDs present with some classical chest x-ray signs that can help with suspicion of the disease, including boot-shaped heart (Tetralogy of Fallot), egg-on-string (Transposition of Great Arteries), snowman sign (Total Anomalous Pulmonary Venous Return), scimitar sign (Partial Anomalous Pulmonary Venous Return), gooseneck sign (Endocardial cushion defects), a figure of three (Coarctation of Aorta) and box-shaped hear (Ebstein anomaly) [99]. Chest radiography being easy, cost-effective, and readily available allows a direct or collaborative diagnostic approach to CHD. The evolution of deep learning models and machine learning models and the establishment of defined pediatric datasets have allowed the entry of AI for use in the pediatric population [74]. AI can be immensely beneficial in various steps of imaging such as ordering tests, reporting communication, enhancing the quality of images, and aiding radiologists in interpreting images [77].

#### 3.2.2. MRI

Cardiac MRI has evolved as a precise method for structural and functional evaluations of the heart [100]. The technology has evolved over the years from traditional techniques such as cardiac gating and the suspension of breathing to newer advanced techniques of high-field-strength magnets, high-performance gradient hardware and ultrafast pulse sequences. In the discipline of magnetic resonance imaging, the advancement of AI has allowed for shorter scan times, resulting in higher patient satisfaction; it also reduces errors by minimizing motion artefacts caused by patient movement. The segmentation of cardiac chambers helps visualize them better and aids in diagnosis. Currently, we do this manually, and a shift to AI will help with a faster diagnosis and reduce variation between different analysts [69].

#### 3.2.3. Echocardiography

New AI-based technologies have revolutionized modern medicine in obtaining fetal echocardiograms with improved precision and accuracy. Combining it with machine learning has been quintessential in making predictions of future variables associated with disease progression using retrospective patient data. It has helped in eliminating limitations associated with a lack of expertise in fetal echocardiology, fetal movements, and fetal heart size [19,74]. Machine learning-based systems have been useful in differentiating pathological versus physiological hypertrophic remodeling of the heart [59]. Deep learning models have demonstrated superiority in terms of sensitivity and specificity up to 76% and 88%, respectively, in diagnosing atrial septal defect (ASD) compared with pediatric cardiologists who demonstrated sensitivity and specificity of 53% ± 0.04 and 67% ± 0.10 respectively [35]. Automation, AI, and machine learning are game-changing as complementary tools to physicians and in areas with limited expert medical personnel or cardiologists [60]. Fetal intelligent navigation echocardiography (FINE) has presented as a novel method for fetal echocardiography using "intelligent navigation" technology to obtain nine standard views—four-chamber, aortic arch, three vessels, and trachea—and display abnormalities with great sensitivity helping to detect CHD [23,79].

#### 3.2.4. ECG

The use of deep learning (DL), an application of AI, in the field of adult cardiology has been well studied [64]. Its application in pediatric cardiology has become especially relevant due to its potential for allowing early diagnosis, and thus better prognosis, in congenital heart diseases (CHDs). DL models such as convolutional neural network (CNN) and recurrent neural network (RNN) are used in conjunction with electrocardiograms, which remain the staple diagnostic tool for CHDs, to provide enhanced diagnostic information that is otherwise only deduced with input from specialists [35]. Convolutional neural networks can perform image processing and classification, providing an advantage of extracting additional ECG information that would otherwise either be undetected [82]. While the efficiency and utility of using AI models with ECGs for CHDs is evident, the enhancement of the interpretability and training of deep learning models is still needed for widespread implementation [75].

#### *3.3. Prognosis and Risk Stratification*

AI-based algorithms have proven to be of great help for pediatric cardiology in the clinical examination, diagnosis, procedural planning, and management of cardiac interventions [12]. AI models have also aided with the extraction of patient data for risk stratification and ambulatory health monitoring from wearables [15]. Machine learning (ML)-based models such as optimal classification trees (OCTs) have accurately predicted mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) even with nonlinear data in patients with a history of congenital heart surgery [25]. Similarly, ML algorithm-based models, extreme gradient boosting (XGBoost), and RCRnet have accurately predicted preoperative mortality odds in patients with CHDs and statistically significant prognostic indicators along with risk stratification markers in patients with Tetralogy of Fallot and left-to-right shunt CHDs, respectively [41,53]. Importantly, the far-reaching utility of AI-based models in risk stratification and prognostic predictions is the trainability of models to work with various data cohorts [76].

#### *3.4. Planning and Management of Cardiac Interventions*

The current approach to planning and the execution of interventions in CHD relies primarily on generic treatment protocols derived from biological data and set guidelines. As such, there is a lack of tailor-made interventions based on each patient, which would drastically improve outcomes and post-intervention prognosis [78]. Artificial intelligence (AI), with its applications in the form of machine learning (ML) and deep learning (DL) among many, has emerged as a tool aiding the creation of personalized intervention plans as well as an accurate data extractor to ascertain potential post-operative sequelae [80]. The supervised machine learning models such as k-nearest neighbor classifier (KNN) and support vector machine classifier (SVM) have also aided in the intra-operative assessment of cardiac fluid kinematics, which are significant determinants of successful congenital heart surgeries [81]. The subsequent post-operative management of CHDs is imperative for maintaining the structural fluid dynamics and thus the prognosis. This could be done seamlessly with the help of recurrent neural networks based on ML models and deep learning models, allowing for an integrated system that can enhance survival outcomes in pediatric patients [101].

#### *3.5. AI in Cardiac Surgeries*

AI can revolutionize pediatric surgery in all stages of surgery: preoperative, intraoperative and post-operative. Preoperative risk assessment and decision-making can be made easier by the tremendous processing and analyzing capacity of AI-enabled algorithms. Surgical decision support systems using ANN can predict post-op outcomes and can prevent morbidity and mortality arising from poor risk assessment pre-operatively.

Tech-enhanced (Hitech) operation theaters can enable intraoperative interventions and decision support [83,102] and bring about a paradigm shift in telesurgery; it is especially useful in cardiac surgeries, where manual segmentation of retrieved images (CT/MRI) can take an unreasonable amount of time. Humans embarked on this path successfully when Xiaowei Xu et al. utilized AI to perform a cardiac surgery remotely in a patient having complications of long-standing ASD through the backbone of 5G technology. This was possible since AI replaced manual segmentation and provided accurate results in just two minutes compared to the traditional manual segmentation which takes 2–3 h even by experts [69]. In this situation, the authors helped a patient who could not be transported to another hospital due to her frail condition; we can extend the same advantage to a remote inaccessible location especially in rural areas and developing countries [103].

Postoperative AI can help in various aspects such as ambulatory patient monitoring post-discharge with AI wearables and automated risk stratification of patients to enable stricter follow-ups. Mahayni AA et al. described an ECG based AI algorithm that predicts ventricular dysfunction post-surgery, predicts long-term mortality in cardiac surgeries. Such algorithms can be developed and implemented for pediatric surgical procedures and drastically improve surgical outcomes [104].

#### *3.6. AI in other Pediatric Heart Diseases*

Kawasaki disease (KD) is an acquired pediatric vasculitis that can lead to coronary artery aneurysms and acute coronary syndrome [105]. Very little is known about the pathogenesis of Kawasaki disease but a recent study using AI-guided signatures reveals a shared pathology with Multisystem inflammatory syndrome in children (MIS-C), COVID-19 associated vasculitis in children. Both these syndromes share systemic inflammatory storm with similar cytokines such as IL15/IL15RA. Such AI-based investigative approaches will further elucidate its complex pathogenesis and help decipher novel diagnostic and therapeutic targets [86].

The appropriate and timely management of Kawasaki disease can significantly reduce the coronary complications associated with Kawasaki disease which contribute greatly to mortality in adulthood [106]. AI-based approaches can prove to be extremely crucial in predicting the risk of developing a coronary aneurysm. It is widely known that Kawasaki patients with intravenous immunoglobulin resistance are at a higher risk of developing coronary artery aneurysms, but most scoring models present for predicting the resistance are impractical. Wang T et al. describe a machine learning-based model on patient data that can predict intravenous immunoglobulin resistance in Kawasaki disease patients successfully. The scope for several such prediction models is possible with the advent of AI and can support clinical decisions to improve patient outcomes [84].

Distensibility changes in a coronary artery can be used to predict acute coronary disease in these patients. Benovoy M et al. calculated these changes in KD using an automated deep learning approach and correlated it with the severity of OCT (Optical coherence tomography) findings of KD-related CA damage [36]. Despite, the limited studies using AI-based approaches, AI has the potential to revolutionize risk stratification and prognosis estimation of Kawasaki disease as well as paving way for newer drug targets by elucidating the pathogenesis.

RHD—The screening of RHD requires the clinician's expertise, and hence the grassroot level is limited by a lack of skilled human resources. Automatic diagnosis of echodetected RHD is feasible and can form the core of the screening programs of the future covering the workload of experts [85]. Recent data has also shown promise for a convolutional neural network-based deep learning algorithm to identify heart sounds as 'rheumatic' with an overall accuracy of 96.1% having 94.0% sensitivity and 98.1% specificity [67]. Therefore, AI can form the backbone of future global screening programs for RHD.

#### *3.7. AI Algorithms in Pediatric Cardiology*

In many areas of pediatric cardiology, AI-based algorithms can be beneficial, including: (1) clinical examination and diagnosis, (2) image processing, (3) fetal cardiology, (4) prognosis and risk stratification, (5) precision cardiology, and (6) planning and management of cardiac interventions. Machine learning algorithms are a very promising tool for diagnosing and assessing critical and non-critical CHD; however, extensive research is still required to develop interpretable, robust, and generalizable models for clinical application, especially in light of the extreme heterogeneity of complex CHD. Various such AI algorithm models have gained interest over the past decade, and the present time has seen many motivating developments. The most relevant ones for our discussion are summarized in Table 2 and Figure 3.

**Figure 3.** AI-based algorithms and pediatric cardiology.

#### **4. Discussion**

AI is rapidly growing, and its significance in clinical practice cannot be negated. It plays a crucial part in augmenting and standardizing care by adding to a physician's skills and expertise. The role of AI in pediatric cardiology has greatly evolved over the past two decades (Figure 4). It picks up subtle or unrecognizable features, preventing missed diagnoses and leading to a better prognosis. Physicians can combine their clinical expertise with AI to enhance their outputs in the domains of prevention, predictive intervention, and health maintenance. AI can make use of continuous data received from wearable devices to offer insight into patients' behaviors and health trends. These features of AI empower the professional to provide the best care to the patient earlier in the course of the disease, helping to improve prognosis and leading to better outcomes.

**Figure 4.** Diagnostic and prognostic applications of AI in Pediatric Cardiology.

The current AI-based applications have greatly enhanced the diagnostics and prognostic areas of pediatric cardiology. The applications with the most promising potential are:

AI seems to be the tool of rich and economically advanced countries, but it can be the magic wand for risk stratification troubles in lower- and middle-income countries (LMICs). AI has the potential to reduce global health inequalities [107]. It can help serve LMICs by bridging gaps in equity, availability, affordability, and accessibility [108]. For instance, the major addition of AI is a reduction in the costs of dimensions requiring expensive equipment and specialized expertise, including tools of screening and planning that are unavailable in most hospitals, especially in LMICs. AI-based innovations can help overcome resource-constrained environments.

The advent of AI has allowed smart tools to assist peripheral health workers in helping with a rough screening at the doorstep and in peripheral areas having poor doctor–patient ratios [60]. The availability of skilled cardiologists to diagnose CHD in remote locations will require a giant leap in these countries. AI-based medical equipment can aid peripheral health care providers in helping with screening and referral. It can also help in terms of teleconsultation and remote radiology [109]. Furthermore, with the availability of AIbased chatbots or virtual avatars and characters, we can offer greater help to populations suffering from stigmatizing pathologies (such as HIV/AIDS and psychiatric pathologies). This therefore can aid in improving access to health care and follow-up services. One of the biggest challenges to health care is language, with improving translation tools, AI can help cover this area well too [110].

#### *4.1. AI: An Efficient Physician Assistant*

The full-blown implementation of AI still is resisted by physicians and health care staff fearing inaccuracies, the exclusion of social variables, and possible unemployment. The mechanization of health care, loss of empathy, and human touch to this extent often invite skepticism from the traditional medical community. Despite the fear-mongering and skepticism of loss of human touch and jobs, we opine AI can be an efficient aid to physicians rather than a replacement [111]. It can streamline insurance reviews, provide real-time data analysis, and assist in research. It can reduce the burden on physicians and burnout rates in the medical community. Thus, application in appropriate areas can contribute to the overall enhancement of health care delivery and experience [13].

#### *4.2. Challenges to AI in Pediatric Cardiology*

The incorporation of AI into pediatric cardiology has several challenges. The data available for pediatric cardiology is still very limited which is necessary to train AI algorithms to identify, assess and reduce inherent biases and overfitting. Furthermore, the heterogeneity in cardiac anatomy and the rarity of individual disease entities make data accessibility and AI incorporation into pediatric cardiology difficult. We can cater to this limitation by pooling data from all the different hospitals to get a large data set [19]. Imaging in the pediatric population has another challenge owing to their smaller size and frequent movements during imaging, which lead to higher motion artifacts. This poses a technical challenge, for instance, requiring higher spatial resolution in the MRI [69].

The doctor and patient may hesitate to use AI to replace the current protocols. The incorporation of AI will require health care providers to learn interpret data and accurately understand the many model parameters or model architectures. This challenge is being tackled in different ways. For example, some works have incorporated more intuitive interfaces in the models aiding easier interpretation [75].

Additionally, AI is evolving and several ethical concerns are arising. These concerns include informed consent to data access, data security and privacy, algorithm fairness and biases, and transparency [112]. Currently, there are no well-defined laws or regulations in place to address the legal and ethical issues that may arise with AI use in health care settings. However, as AI continues to grow and be used, laws and regulations can certainly be explored to ensure algorithmic transparency and protect data privacy.

#### **5. Conclusions**

AI can be touted as the next revolution in medicine. From making physicians' lives easier to enabling research with ease, it has variegated applications in the working environment as well as in the management of patients. Pediatric cardiology is a specialty requiring a great skill set in cognition and interpretation which makes it an ideal candidate for AI incorporation. AI has been successfully integrated into clinical examination, image interpretation, diagnoses, prognosis, risk stratification, precision medicine, and treatment in pediatric cardiology. The advent of AI has facilitated medicine to be more accurate and precise, but it is still a work in progress with challenges and limitations. Despite the roadblocks, we optimistically opine that AI with its current pace will streamline approaches in pediatric cardiology.

**Author Contributions:** Conceptualization, methodology, visualization, formal analysis and writing— Y.S., N.P. and N.K.; original draft preparation—Y.S., N.P., N.K., A.D., O.K., M.S., R.S. and H.H.; validation and writing—review and editing—Y.S., N.P., N.K., O.K., A.D., H.C., M.U.K., M.M.A.L., Z.Y.H. and T.B.E.; software, investigation, resources and data curation, Y.S., N.P., N.K., A.D., O.K. and M.S.; supervision and project administration—Y.S., N.P., N.K., M.U.K., M.M.A.L., Z.Y.H. and T.B.E. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data available within this manuscript.

**Acknowledgments:** Our gratitude goes out to Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Article* **Neuroimaging and Cerebrovascular Changes in Fetuses with Complex Congenital Heart Disease**

**Flaminia Vena 1,2,\*, Lucia Manganaro 3, Valentina D'Ambrosio 1, Luisa Masciullo 1, Flavia Ventriglia 4, Giada Ercolani 3, Camilla Bertolini 5, Carlo Catalano 3, Daniele Di Mascio 1, Elena D'Alberti 1, Fabrizio Signore 6, Antonio Pizzuti <sup>2</sup> and Antonella Giancotti <sup>1</sup>**

**Abstract: Background:** Congenital heart diseases (CHDs) are often associated with significant neurocognitive impairment and neurological delay. This study aims to elucidate the correlation between type of CHD and Doppler velocimetry and to investigate the possible presence of fetal brain abnormalities identified by magnetic resonance imaging (MRI). **Methods:** From July 2010 to July 2020, we carried out a cross-sectional study of 63 singleton pregnancies with a diagnosis of different types of complex CHD: LSOL (left-sided obstructive lesions; RSOL (right-sided obstructive lesions) and MTC (mixed type of CHD). All patients underwent fetal echocardiography, ultrasound evaluation, a magnetic resonance of the fetal brain, and genetic counseling. **Results:** The analysis of 63 fetuses shows statistically significant results in Doppler velocimetry among the different CHD groups. The RSOL group leads to higher umbilical artery (UA-PI) pressure indexes values, whereas the LSOL group correlates with significantly lower values of the middle cerebral artery (MCA-PI) compared to the other subgroups (*p* = 0.036), whereas the RSOL group shows a tendency to higher pulsatility indexes in the umbilical artery (UA-PI). A significant correlation has been found between a reduced head circumference (HC) and the presence of brain injury at MRI (*p* = 0.003). **Conclusions:** Congenital left- and right-sided cardiac obstructive lesions are responsible for fetal hemodynamic changes and brain growth impairment. The correct evaluation of the central nervous system (CNS) in fetuses affected by CHD could be essential as prenatal screening and the prediction of postnatal abnormalities.

**Keywords:** congenital heart disease; brain abnormalities; Doppler velocimetry

#### **1. Introduction**

Congenital heart diseases (CHDs) represent some of the most frequent fetal and neonatal abnormalities, which seem to affect 9 per 1000 live births [1]. These numbers may underestimate the real prevalence, which includes 20% of the spontaneous miscarriages and 10% of intrauterine demises [2]. CHD involves a huge variety of cardiovascular defects which could have a detrimental effect on neonatal and infant outcomes as well as a great impact on personal and family's quality of life [3]. An extensive body of evidence has already assessed an association between CHD and neurocognitive impairment, as a direct

**Citation:** Vena, F.; Manganaro, L.; D'Ambrosio, V.; Masciullo, L.; Ventriglia, F.; Ercolani, G.; Bertolini, C.; Catalano, C.; Di Mascio, D.; D'Alberti, E.; et al. Neuroimaging and Cerebrovascular Changes in Fetuses with Complex Congenital Heart Disease. *J. Clin. Med.* **2022**, *11*, 6740. https://doi.org/10.3390/ jcm11226740

Academic Editors: Massimiliano Cantinotti and Raffaele Giordano

Received: 13 October 2022 Accepted: 9 November 2022 Published: 14 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

consequence of the postnatal cardiac surgery on brain development [4–6]. Interestingly, the use of neuroimaging techniques, such as magnetic resonance imaging (MRI) and functional-MRI (f-MRI), suggest the hypothesis of brain abnormalities even before birth [7–14].

Therefore, several authors have studied the hemodynamic changes in fetuses affected by various subtypes of CHD [15–20]: in particular, they evaluated the presence of hypoplastic left heart syndrome (HLHS) on the onset of cerebral abnormalities and cardiovascular changes. This CHD consists of an inadequate left cardiac output and a negligible flow into the ascending aorta [21]. This leads to a necessary redistribution of energetic substances, as a consequence of this impaired circulation [15–20]. This inadequate hemodynamic flow can severely compromise myelinization and growth of brain cells as well as its microstructure, leading to the risk of a white matter impairment [12,22].

Consequently, exploring the hemodynamic adaptation of CHD and the possible relationship between heart disease and neurological impairment becomes of paramount importance.

Therefore, the primary aim of this study is to detect potential correlations between the type of CHD and changes in Doppler velocimetry. The secondary aim is to investigate the presence of central nervous system (CNS) abnormalities in fetuses affected by complex CHD.

#### **2. Materials and Methods**

From July 2010 to July 2020, a cross-sectional study was carried out, recruiting pregnant women referred for fetal echocardiography. The inclusion criteria required a diagnosis of one of the following complex CHD:

Exclusion criteria included (1) gestational age less than 20 weeks or greater than 40 weeks, (2) cardiac lesion other than the ones listed in the inclusion criteria, (3) age less than 18 years (4) persistent non-sinus rhythm, (5) fetal anemia (6) maternal condition that might affect fetal hemodynamics, such as fetal growth restriction, gestational diabetes, thyroid disease, or pre-eclampsia, (7) presence of any kind of extracardiac anomalies or neurologic malformations detectable with US, MRI and invasive procedures such as amniocentesis, (8) monochorionic twins and (9) chromosomal and sub-chromosomal anomalies, analyzing amniotic fluid samples.

All patients included in the study group as part of a research protocol underwent:

#### *2.1. Fetal Echocardiography*

Fetal echocardiography was conducted according to the International Society of Ultrasound in Obstetrics and Gynecology guideline [23] using a WS80A Elite scanner (Samsung Electronics, Seoul, South Korea) equipped with a 6 MHz curvilinear transducer.

Second-level echocardiography was performed in all fetuses following the sequential and systematic approach of heart evaluation. Multiple two-dimensional views were obtained to evaluate fetal heart anatomy. Doppler flow was employed to evaluate valve competence, stenosis, and shunting. M-mode was used to assess the cardiac rhythm. Doppler color flow mapping was used to identify the umbilical vessels; subsequently, a

reduced color scale was used to identify the circle of Willis and the middle cerebral artery (MCA). All the CHD prenatally diagnosed were confirmed with an echocardiography performed postnatally.

#### *2.2. Ultrasound Evaluation*

All ultrasound evaluations were performed with a Voluson 730 Expert GE or Samsung Elite WS80A machine. Two full-time certified sonographers (F.V., A.G.) performed all the ultrasound scans. A first-trimester evaluation assessed the exact gestational age using the last menstrual period or fetal crown–rump length (CRL) [24].

The following fetal biometric parameters were analyzed: biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC) and femoral length (FL). Estimated fetal weight (EFW) was calculated according to the method of Hadlock et al. [24,25]; both estimated fetal weight and birth weight centile were obtained using local reference curves [26].

IUGR was defined as birth weight (BW) below the 10th percentile for gestational age or estimated fetal weight or abdominal circumference below the 5th percentile at the mid-trimester anomaly scan, in presence of maternal and/or fetal Doppler anomalies [27]. Conversely, a fetus was detected as small for gestational age if the EFW or AC were below the 10th percentile, according to gestational age, or if the Z-score was below 2 [28].

Pulsed-wave Doppler was used to determine blood flow velocities in the umbilical artery (UA) and MCA. The peak systolic velocity, peak diastolic velocity and mean velocity were measured from stable signals during fetal apnea. The pulsatility index (PI) is a measure of vascular resistance in the circulatory bed downstream from the point of Doppler sampling. It is calculated according to the relationship: PI = (systolic velocity − diastolic velocity)/mean velocity. The MCA-PI to UA-PI ratio was labeled as the cerebroplacental (CPR) ratio [29,30].

#### *2.3. Magnetic Resonance Imaging*

MRI examinations were acquired using a 1.5 T Magnet (Siemens Magnetom Avanto, Erlangen Germany) without maternal–fetal sedation with one or two surface coil phased arrays.

The study protocol included the following sequences with the multiplanar acquisition (axial, coronal, sagittal) [31]:

The following parameters were evaluated: biometry (Fronto-Occipital Diameter, cerebral biparietal diameter (BPD), Transverse Cerebellar Diameter, height of the vermis, Antero-Posterior Diameter of the vermis and length of Corpus callosum), ventriculomegaly (VM), gyration, and signal intensity.

#### *2.4. Genetic Counseling*

Genetic counseling was proposed to all the couples in presence of CHD. Amniocentesis for karyotype and CGH-array was proposed in all CHD cases; fetuses showing chromosomal and copy number variations (CNV) were excluded from the conducted analysis.

#### *2.5. Statistical Analysis*

The statistical analysis was performed using the Statistical Product and Service Solutions software (SPSS) version 20 for Windows (SPSS Inc., Chicago, IL, USA). Descriptive analyses were presented as frequency with percentage, mean and standard deviation for all variables considered.

We converted the PI measurements into Z-scores using published normative data from a cohort of 72,387 healthy fetuses from the Fetal Medicine Foundation database (https://fetalmedicine.org, accessed on 21 March 2022) [20,32]. In this way, the conducted analyses turn out to be independent of the gestational age. A Z-score equal to 0 refers to the mean of the normal data and a Z-score equal to ±1 and ±2 is at 1 and 2 SDs from the mean, respectively.

Doppler indices were compared between diagnostic groups using one-way ANOVA to determine differences between two groups. Chi-square test and *t*-test were used for intergroup correlations. *p*-values < 0.05 were considered statistically significant.

#### *2.6. Ethical Approval*

The study was approved by the Institutional Review Board of the Department of Maternal and Child Health and Uro-gynecological Sciences, Sapienza, University of Rome, Policlinico Umberto I, Italy (Report No.: 45/2010) as a quality improvement study with anonymized data. All the patients provided a written informed consent form, and all the followed procedures were in line with the Helsinki declaration's principles of 1975, as revised in 2000.

#### **3. Results**

During the study period, 170 individual fetuses suspected of CHD were evaluated by echocardiography. In 143 fetuses, CHD was confirmed. Eighty fetuses were excluded for: gestational age less than 20 weeks (*n* = 3), extracardiac anomalies (*n* = 29), chromosomal anomalies (*n* = 33), non-sinus rhythm (*n* = 5), maternal condition (*n* = 8), and monochorionic twins (*n* = 1; 2 pairs). In particular 120/143 (83%) underwent amniocentesis and a chromosomal anomaly was found in 27% of cases. Sixty-three fetuses were finally included for the analysis and evaluated between 19 and 38 weeks. The specific CHD diagnoses and the mean gestational ages at the time of the fetal echocardiogram are listed in Table 1.

**Table 1.** Descriptive analysis of main patients' characteristics using mean and standard deviation (SD) or *n* (%).

LSOL: left-sided obstructive lesions; RSOL: right-sided obstructive lesions; MTC: mixed type of CHD.

#### *3.1. Cerebroplacental Doppler Data*

Doppler values were obtained in 46/63 cases. The mean PI Z-scores for the UA and MCA and the mean CPR ratio Z-scores are shown in Table 2.

We did not observe a significant difference in the UA-PI values (*p* = 0.07), even with regard to the RSOL group, which had a higher UA-PI than the other groups. We found a significant difference in the MCA-PI (*p* = 0.036) values among all groups considered, with the LSOL group having a lower MCA-PI than the other ones. We did not observe statistically significant differences in the CPR values among all groups considered (*p* = 0.4343505). We observed a significant reduction in HC measures, as LSOL was associated with lower values. **Table 2.** One-way ANOVA Kruskal–Wallis Test using the z-scores of the variables analyzed, according to different types of CHD. CHD: congenital heart disease, HC: head circumference; UA-PI: pulsatility index of umbilical artery; MCA-PI: pulsatility index of middle cerebral artery; CPR: cerebroplacental ratio; LSOL: left-sided obstructive lesions; RSOL: right-sided obstructive lesions; MTC: mixed type of CHD.

#### *3.2. Brain Abnormalities*

MRI was performed in all fetuses. It was found that 36/63 (57.1%) fetuses had signs of brain abnormalities at MRI and in 27/63 (42.9%) brain MRI was normal. Brain alterations are listed in Table 3. Corpus callosum (CC) abnormalities and ventriculomegaly (VM) were present, respectively, in 16/36 (25.4%) and 13/36 (20.6%) of fetuses. We stratified the analysis according to the single groups of CHD (Table 3): most of the cases of brain abnormalities were detected in the MTC and Others groups.

**Table 3.** Frequencies of brain abnormalities according to the different types of CHD, using number (*n*) and percentage (%). CHD: congenital heart disease; LSOL: left-sided obstructive lesions; RSOL: rightsided obstructive lesions; MTC: mixed type of CHD.

We found a significant correlation between the reduced HC and the presence of brain alterations at MRI (*p* = 0.003). Conversely, we did not achieve statistical significance evaluating the correlation between the detection of brain anomalies and UA-PI, MCA-PI, and CPR values (Table 4).

**Table 4.** Correlations among US descriptors and the presence of brain injury at MRI, using mean (m) and standard deviation (SD). US: ultrasound; MRI: magnetic resonance imaging; CNS: central nervous system; HC: head circumference; UA-PI: pulsatility index of umbilical artery; MCA-PI: pulsatility index of middle cerebral artery; CPR: cerebroplacental ratio.

#### **4. Discussion**

This study investigates the relationship between the fetal cerebrovascular hemodynamic changes and the presence of CNS abnormalities in fetuses affected by CHD. We have observed that RSOL and LSOL CHD might cause a considerable change in Doppler velocimetry: in our LSOL group, 6/11 (54.5%) fetuses had an HLHS and the analysis of their Dopplers showed low MCA-PI values (*p* = 0.036). Different studies reported the same trend but without significant difference [20,33]. Kaltman et al. [20] also found that only fetuses with HLHS had a lower PIMCA (*p* = 0.001): this result might be attributed to the severity of obstructive lesions of fetuses with LSOL, which was inversely proportional to the amount of cerebral blood delivery. Interestingly, the use of Z-score index was able to completely remove the affect related to gender and gestational age, providing more comparable results.

We also observed registered a tendency to higher elevated UA-PI in fetuses with RSOL (*p* = 0.027), whereas in a previous study, Kaltman et al. reported a similar finding in their study, despite a significant elevation of UA-PI only in fetuses with severe RSOL. This could be related to the severe obstruction of the outflow tracts which could impair the diastolic blood flow in the UA, elevating UA-PI [20].

Evaluating the impaired hemodynamic flow that affected IUGR fetuses instead of SG, we decided to exclude IUGR fetuses from our analysis, because of the possible bias in Doppler velocimetry's assessment that might intrinsically affect and compromise its course [29]. Evaluating the CPR values, we did not find any statistically significant difference among the CHD subgroups, except for a tendency of lower CPR values in fetuses with LSOL, compared to the other CHD types. Therefore, this could be explained by the assumption that an obstruction of the left outflow tract might impact downstream pulsatility. In light of the above, the use of CPR as an indication of the brain-sparing effect may be inappropriate in the setting of CHD.

The type of CHD contributes to a blood flow distribution, resulting in devastating effects on neurological development [34,35]. In our study, we have found a significant correlation between reduced HC and the presence of brain alterations at MRI: this is in line with the scientific literature which showed a reduction in frontal brain area of fetuses with CHD with neurodevelopmental delay (NDD), in comparison with normal controls [15–17,36–39]. The exclusion of IUGR fetuses from our study allows us to demonstrate that, in fetuses with CHD, the reduced HC is mainly due to hemodynamic alterations. The etiology of the neurological delay is likely to be complex and multifactorial: some attributed it as a complication of surgery; conversely, it has been already well-established that pre- and peri-operative risk factors account for roughly 30% of poor neuro-developmental outcomes [40,41]. A systematic review examined the prevalence of prenatal brain abnormalities in fetuses with CHD [42]: three studies reported a 28% rate of structural brain abnormalities in fetuses with CHD, including abnormalities in brain's structure, volume and blood flow. In our study we did not find any statistically significant correlation between the CNS morphological alterations and the type of CHD. The lack of statistically significant data could be related to the limited sample size of LSOL and RSOL subgroups.

Our statistical data analysis showed that up to 57% of fetuses with CHD had brain abnormalities; particularly in the groups of LSOL (63%) and Others (70%). The most frequent alteration was observed in the corpus callosum size, usually related to fetal biometry. This finding was also reported by Ng et al. who carried out a study using tensor-based morphometry: they found a high rate of brain's volume alteration in infants with CHD compared to healthy control infants. They detected different development in gray, white matter and corpus callosum size, without establishing a direct correlation between the CHD subtype and the morphologic abnormalities. It seems that the regional brain involvement could be related to different oxygen demand and cerebral hemodynamic changes that affected CHD samples [43].

The international guidelines allow the MRI brain evaluation only in fetuses affected by HLHS, despite the consolidated literature evidence of brain abnormalities present in more than 1/3 of CHD fetuses [44,45].

This research represents one of the few studies in the literature which investigate the hemodynamical changes and brain abnormalities in fetuses with complex CHD using two different imaging techniques: US and MRI. Our results confirm the advantages of performing fetal brain MRI in fetuses with complex CHD to characterize and manage the structural and hemodynamic brain modifications even before birth.

Conversely, there are few limitations: first, the relatively small number of cases for the single complex CHD categories, which has prevented a detailed analysis by a single type of CHD. Second, we did not include a control group and specific gestation windows to compare the Doppler velocimetry indexes; this limitation is partially overcome with the use of Z-scores, which allows us to view our data in the context of previously published normal values. The mean MCA-PI Z-scores for the LSOL and RSOL groups were −0.75 and 0.89, respectively. While these values are within the range of normal (within a Z-score range of ±2), they suggest a deviation from the mean of the normal population. In addition, outcome data, such as HC, birth weight, and the MRI scans of the brain at birth, were not obtained due to our inability to follow the antenatal and postnatal progress of these fetuses.

#### **5. Conclusions**

Our data seem to support that complex CHD impairs the growth of the central nervous system. Left and right-sided cardiac obstructive lesions modify the fetal cerebrovascular resistance. We demonstrated that MCA-PI is lower in fetuses with LSOL, and, according to previous study, we registered higher UA-PI in fetuses with RSOL even if not statistically significant, due to the smaller sample of RSOL group than the other groups. Furthermore, we did not find any statistically significant correlation between the CNS morphological alterations and the type of CHD, but we underline the importance of the study of the fetal brain when a cardiac abnormality is present. Alterations in cerebrovascular blood flow distribution may be associated with the postnatal, neurological abnormalities found in some newborns with complex CHD. Results of the multicenter international children NEUROHEART ongoing study will compare and describe preoperative markers on CHDaffected fetuses in prenatal as well as postnatal brain functional monitoring.

**Author Contributions:** Conceptualization, F.V. (Flaminia Vena); data curation, L.M. (Lucia Manganaro) and E.D.; investigation, V.D. and L.M. (Luisa Masciullo); methodology, G.E.; supervision, C.C.; validation, F.V. (Flavia Ventriglia) and A.P.; writing—original draft, C.B. and D.D.M.; writing—review & editing, F.S. and A.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was approved by the Institutional Review Board of the Department of Maternal and Child Health and Uro-gynecological Sciences, Sapienza, University of Rome, Policlinico Umberto I, Italy (Report No.: 45/2010) as a quality improvement study with anonymized data. All the followed procedures were in line with the Helsinki declaration's principles of 1975, as revised in 2000.

**Informed Consent Statement:** Written informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Article* **Detection of Coronary Artery and Aortic Arch Anomalies in Patients with Tetralogy of Fallot Using CT Angiography**

**Zsófia Kakucs 1,\*, Erhard Heidenhoffer 2,\* and Marian Pop 3,4**

**Abstract: Background:** Tetralogy of Fallot (TOF) is the most common form of cyanotic congenital heart disease (CHD). Furthermore, the prevalence of anomalous origin of a coronary artery is higher in patients with TOF than in the general population (6% vs. ≤1%). Preoperative assessment of cardiovascular anatomy using computed tomography (CT) angiography enables the adaptation of the surgical approach to avoid potentially overlooked anomalies. Our purpose was to determine the prevalence of coronary artery and aortic arch anomalies in a cohort of TOF patients. **Methods:** In this retrospective analysis, data were collected from CT reports (2015–2021) of 105 TOF patients. All images were acquired using a 64-slice multi-detector CT (MDCT) scanner. **Results:** The median age of the patients was 38.7 months, with a male-to-female ratio of 1.39. The overall prevalence of coronary artery anomalies (CAAs) was 7.61% (8 of 105 cases). The anomalous origin and course of coronary arteries across the right ventricular outflow tract (RVOT; prepulmonic course) were defined in 5.71% of cases (six patients). In four of these, the left anterior descending artery (LAD) originated from the right coronary artery (RCA), while in two cases, the RCA arose from the LAD. In the remaining two patients, the coronary arteries followed an interarterial course. The most frequent anomalous aortic arch pattern in the overall TOF population was the right aortic arch (RAA) with mirror image branching, seen in 20% of patients (21 cases). The most frequent anomaly of the supra-aortic trunks was bovine configuration, found in 17.14% (18 cases). **Conclusions:** The prevalence of CAAs and aortic arch anomalies detected by CT angiography was in line with the data reported in anatomical specimens. Therefore, this technique represents a powerful tool for the evaluation of congenital cardiovascular anomalies.

**Keywords:** CT angiography; Fallot tetralogy; coronary arteries; aortic arch anomalies

#### **1. Introduction**

Tetralogy of Fallot (TOF) is the most common form of cyanotic congenital heart disease (CHD), consisting of a ventricular septal defect, stenosis, or atresia of the pulmonary outflow; biventricular origin of the aorta; and right ventricular hypertrophy as a secondary feature (Figure 1C,D). This combination of defects occurs in 421 cases per million live births, constituting around 7–10% of all congenital cardiac malformations [1,2]. Echocardiography is the initial modality of choice for making the diagnosis and follow-up. Useful secondary diagnostic tools are electrocardiogram (ECG) and chest radiography. Findings from these tests are often suggestive but not definitive for the diagnosis of TOF. Invasive angiography is sometimes needed to establish the diagnosis and to provide detailed anatomy and hemodynamic characterization. As a good alternative to invasive cardiac catheterization, multi-detector computed tomography (MDCT) with high spatial and temporal resolution plays an important role in the evaluation of complex anatomical findings [3].

**Citation:** Kakucs, Z.; Heidenhoffer, E.; Pop, M. Detection of Coronary Artery and Aortic Arch Anomalies in Patients with Tetralogy of Fallot Using CT Angiography. *J. Clin. Med.* **2022**, *11*, 5500. https://doi.org/ 10.3390/jcm11195500

Academic Editors: Massimiliano Cantinotti and Raffaele Giordano

Received: 25 August 2022 Accepted: 17 September 2022 Published: 20 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** Representative CT angiography images. (**A**,**B**) Volume-rendering technique (VRT) reconstructions for CT images of 2 patients with Fallot tetralogy (ages: 7 and 14 months). Black arrow: anomalous course of the left anterior descending artery over the right ventricle outflow tract (RVOT). (**C**,**D**) Double oblique reconstruction of CT images in an infant with Fallot tetralogy. Red arrow: Mildly dilated aortic root overlapping the interventricular septum. Green arrow: Ventricular septal defect. Blue arrow: Narrowing of the right ventricular outflow tract (subpulmonary stenosis). The right ventricle (star) is mildly hypertrophied.

Coronary artery anomalies (CAAs) are a diverse group of congenital conditions with highly variable clinical presentation and pathophysiological mechanisms usually observed in the context of complex CHDs [4]. The prevalence of the anomalous origin of a coronary artery is higher in patients with TOF than in the general population (6% vs. ≤1%), as described by Koppel et al. [5].

Right aortic arch (RAA) anomalies are known to occur in association with cardiac outflow malformations, of which the most common type is TOF [6]. The prevalence of RAA varies between 13% and 34% in TOF patients, as opposed to 0.1% in the general population [7,8]. While the appearance of CAAs and RAA in TOF has been intensively analyzed, the number of studies describing the variety of branching patterns in patients with the left aortic arch (LAA) configuration is limited.

Preoperative identification of the coronary tree and aortic arch anatomy in patients with TOF is relevant to enable the adaptation of the surgical approach and avoid potentially overlooked anomalies. Some cases of CAAs crossing the right ventricular outflow tract (RVOT) are not detectable intraoperatively due to their intramyocardial course, overlying epicardial fat, or pericardial–epicardial adhesions from previous palliative surgery [9].

Consequently, preoperative computed tomography (CT) angiography may be essential in coronary assessment to minimize the risk of negative postoperative outcomes such as myocardial infarction and patient death. Furthermore, knowledge of aortic arch morphology is of crucial importance in deciding the appropriate access to palliative interventional procedures (e.g., constructing a systemic to pulmonary artery shunt), placement of monitoring lines, and cannulation for extracorporeal life support [7].

In the present study, our goal was to determine the prevalence of coronary artery and aortic arch anomalies in a cohort of TOF patients and to compare our results with findings from previous studies. The majority of studies were based on invasive coronary angiography (ICA) for assessing coronary anatomy; therefore, we also aimed to highlight the role of CT angiography in preoperative evaluation.

#### **2. Materials and Methods**

This retrospective analysis included 105 TOF patients who underwent CT angiography examinations between 2015 and 2021 for the evaluation of cardiovascular anatomy at a single tertiary care hospital. All examinations were performed using a 64-MDCT scanner (Definition AS, Siemens, Erlangen, Germany or Revolution HD, GE Healthcare, Milwaukee, WI, USA) using ECG gating and power injectors, with contrast volumes and flow rate according to local protocols.

The institutional reports database was queried to identify Fallot tetralogy patients who underwent CT examinations, and the reports were further analyzed. All reports were provided by a radiologist with 5+ years of experience in cardiovascular imaging (EACVI Level 3). The data were collected retrospectively from the CT report protocols, with variables being recorded in an MS Excel database (see Figures 1 and 2 for representative images). We included all patients with a diagnosis of TOF, including those with extreme variants (pulmonary atresia with major aortopulmonary collateral arteries (MAPCAs) or Fallot type double outlet right ventricle (DORV)). For patients with multiple examinations, the data were recorded only once. We recorded the following variables: gender, age, thoracic vessels anatomical course, and variants, as well as coronary arteries' course. CAAs and aortic arch anomalies were assessed in both unrepaired and repaired TOF patients. This is a small sample size and a single-center study; therefore, neonates, children, and adults were also included. Patients who underwent surgical repair in our center before 2015 were evaluated preoperatively through ICA as the golden standard. In this subgroup, CT angiography was executed as part of a preoperative screening protocol for later interventions if any acute cardiovascular event or late postoperative TOF complication arose. With the emergence of newer-generation CT-scanners and software, CT angiography was preferred to ICA due to its non-invasive approach, and, as such, it was the predominantly used evaluation method, preceding primary TOF repair after 2015, in our center.

From these data, the incidences of both surgically critical (prepulmonic course) and non-critical CAAs and aortic arch anomalies were calculated.

All statistics were performed using GraphPad InStat. Associations were tested using Chi-test with a significance level *p* < 0.05. The Emergency Institute for Cardiovascular Diseases and Heart Transplant ethics committee approved the tertiary analysis of data through address 8984/2020.

**Figure 2.** Representative image for aortic arc anomalies. (**A**) Left-sided aortic arch with left carotid artery (white arrow) from brachiocephalic trunk ("bovine arch"). (**B**) Right-sided aortic arch (yellow arrow) with anomalous left subclavian artery (white arrow). (**C**) Left-sided aortic arch with aberrant right subclavian artery (white arrow). (**D**) Major aortopulmonary collateral arteries (MAP-CAs) feeding the left pulmonary artery (green arrow) and the apical portion of the left upper lobe (blue arrow).

#### **3. Results**

In this study, a comprehensive CT angiography evaluation was performed on 105 TOF patients, of whom 61 (58.1%) were male (male/female ratio of 1.39). The median age of this population was 38.7 months (interquartile range 6.9–179.4), with a broad age distribution ranging from 0 months to 47 years. The majority of participants were under 18 years of age, and more than one-third were neonates and infants (35.2%). Table 1 summarizes the demographic characteristics of these patients.

**Table 1.** Demographic characteristics.

IQR = interquartile range (IQR); M:F ratio = male to female ratio.

The overall prevalence of CAAs was 7.61% (8 of 105 cases). Anomalous origin and course of the coronary arteries across the RVOT (prepulmonic course) were defined in 5.71% of cases (six patients), representing 75% of CAAs. Within this group, the left anterior descending artery (LAD) emerging from the right coronary artery (RCA) was found in four patients (Figure 1A,B), while RCA originating from the LAD was seen in two cases. In the remaining two patients, the CAAs followed an interarterial course, with one case of LAD arising from the RCA and one case of RCA originating from the left main coronary artery (LM). Additional anatomical findings were noted, namely four other cases with a prominent conus artery similar in caliber to the RCA (Table 2).

**Table 2.** Coronary artery anomalies and prominent conus artery.

LAD = left anterior descending artery; LM = left main coronary artery; RCA = right coronary artery.

Of 105 patients with TOF, CAAs following a prepulmonic course were found in five patients in addition to aortic arch anomalies. Of these, LAD emerging from the RCA and bovine arch was observed in three patients, while LAD originating from the RCA and RAA with mirror image branching was seen in one. Another patient had RCA arising from the LAD in addition to RAA with mirror-image supra-aortic trunks. No statistically significant association was found between the prevalence of CAAs and anomalous aortic arch patterns (*p* > 0.9999).

The variation in aortic arch position and branching pattern is summarized in Tables 3 and 4. In total, 76.2% of patients (80) had LAA, while RAA was seen in 23.8% of cases (25 patients). As determined using the classification of Popieluszko et al. [10]—see Table 3—the most common aortic arch pattern among these patients was type 1 (normal configuration), which occurred in 40.95% of all cases. The second most frequently observed configuration was RAA (23.8%), followed by the bovine arch variant (16.19%; Figure 2A). Equal proportions of aberrant branching of the left vertebral artery (LV) and right subclavian artery were observed (2.85%; Figure 2C). Only one patient had both a bovine arch and an aberrant LV branching pattern (0.95%).

**Table 3.** Different types of aortic arch variations.

ARSA = aberrant right subclavian artery; LAA = left aortic arch; LCC = left common carotid artery; LSA = left subclavian artery; LV = left vertebral artery; MAPCAs = major aortopulmonary collateral arteries; PDA = patent ductus arteriosus; RAA = right aortic arch; RSA = right subclavian artery.

**Table 4.** Types of RAA according to the Edwards classification.

ALSA = aberrant left subclavian artery; LSA = left subclavian artery; PDA = patient ductus arteriosus; RAA = right aortic arch.

During the evaluation of LAA patterns, we found three unclassified arch anatomy features. In one patient, both the left common carotid artery (LCC) and left subclavian artery (LSA) arose from the anterior aspect of the aortic arch, while in another patient, only the LCC had an anterior origin. A separate origin of each supra-aortic trunk was also noted in one case. None of the patients in this series had a type 5 (common carotid trunk) branching configuration.

In Table 4, we present the various forms of RAA. Within this group, 84% (21 of 25 patients) had RAA with mirror image branching of the main vessels, 12% (three patients) had RAA with an aberrant left subclavian artery (ALSA; Figure 2B), and 4% (one patient) had RAA with a bovine arch. Three types of RAA exist in the literature, as defined by the Edwards classification scheme [11,12]. However, none of the CT scans showed obliteration or isolation of the LSA with collateralization (type III).

The most frequent anomalous aortic arch pattern found in the overall TOF population was RAA with mirror image branching, identified in 20% of cases (21 patients). The most frequent anomaly of the supra-aortic trunks was bovine configuration, found in 17.14% of cases (eighteen patients, one of them in association with RAA), followed by an aberrant course of the subclavian artery in 5.71% of cases (six patients, equal numbers of left and right subclavians).

Other vascular anomalies were detected using CT angiography: patent ductus arteriosus (PDA; eight patients, 7.61% of all cases), major aortopulmonary collateral arteries (MAPCAs; four patients, 3.8%; Figure 2D), prominent sinoatrial nodal artery (one patient, 0.95%), and ductal diverticulum (one patient, 0.95%). PDA was associated with RAA in two cases, while the remaining six patients had a left-sided aortic arch (Table 5).

**Table 5.** Other vascular findings.

LAA = left aortic arch; LCC = left common carotid artery; LSA = left subclavian artery; MAPCAs = major aortopulmonary collateral arteries; PDA = patent ductus arteriosus.

#### **4. Discussion**

The survival rate of neonates with TOF has increased over the years due to highly sophisticated imaging techniques and successful management of patients with CHDs. The treatment strategies currently used in TOF have improved, offering excellent long-term survival (30-year survival rate of 68.5% to 90.5%). However, reintervention procedures are still often required [13]. Diagnostic imaging findings provide key information about the anatomical relationships of cardiac and extracardiac structures and hemodynamic features prior to surgical repair. The goals of preoperative imaging in TOF are to establish the severity of the primary anatomical lesions and the degree of functional alterations, as well as identify associated anomalies (such as variants of aortic arch patterning, CAAs, PDA, and MAPCAs). Moreover, the assessment of the presence and extent of lesions is required to determine the optimal timing and approach of surgical interventions—either definitive early repair or a palliative interventional procedure followed by surgery at a later stage [14].

Inadvertent division of coronary vessels crossing the RVOT during right ventriculotomy or transannular repair can lead to serious complications. Thus, clear delineation of the origin and course of these arteries is vital for the selection of a suitable surgical approach. Alternative surgical techniques must be chosen individually for this subset of anomalies (e.g., transatrial-transpulmonary repair, RVOT stenting) [15,16]. Cases have been described where a CAA crossing the RVOT was not identified during the preoperative assessment and did not become clear during surgical repair, causing it to be damaged, followed by myocardial infarction [5].

Different methods of visualization of the coronary tree anatomy can be used during the surveillance of patients with TOF. ICA was considered the gold standard imaging modality to identify and classify CAAs. However, a potential complicating factor during ICA examination is the counterclockwise rotation of the aortic root, which causes overlapping of the right and left coronary arteries in anteroposterior projection [17]. The possible utility of ICA has been reappraised, and this technique is being progressively replaced by coronary computed tomography angiography (CCTA). As CCTA is non-invasive, it is more widely applicable for diagnosis [4].

Multi-detector CCTA offers a detailed characterization of cardiac structures and small vessels using static images as well as 3D reconstructions. Although no studies have compared coronary angiography and CT scanning directly, CCTA has been shown to be a promising substitute for ICA as a method of identifying aberrant coronary patterns [5]. The main limitations of CT angiography are the dose of ionizing radiation given to the patients and the potential induction of contrast-induced nephrotoxicity. However, the patient's radiation dose is higher during ICA when an appropriate pediatric protocol is used [18]. Moreover, invasive angiography also involves the administration of an intravascular contrast agent, leading to iatrogenic renal injury in susceptible individuals. It is important to note that the introduction of ECG gating and postprocessing techniques has resulted in significant improvements in image resolution, as well as reductions in acquisition time and radiation dose [4].

In adults, cardiac magnetic resonance (CMR) imaging has an established role in the diagnosis of CAAs. However, this method has had limited success in the evaluation of children as the smaller heart size and faster heart rate in pediatric patients lead to poor spatial and temporal resolution. Moreover, additional sedation may be needed for adequate image quality. In a recent study, CMR angiography did not perform well in patients younger than 4 months; diagnostic image quality was obtained in only 17% of cases, even though all examinations were performed under general anesthesia [19].

There is also increasing evidence for the successful identification of anatomical evidence by transthoracic echocardiography, which can be a suitable method of primary investigation in children that avoids radiation exposure. However, echocardiography is not routinely used for the visualization of coronary vessels in the adult population because of its lower spatial resolution and suboptimal acoustic windows [4].

Due to many possible anatomic variants, which are not considered anomalies, the term CAA, as a definition, has historically been restricted to those occurring in less than 1% of the general population [4]. The reported prevalence of CAAs in patients with TOF is between 2% and 23%. The overall prevalence of these congenital malformations was 6% in a meta-analysis including 28 studies, which was similar to our results (7.61%). In our TOF patients, the proportion of surgically significant anomalies with coronary arteries passing across the RVOT was 75% of all CAAs; again, this was similar to the percentage (72%) calculated in the previously mentioned large-scale meta-analysis [5]. Anomalous origin of the LAD from the RCA or the right sinus of Valsalva was the most common CAA in the majority of studies; this was also the vascular anomaly most frequently encountered in conjunction with prepulmonic course in TOF patients [5,14]. Our results correlated with these findings.

Solitary coronary arteries arising from one of the sinuses of Valsalva are also frequently reported CAAs in patients with TOF [9,17,20]. The prevalence varies between 0.0240% and 0.066% in the general population, whereas these anomalies are reported between 1.5% and 3.7% in TOF patients [21,22]. In the present study, a single coronary artery was found in three cases (2.85%), which is in line with the reported frequency in TOF patients. This type of CAA may cause myocardial ischemia by different mechanisms, even in the absence of atherosclerotic coronary lesions. The abnormal vessel angulations or courses may affect the distribution of blood flow, leading to serious complications. Coronary classification systems are useful tools for providing detailed information about these anomalies; however, neither of them can be applied in all TOF cases. The Leiden Convention coronary coding system has been shown to be a feasible method for the characterization of single coronary arteries in the setting of complex CHD; however, this classification is not routinely used in patients with TOF. In this classification system, the aortic sinuses are described as left- or right-facing or non-facing relative to the pulmonary valve sinuses; therefore, the applicability is limited in cases of pulmonary atresia. In such cases, a detailed description of the coronary anatomy, as well as associated characteristics, should be provided for treatment planning [23].

By the classic definition, five CAA course subtypes exist: prepulmonic, interarterial, subpulmonic (intraconal or intraseptal), retroaortic, and retrocardiac [24]. As in the general population, not all variations in CAAs are of equal clinical importance. Coronary arteries following an interarterial course (between the ascending aorta and the pulmonary trunk) with high-risk anatomic features (e.g., intramural tract, slit-like ostium) are considered malignant and require additional corrective surgery. In this study, the morphological aspects of these arteries were deemed benign.

It should also be noted that a single coronary artery with an interarterial course may increase the risk of major adverse cardiac events [4]. In the case of subpulmonic course, the coronary vessel exits the aorta below the pulmonic valve and traverses the RVOT, pulmonary infundibulum, and interventricular septum. The differential diagnosis between the intraseptal and the intramural interarterial course of a coronary artery is especially relevant and can be difficult; however, an intraseptal CAA has a more inferior position [25]. In this study, none of the CT scans showed CAAs with subpulmonic, retroaortic, or retrocardiac courses in patients with TOF. In our sample population, represented mainly by preoperative

TOF patients with a median age of 3 years, the clinical impact of the "malignant" variant remains to be investigated in follow-up examinations.

The conus artery is a small branch that usually originates from the proximal portion of the RCA. A large conus artery (as identified in four cases in this study) may supply a larger area of myocardium and therefore should undoubtedly be preserved during corrective surgery [26]. Variants larger than the RCA have been described, crossing over the outflow tract of the right ventricle and reaching the heart apex. These variants could be easily mistaken for an accessory LAD on ICA examination in the anteroposterior projection [27]. Hence, the true prevalence remains uncertain [5].

Congenital aortic arch malformations embody a large group of anomalies that result from the disordered embryogenesis of the pharyngeal arches, including abnormal or incomplete regression of one or more embryogenic vascular segments [28]. The recognition of these aberrations is of paramount importance in TOF patients to ensure accurate preoperative surgical decisions. In this study, the classic branching pattern of the aortic arch (left-sided aorta giving rise to the brachiocephalic trunk, LCC, and LSA) was found in 40.95% of patients, whereas the prevalence of this pattern is significantly higher in the general population (80.9%) [10]. This finding suggests that the occurrence of atypical aortic arch patterns is higher in TOF patients. According to the Popieluszko classification, the most frequently seen anomalous arch pattern in our patients was the RAA variant (23.8%). The incidence of RAA has been reported as around 25% in previous studies [7,18,29]. Apart from the normal branching pattern, the most common left-sided aortic arch pattern was the bovine arch, including the common origin of the innominate artery and the LCC, which were seen in 40.95% and 16.19% of patients, respectively. These findings are in accordance with those of Moustafa et al. (40.4% and 22.8%) [29] and Tawfik et al. (36% and 16%) [30].

The aberrant origin of one of the subclavian arteries occurs with a higher incidence in patients with TOF than in the general population, where it is less than 2% [31,32]. Aberrancy of the subclavian artery can influence monitoring line placement—if this anomaly is recognized preoperatively, the arterial line should be placed in the contralateral radial or the femoral artery [33,34]. Different palliative procedures are offered depending on clinical presentation, although the procedure most often carried out remains the modified Blalock–Taussig shunt, which involves inserting an interposed graft between the subclavian artery and the ipsilateral pulmonary artery [35]. Failure to identify an aberrant subclavian artery prior to surgery might lead to incorrect insertion of the shunt between the carotid artery and the pulmonary circulation [7].

Within the RAA branching configuration, a mirror image of supra-aortic trunks was observed in 84% of patients, whereas RAA with ALSA was found in 12% of cases. These findings are in accordance with the results of Prabhu et al., who identified RAA with mirror image branching in 86.6% of 688 patients and RAA with ALSA in 10.6% [7].

Fallot-type DORV (where DORV means abnormal ventriculoarterial connection with both great vessels arising, entirely or predominantly, from the right ventricle, and Fallottype means that DORV mimics elements of TOF) is an uncommon complex congenital heart anomaly in which early complete repair should be considered to avoid ventricular volume overload and progression of tricuspid valve regurgitation. The complex anatomy of Fallot-type DORV can make surgical correction a challenge; therefore, comprehensive preoperative assessment and planning are needed [36].

Tetralogy of Fallot with pulmonary atresia (TOF-PA) is a severe variant of TOF, characterized by a lack of antegrade flow into the pulmonary arteries. To provide additional blood flow to the pulmonary circulation, PDA is usually seen in association with TOF-PA. Stenting of PDA has gained acceptance for palliation in TOF-PA, although the PDA is usually elongated and tortuous, making the implantation of a rigid, straight stent challenging. Hence, advanced imaging with MDCT angiography is necessary for case selection and preprocedural planning [37]. The common carotid and axillary artery approaches are the most feasible routes for stent implantation. However, ductal stenting is contraindicated in

the case of the common carotid trunk, as damage to the vessels during direct surgical or percutaneous approach could lead to neurological deficits [7].

Finally, MAPCAs are persistent tortuous fetal arteries branching from the aorta or systemic arteries that form in an attempt to compensate for the underdeveloped pulmonary circulation via multifocal supply. TOF-PA with MAPCAs is a severe and rare type of congenital heart defect [38]. We found this malformation in just 5% of cases. Surgical management to restore normal pulmonary blood flow is challenging due to the heterogeneity of arborization in these patients [35]. Therefore, preoperative imaging is necessary to achieve a complete understanding of cardiovascular anatomy.

The systematic use of CT angiography for preoperative identification of CAAs and aortic arch patterns in TOF was scarcely reported. Therefore, one of the main objectives of our study was to highlight the role of CT angiography in the evaluation of complex anatomical findings, as a good alternative to invasive cardiac catheterization. Moreover, the number of studies describing the variety of aortic arch and branching patterns in TOF patients is limited, and most provide incomplete information regarding the entire spectrum of aortic anomalies. This led to our aspiration to encompass all classifiable and non-classifiable aortic malformations in this specific population.

#### **5. Study Limitations**

There are several limitations to the design of this study. First, this is a single-center, small-sample observational study based on retrospective data analysis. Therefore, no comparison of diagnostic accuracy was performed for CT angiography and ICA in TOF patients. As mentioned previously, no prospective studies comparing these diagnostic imaging methods have been found in the literature [5]. However, Gorenoi et al. declared that CCTA with at least 64 slices should be used to identify coronary alterations in order to avoid invasive investigation in patients with CHD [39].

Second, the concordance of coronary artery patterns between the preoperative CCTA and corrective surgical findings was not evaluated. However, Goo et al. demonstrated that dual-source CCTA with a high temporal resolution was useful for the assessment of coronary artery anatomy before corrective surgery in TOF, exhibiting not only a high concordance rate (95.0%) with surgical findings but also a high level of diagnostic accuracy (96.9%) [9].

Technical limitations were also present. Not only was it possible that different contrast agents might have impacted the quality of the CT examination [40], but the results of this study were obtained using a 64-slice last-generation CT scanner. In order to reduce the number of misinterpreted cases, higher temporal resolution CCTA is needed. The second-generation 128-slice dual-source CT is one of the most frequently used imaging modalities for evaluating TOF patients, enabling high temporal resolution and scanning speed, as well as a low radiation dose. Moreover, diagnostic accuracy is expected to be further improved with the recently introduced third-generation 192-slice dual-source CT system, which provides even better image quality at lower radiation doses [41].

#### **6. Conclusions**

The prevalence of CAAs and aortic arch anomalies detected by CT angiography in this study is in line with the data reported in anatomical specimens. Therefore, CT angiography represents a powerful tool for the evaluation of congenital cardiovascular anomalies.

**Author Contributions:** Conceptualization, Z.K. and M.P.; methodology, M.P.; software, Z.K..; validation, Z.K. and M.P.; formal analysis, Z.K.; investigation, Z.K. and E.H.; resources, M.P.; data curation, Z.K.; writing—original draft preparation, Z.K.; writing—review and editing, Z.K. and E.H.; visualization, Z.K.; supervision, M.P.; project administration, M.P.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Emergency Institute for Cardiovascular Diseases and Heart Transplant of Tirgu Mures (4920/2020).

**Informed Consent Statement:** This is a data analysis using secondary sources. Informed consent was obtained from all subjects/legal guardians prior to CT examinations.

**Data Availability Statement:** Data are available upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Article* **Atrial Function Impairments after Pediatric Cardiac Surgery Evaluated by STE Analysis**

**Massimiliano Cantinotti 1, Pietro Marchese 1,2, Marco Scalese 3, Eliana Franchi 1, Nadia Assanta 1, Martin Koestenberger 4, Alessandra Pizzuto 1, Vitali Pak 1, Giuseppe Santoro 1, Vivek Jani 5, Shelby Kutty <sup>5</sup> and Raffaele Giordano 6,\***

**Abstract: Background:** Applications of atrial speckle tracking echocardiography (STE) strain (ε) analysis in pediatric cardiac surgery have been limited. This study aims to evaluate the feasibility of atrial STE ε analysis and the progression of atrial ε values as a function of post-operative time in children after pediatric cardiac surgery. **Methods:** 131 children (mean 1.69 ± 2.98; range 0.01– 15.16 years) undergoing cardiac surgery were prospectively enrolled. Echocardiographic examinations were performed pre-operatively and at 3 different post-operative intervals: Time 1 (24–36 h), Time 2 (3–5 days), Time 3 (>5 days, before discharging). The right and left atrium longitudinal systolic contractile (Ct), Conduit (Cd), and Reservoir (R) ε were evaluated with a novel atrial specific software with both P- and R-Gating methods. One hundred and thirty-one age-matched normal subjects (mean 1.7 ± 3.2 years) were included as controls. **Results:** In all, 309 examinations were performed over the post-operative times. For each post-operative interval, all STE atrial ε parameters assessed were significantly lower compared to controls (all *p* < 0.0001). The lowest atrial ε values were found at Time 1, with only partial recovery thereafter (*p* from 0.02 to 0.04). All atrial ε values at discharge were decreased compared to the controls (all *p* < 0.0001). Significant correlations of the atrial ε values with cardio-pulmonary-bypass time, left and right ventricular ε values (*p* < 0.05), and ejection fraction (*p* < 0.05) were demonstrated. **Conclusions:** Atrial ε is highly reduced after surgery with only partial post-operative recovery in the near term. Our study additionally demonstrates that post-surgical atrial and ventricular ε responses correlated with each other.

**Keywords:** congenital heart disease; STE echocardiography; atria; pediatric cardiac surgery

#### **1. Introduction**

Speckle tracking echocardiography (STE)-derived myocardial strain (ε) analysis has demonstrated a significant prognostic value in pediatric cardiology [1–4]. Preliminary studies have reported both the feasibility and post-operative trends of left [4,5] and right [6] ventricular ε in children with congenital heart disease (CHD) after cardiac surgery. Atrial function has been shown to be an important predictor of cardiovascular outcomes in the adult population [1,2]. Atrial ε indices enable a better understanding of the overall function of the atrium, and there is increasing evidence to support the additional role of

**Citation:** Cantinotti, M.; Marchese, P.; Scalese, M.; Franchi, E.; Assanta, N.; Koestenberger, M.; Pizzuto, A.; Pak, V.; Santoro, G.; Jani, V.; et al. Atrial Function Impairments after Pediatric Cardiac Surgery Evaluated by STE Analysis. *J. Clin. Med.* **2022**, *11*, 2497. https://doi.org/10.3390/ jcm11092497

Academic Editor: François Roubille

Received: 16 March 2022 Accepted: 28 April 2022 Published: 29 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

atrial ε as a sensitive parameter of ventricular diastolic dysfunction [1,2]. Investigation of STE-derived atrial ε [1,2] to evaluate post-surgical outcomes in pediatric cardiology has been limited. While some studies [7,8] evaluated differences in the atrial ε response a few months after percutaneous versus surgical closure of atrial septal defects on small cohorts (10–30 subjects), no large-scale analysis has been performed in the pediatric population [7,8].

The recent availability of pediatric nomograms for atrial [9–11] ε values, including those obtained with dedicated atrial strain software [12], may allow for the comparison of post-operative atrial ε values with normal values, consequently enabling further understanding of the degree of alteration in atrial mechanics. The primary aims of this investigation were to (1) assess the feasibility of atrial STE ε analysis in a large cohort of children after biventricular cardiac surgery, (2) evaluate the progression of STE derived atrial ε as a function of post-operative time, and (3) to compare these findings to atrial ε values in normal age-matched controls.

#### **2. Methods**

From May 2018 to May 2019, children undergoing biventricular cardiac surgery for CHD were prospectively enrolled at a Single Institution (Fondazione CNR-Regione Toscana, G. Monasterio, Massa, Italy). Demographic data are reported in Table 1. Echocardiographic examinations were performed at three different post-operative times according to institutional protocols: Time 1 (*n* = 131) 24–36 h, Time 2 (*n* = 108) 3–5 days, Time 3 (*n* = 70) 6–9 days, and at the immediate pre-operative time (*n* = 95). In complicated patients, echocardiograms were repeated whenever required for clinical management, and no examinations other than those necessary for treatment were performed. Only subjects in which atrial strain analysis was deemed feasible for at least 80% of the parameters evaluated were included in this study. A group of 131 age-matched normal subjects (mean 1.7 ± 3.2 years old) were used as controls from a pool of 721 healthy children collected in a previous study [9]. Echocardiograms were performed on Philips iE33 systems (Philips Medical Systems, Bothell, WA, USA) using 8 Mhz and 5 Mhz transducers [9,13]. All studies were performed with simultaneous electrocardiographic monitoring. Images were obtained in the apical four-chamber (4Ch), three-chamber (3Ch), and two-chamber (2Ch) views for the evaluation of left ventricular strain, and in apical four-chamber (4Ch) for the evaluation of right and left atrium speckle tracking analysis. The following parameters were calculated off-line: LA and RA longitudinal reservoir ε (SR), conduit ε (SCd.), and contractile ε (SCt) [9,13–16]. A dedicated atrial ε package was used for analysis on a computer workstation (QLAB 10; Philips Medical Systems, Andover, MA, USA) according to recent guidelines [9–13]: interatrial septum was included, and the atrial appendages were excluded. Feasibility is meant as the capacity of the software to recognize and define all the atrial wall segments. Atrial ε analysis was validated when at least ≥ 80% of the atrial segments were recognized correctly.

After semi-automatic placement of basic markers (lateral and septal mitral/tricuspid annulus and septal roof) in end-diastole, the software automatically generated atrial contours and performed STE analysis in seven segments through the cardiac cycle [9]. Minimal manual adjustment of tracking was performed when required. For each parameter, the mean value of three consecutive measurements was obtained. The P waves (P-P gating) were used as the initiation of the ε calculation (Figure 1). The analysis was then repeated by using QRS complex (R-R gating) for the initiation of the ε calculation (Figure 2). Enddiastole and onset of atrial contraction were checked and manually corrected according to mitral inflow profile. Two experienced pediatric cardiologists (M.C., E.F.) acquired the images and performed the measurements. Rates of intra-observer and inter-observer variability were calculated from 20 subjects, randomly selected.

**Table 1.** Patients demographics.

AVSD = Atrioventricular Septal Defect; BSA = body surface area; CHD = congenital heart disease; CPB = cardio-pulmonary bypass; ICU = intensive care unit; LUS = lung ultrasound; LOS = length of stay; min = minutes; LVPO = Left Ventricle Pressure Overload; LVVO = Left Ventricle Volume Overload; RVPO = Right Ventricle Pressure Overload; RVVO = Right Ventricle Volume Overload; TGA = transposition of the great arteries; STAT-score = Society of Thoracic Surgeons/European Association of Cardio-Thoracic Surgery-STS/EACTS. § 1 tamponed, ◦ 1 tamponed, 1 Av block, \* 1 AV block, 2 diaphragmatic paralyses.

**Figure 1.** Left atrial ε analysis in the four-chamber view by using the atrial specific software for strain STE analysis with P-gating method. LA = Left atrium, LV = Left Ventricle, RA = Right Atrium, RV = Right Ventricle, Sr = strain reservoir, Sct = strain contractile, Scd = strain conduit.

The ejection fraction was calculated by the biplane Simpson method. Approval for this study was obtained from the Local Ethics Committee (Comitato Etico Meyer no. 62/2016). Parents or legal guardians of all the children were informed and provided written consent for participation in this study.

Images were acquired only in cooperative babies or in those who were sedated for clinical reason. No sedations just for image acquisition were performed. Only patients in sinus rhythm during the echocardiography were included in the present study.

**Figure 2.** Left atrial ε analysis in the four-chamber view by using the atrial specific software for strain STE analysis with R-gating method. LA = Left atrium, LV = Left Ventricle, RA = Right Atrium, RV = Right Ventricle, Sr = strain reservoir, Sct = strain contractile, Scd = strain conduit.

All images and measurements were acquired by two independent and experienced pediatric cardiologists (M.C., E.F.). The low rate of intra- and inter-observer variability in atrial [9] ε measurements has been described in the previous reports [9].

#### *Statistical Analysis*

All continuous variables and categorical variables were expressed as mean standard deviation (SD) and a number of cases and percentage, respectively. Comparison of continuous variables at different time points was performed using Wilcoxon test and nonparametric test for trends, as appropriate. Comparison of categorical variables at different time points was performed using a chi-square (Cochran–Armitage) test for trends in proportions. Comparison of age class was performed using a Mann–Whitney U test and a chi-square test as appropriate. Additionally, Pearson correlation coefficients (r) between strain values, operative data, and outcome parameters were determined. The control group of normal subjects was selected by 1:1 propensity score matching. Propensity score matching was calculated for each group with bivariate logistic regression analysis by age. All calculations were done by using SPSS v23 (SPSS Inc, Chicago, IL, USA) and STATA v13 software. A *p* < 0.05 was considered significant.

#### **3. Results**

#### *3.1. Population*

In all, 621 examinations were performed from May 2018 to May 2019 in 210 children (mean 2.25 ± 3.62 years; range 0.01–17.68 years). Seventy-nine patients were excluded for incomplete examinations (of these, 52 were not cooperative children and 27 had a poor acoustic window due to wounds and medications), leaving 309 examinations in 131 children (mean 1.69 ± 2.98 years; range 0.01–15.16 years) for final analysis. Among these children, 30 were neonates (0–31 days), 37 were infants (1–6 months), and 64 were >6 months. All demographic data are summarized in Table 1.

One hundred and thirty-one age-matched normal subjects (mean age 1.71 ± 2.94 years; range 0.03–14.12 years; mean BSA 0.43 ± 0.25) were included as controls. No differences in both age and BSA among healthy and CHD children were found (*p* = 0.94 and *p* = 0.657, respectively).

#### *3.2. Feasibility*

Feasibility, as assessed by the total number of studies from which relevant atrial ε parameters were acquired, ranged from 62 to 85% for all parameters. Feasibility was similar between all age groups, although at Time 1 and Time 2, it was marginally higher in neonates (≥80%) (*p* = 0.15 and *p* = 0.7). These results are summarized in Table 2.

**Table 2.** Feasibility at different post-operative times and in different age groups.

ε = strain, LAR = left atrial reservoir, LACT = left atrial contractile, RAR = right atrium reservoir, RACT = right atrium contractile.

#### *3.3. Comparison vs. Normal Subjects*

Pre-operative atrial ε values, obtained with either P-P or R-R gating methods, were significantly lower (*p* all < 0.0001), compared with normal subjects, with the only exception being the RA and LA contractile functions which were comparable with normal subjects.

#### *3.4. The Post-Operative Trend for Atrial STE ε*

Post-operatively, all atrial ε values decreased with the lower values observed at Time 1 with a progressive recovery thereafter (*p* ranging from <0.0001 to 0.027). At discharge, however, all atrial ε parameters remained significantly lower compared to the control group (*p* ranging <0.0001 to 0.004), with the only exception being the LA Ct ε function, which was similar to healthy subjects' values, as reported in Table 3. From Time 1 to Time 2, only the reservoir function, for both LA and RA, reported a significant increase (*p* from 0.007 to 0.022). From Time 2 to Time 3, the reservoir ε function, for the LA and RA, and the LA Cd ε function, reported a significant increase (*p* from 0.010 to 0.022). The remaining atrial functions showed a slower, however, significant, upward trend from Time 1 to Time 3 (*p* from 0.0009 to 0.002, *p* 0.0066 and 0.027, respectively). All these results are summarized in Table 3 and Supplementary Table S1, and Figures 3 and 4.

**Table 3.** Mean and standard deviations of examinations at different pre-/post-operative times and in control group.

ε = strain,R=reservoir, Ct = contractile, Cd = conduit.

**Figure 3.** Left Atrial Strain trends at different pre-/post-operative times and in comparison to the control group. Median and interquartile range of ε values over time are shown. *p* values in the patient group were determined relative to strain from Time 1 to Time 3. Horizontal line = median; Box = interquartile range.

**Figure 4.** Right Atrial Strain trends at different pre-/post-operative times and in comparison to the control group. Median and interquartile range of ε values over time are shown. *p* values in the patient group were determined relative to strain from Time 1 to Time 3. Horizontal line = median; Box = interquartile range.

#### *3.5. Comparison of Post-Operative Trend for Atrial STE ε with Pre-Operative Values*

Time 1 reported lower values in all LA and RA functions than the pre-operative data. Concerning Time 3, all LA ε values were totally recovered with values comparable with the pre-operative data (*p* > 0.05 each one), but, contrariwise, all RA ε values were still lower than the pre-operative data.

All these results are summarized in Table 3 and Supplementary Table S1.

#### *3.6. Differences among P- and R-Gating Post-Operative ε Values*

No significant differences in the post-operative time for the atrial ε trends were observed among values calculated with the P- and R-gating methods. Surgery atrial strain values calculated with P-gating, however, were constantly lower than those obtained with R-gating (*p* < 0.001).

#### *3.7. Differences among Age Groups*

Children younger than 6 months reported no significant differences from Time 1 to Time 2 in atrial STE ε with both gating methods. Children older than 6 months reported a significant increase from Time 1 to Time 2 only for the LA ε R function, through P-Gating (*p* 0.04), and for RA ε Cd through the two Gating methods, R and P (*p* 0.015 and *p* 0.017).

#### *3.8. Correlation of Atrial Strain with Operative Data and Outcome Parameters*

Strain parameters correlated with a CPB and cross-clamp time. At Time 1 (12–36 h post-surgery), CC inversely correlated with the RA conduit function in both the P- and R-gating methods (beta −0.04 *p* 0.009 and beta −0.004 *p* 0.045, respectively). Furthermore, a CPB is inversely related with the conduit function of both LA and RA (*p* all < 0.05), while it is inversely related with the reservoir function only for RA. Moreover, the LA reservoir function demonstrated an inverse correlation with the STAT-score (beta −3.78 *p* 0.008 for P-gating and beta −2.98 *p* 0.008 for R-gating).

No significant correlations were observed between the atrial STE ε values and the additional outcome parameters. Furthermore, no significant correlation was observed between the atrial STE values, body surface area (BSA), and age. These results are summarized in Supplementary Tables S2 and S3.

#### *3.9. Correlation of Atrial Strain with Left and Right Ventricular STE*

The atrial ε values correlated with the right/left ventricular ε and left ventricle ejection fraction (LVEF).

At Time 1 (12–36 h post-surgery), univariate regression analysis demonstrated that LV Longitudinal 4c, 2c, 3c, and global longitudinal (GL) ε values all positively correlated with all the LA longitudinal systolic ε with the R-gating method only (Supplementary Table S4). Right Ventricular ε, instead, did not correlate with LA and RA longitudinal systolic ε with both gating methods. Additionally, EF positively correlated only with LA ε R in both the Rand P-gating method (*β* = 0.28, *p* = 0.03 and *β* = 0.22, *p* = 0.03, respectively).

At Time 2, approximatively all atrial longitudinal systolic ε parameters positively related with all LV ε in the univariate regression analysis. Right Ventricular ε, instead, positively correlated only with RA ε R in both the R- and P-gating method (*β* = 0.44, *p* = 0.044 and *β* = 0.41, *p* = 0.023, respectively). Lastly, EF remained positively related with almost all atrial longitudinal systolic ε parameters. These results are summarized in Supplementary Table S4.

#### **4. Discussion**

In this study, we prospectively investigated atrial ε using STE and examined trends in atrial ε as a function of time after pediatric cardiac surgery. Defects assessed in this study included atrial septal defects, ventricular septal defects, atrioventricular septal defects, tetralogy of Fallot, transposition of the great arteries (TGA), and aortic stenosis. Our data demonstrate that STE-derived atrial ε analysis is feasible for various surgical indications of CHD across different ages.

By using a novel software specifically designed for atrial STE analysis [12], we report the feasibility of 62–85%, which is slightly lower than that presented in similar studies assessing ventricular ε analysis in a similar population (87–93% feasibility) [4,5]. Feasibility in this study was also lower than that observed for atrial ε analysis in a normal cohort (feasibility from 96.8% to 98.9%) [12].

Our study has demonstrated that all atrial ε parameters evaluated were significantly reduced compared to atrial ε in normal subjects [9]. As expected, the lowest atrial ε values were recorded at the first post-operative sample time (e.g., 12–36 h), with progressive recovery thereafter. However, at discharge, STE atrial ε values remained depressed compared to

that observed in normal subjects (*p* < 0.0001). To our knowledge, this is the first study to assess the STE atrial ε response after pediatric cardiac surgery. Few studies have assessed atrial ε response in the medium-term for children undergoing percutaneous versus surgical closure of atrial septal defects [7,8]. Di Salvo and colleagues [7] evaluated color doppler LA and RA atrial ε in 15 subjects (mean age: 9 ± 3 years) 6 months after surgical ASD closure and compared these data with 15 age-matched patients who underwent percutaneous closure, along with 15 age-matched controls. In this particular study, patients who had undergone surgical AS closure had significantly lower LA and RA atrial ε compared to the percutaneous group and to normal subjects [7]. In another study, adults [8] (30 subjects; age 34.4 ± 8.3 years) > 1 year from surgical closure (range 1–5 years) were more likely to have an impaired RA strain and LA conduit strain compared to age-matched controls.

The trend of atrial ε response, we found, is similar to what has been described in the limited studies available for STE ventricular ε response after pediatric cardiac surgery [5,6,17]. In a study of over 117 children (mean age: 2.4 ± 3.9; range: 0–16 years), evaluated at different times pre-operatively and post-surgery, STE ε analysis revealed a significant LV systolic impairment after surgery with amelioration thereafter, but incomplete normalization at discharge [12]. In one retrospective study, De Boer et. al [17] showed in children (*n* = 204; median age: 3.7 ± 5.1 years) undergoing cardiac surgery for different CHD variants that LV and RV global longitudinal strain at the discharge echocardiography (median 7, range 6–14 days post-op) were significantly lower compared to age-matched controls (*n* = 78). Another study in 37 children (median age 19 months, interquartile range 5–63 months) demonstrated that RV peak systolic strain significantly decreased compared to the baseline (–10.5 ± 2.9% vs. –19.5 ± 4.8%; *p* < 0.0001). In this same study, RV peak systolic strain remained depressed compared with pre-operative values (–13.5 ± 4.0% vs. −9.5 ± 4.8%; *p* < 0.0001) [6]. The results from our study support these observations.

The degree of impairment in ε correlated with the duration of the cardio-pulmonary bypass and cross-clamp time. Our results support what has been published on ventricular ε response after pediatric cardiac surgery. In a study with over 33 children (4.2 ± 2.5 years) undergoing bypass cardiac surgery [5], LV longitudinal strain (ε) values were correlated to the aortic cross-clamp duration on post-operative day 0 (*r* = 0.47, *p* = 0.016) and postoperative day 1 (*r* = 0.53, *p* = 0.010) [5].

Of note, atrial ε values correlated with RV and LV ε, and LVEF. While similar correlations between atrial ε and LV strain [18,19] have been demonstrated in adult populations, data on children are lacking. In general, impaired atrial ε is more reflective of diastolic dysfunction [20,21]. The impaired atrial function observed in the present study may be indicative of abnormal ventricular diastolic properties. This is clinically relevant because traditional echocardiographic parameters for ventricular diastolic function assessment are poorly validated in children. Indeed, our data suggest that both the systolic and diastolic function are impaired after pediatric cardiac surgery, and that the dysfunction in the systole is correlated with dysfunction in the diastole.

We present data with a novel atrial specific software, calculated with both the P- and Rgating method. Although the R-wave method of gating is the one currently recommended, the P-gating method may be more appropriate for assessing the atrial function. Significant differences among the two methods have been demonstrated in healthy children where all atrial ε values were lower with P- than R-gating (*p* < 0.001) [9]. Similar differences were noted even in children with CHD after surgery, where atrial strain values calculated with P-gating were constantly lower than those obtained with R-gating. Feasibility and time trends of atrial strain values, however, did not change among the P- and R-gating methods. Thus, under a clinical point of view, both methods may be used; however, measurements obtained with the two different methods cannot be used interchangeably and, during the follow-up, values should be compared with those obtained with the same gating method (and with the same vendor) [13]. Atrial strain analysis with the novel semi-automatic software is very easy and fast, requiring just a few seconds for processing and automated reporting. Thus, considering data acquisition and data analysis, atrial strain analysis may

be routinely introduced in the follow-up of children with CHD undergoing cardiac surgery, without big efforts or a loss of time.

#### *Limitations*

We used STE software developed for the left atrium's deformation only, which has been used for measuring right atrial ε. The use of vendor-specific software represents another limitation. Atrial ε was measured only in the four-chamber view, and not in the two- and three-chamber views [5,6]. Our study assessed longitudinal ε, including components of atrial contraction, reservoir, and conduit function, but did not include indices of atrial electromechanical coupling [1]. Examination times were slightly variable between patients depending on the institutional protocols for single CHD and the clinical indications. However, all 12–36 h examinations corresponded with the time of the first post-operative echocardiographic examination. Around half of the patients, including most older children assessed in our study, were extubated at the time of the examination, while most neonates and infants were intubated.

In addition, patients included in this investigation were heterogeneous and included a wide range of ages and cardiac defects. The relatively limited number of subjects enrolled did not allow for a sufficiently powerful sub-group analysis of patients (e.g., age, corrected vs. palliated, CHD groups), which may have constituted some bias in the final analysis. Nonetheless, despite the heterogeneity of the patient population assessed, significant trends in the ε response after cardiac surgery were observed, across different ages and different CHD variants. Age-related differences for LA and RA strain values were too limited and heterogenous to draw definitive conclusions. Parameter acquisition was incomplete at different sample times, reflecting an additional limitation. A comparison with pre-operative STE data is lacking; however, in uncorrected CHD, when significant shunts are present, the value of the ε data may be limited.

#### **5. Conclusions**

We report the progression of STE-derived atrial ε values after pediatric cardiac surgery as a function of the post-operative time. Our study observed that after pediatric cardiac surgery, all atrial ε parameters were significantly reduced compared to normal subjects. Atrial ε progressively recovered during the post-operative time; however, despite this improvement, atrial values remained significantly depressed compared with normal subjects upon discharge. The degree of atrial ε reduction seems to reflect the duration of the cardio-pulmonary bypass. Furthermore, the atrial and ventricular ε response appears to be correlated with each other. Additionally, the atrial ε response significantly correlated also with the left ventricular ejection fraction. Further studies in a larger cohort are required to validate and reinforce these observations.

**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/jcm11092497/s1, Table S1. Mean differences of examinations at different pre-/post-operative times and in control group. Table S2. Atrial STE *ε* mean values at different post-operative times for the entire cohort and in different age groups. Table S3. Correlations of Atrial STE ε with outcome predictors at Time 1. Table S4. Correlation between Atrial and ventricular STE ε at Time 1 and Time 2.

**Author Contributions:** All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by R.G., P.M., M.S., E.F. and N.A. The first draft of the manuscript was written by M.C. and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

**Institutional Review Board Statement:** This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Meyer Hospital CE62/2016. **Informed Consent Statement:** Informed consent was obtained from all individual participants/parents included in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors have no relevant financial or non-financial interest to disclose. There is no potential conflict of interest, real or perceived, by the author.

#### **References**

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