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Clinical Applications of Novel Tools to Personalize the Follow-Up and Predict the Outcomes in Congenital Heart Disease

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Cardiology".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 11787

Special Issue Editors

Dr. Benedetta Leonardi

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Guest Editor
Department of Pediatric Cardiology, Cardiac Surgery and Heart Lung Transplantation, Bambino Gesu Children’s Hospital and Research Institute, IRCCS, Rome, Italy
Interests: pulmonary valve replacement; tetralogy of Fallot; genetic abnormalities; QRS fragmentation; cardiopulmonary stress test in tetralogy of Fallot; congenital heart disease
Special Issues, Collections and Topics in MDPI journals
Dr. Giorgia Grutter

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Guest Editor
Pediatric Cardiology, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
Interests: arrthymias; congenital heart disease; heart failure; MCS; VAD heart; heart-lung; bilateral lung transplantation in pediatrics; artficial intelligence in cardiovascular disease and role of HLA

Special Issue Information

Dear Colleagues,

In recent years, there has been growing interest in identifying tools that can be used by clinicians to help predict the outcome of patients with congenital heart disease (CHD). In fact, although a clinical assessment is fundamental to understand the patient's state of health, the use the artificial intelligence to standardize, for example, biventricular volumes analysis with cardiovascular magnetic resonance imaging (MRI) can save our clinicians time and decrease contour errors, such as of the right ventricle measurement which remains difficult to standardize (even with MRI). In addition, nowadays, clinicians can go into detail in cardiac shape modeling using machine learning to visualize abnormal mechanisms underlying dysfunction in heart disease, which was not previously possible with other methods. Similarly, the use of 4D flow MRI in bicuspid aortic valve disease allows clinicians to understand the anomalous direction of the blood flow over time highlighting patients at risk of developing a progressive dilatation of the ascending aorta. Furthermore, being able to calculate the aortic wall shear stress provides precise data regarding the distensibility in different aortic regions of interest (ROI), thereby hypothesizing which site is most at risk of aortic dissection. Lately, clinicians have been using strain analysis either with echocardiography or with cardiac MRI to anticipate the worsening of cardiac performance before the common method of estimating ejection fraction to predict adverse events in CHD. Finally, at present, we are also considering the use of biomarker-based risk models (interleukin -8 (IL-8), chemokine ligand 3 (CCL3)) to predict persistent multiple organ dysfunctions after congenital heart surgery.

Thus, the aim of this issue is to encourage researchers to present original works on how these new tools can be used in clinical practice to optimize the management of patients with CHD and reduce the incidence of adverse events.

Dr. Benedetta Leonardi
Dr. Giorgia Grutter
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • artificial intelligence
  • strain analysis
  • biomarkers
  • aortic wall shear stress
  • 4D flow

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Published Papers (7 papers)

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Editorial

Jump to: Research, Review, Other

3 pages, 149 KiB  
Editorial
Clinical Applications of Novel Tools to Personalize Follow-Up and Predict Outcomes in Congenital Heart Disease
by Giorgia Grutter and Benedetta Leonardi
J. Clin. Med. 2025, 14(2), 521; https://doi.org/10.3390/jcm14020521 - 15 Jan 2025
Viewed by 566
Abstract
Recently, the application of novel tools to predict and personalize outcomes for congenital heart disease (CHD) patients has significantly transformed their treatment [...] Full article
(This article belongs to the Special Issue Clinical Applications of Novel Tools to Personalize the Follow-Up and Predict the Outcomes in Congenital Heart Disease)

Research

Jump to: Editorial, Review, Other

22 pages, 4954 KiB  
Article
Machine Learning and Statistical Shape Modelling Methodologies to Assess Vascular Morphology before and after Aortic Valve Replacement
by Yousef Aljassam, Froso Sophocleous, Jan L. Bruse, Vico Schot, Massimo Caputo and Giovanni Biglino
J. Clin. Med. 2024, 13(15), 4577; https://doi.org/10.3390/jcm13154577 - 5 Aug 2024
Viewed by 1663
Abstract
Introduction: Statistical shape modelling (SSM) is used to analyse morphology, discover qualitatively and quantitatively unique shape features within a population, and generate mean shapes and shape modes that show morphological variability. Hierarchical agglomerative clustering is a machine learning analysis used to identify [...] Read more.
Introduction: Statistical shape modelling (SSM) is used to analyse morphology, discover qualitatively and quantitatively unique shape features within a population, and generate mean shapes and shape modes that show morphological variability. Hierarchical agglomerative clustering is a machine learning analysis used to identify subgroups within a given population in relation to shape features. We tested the application of both methods in the clinically relevant scenario of patients undergoing aortic valve repair (AVR). Every year, around 5000 patients undergo surgical AVR in the UK. Aims: Evaluate aortic morphology and identify subgroups amongst patients who had undergone AVR, including Ozaki, Ross, and valve-sparing procedures using SSM and unsupervised hierarchical clustering analysis. This methodological framework can evaluate both pre- and post-surgical variability across subgroups undergoing different surgeries. Methods: Pre- (n = 47) and post- (n = 35) operative three-dimensional (3D) aortic models were reconstructed from computed tomography (CT) and cardiac magnetic resonance (CMR) images. Computational analyses for SSM and hierarchical clustering were run separately for the two subgroups, assessing (a) ascending aorta only and (b) the whole aorta. This allows for exploring possible variations in morphological classification related to the input shape. Results: Most patients in the Ross procedure subgroup exhibited differences in aortic morphology from other subgroups, including an elongated ascending and wide aortic arch pre-operatively, and an elongated ascending aorta with a slightly enlarged sinus post-operatively. In hierarchical clustering, the Ross aortas also appeared to cluster together compared to the other surgical procedures, both pre-operatively and post-operatively. There were significant differences between clusters in terms of clustering distance in the pre-operative analyses (p = 0.003 for ascending aortas, p = 0.016 for whole aortas). There were no significant differences between the clusters in post-operative analyses (p = 0.47 for ascending, p = 0.19 for whole aorta). Conclusions: We demonstrated the feasibility of evaluating aortic morphology before and after different aortic valve surgeries using SSM and hierarchical clustering. This framework could be used to further explore shape features associated with surgical decision-making pre-operatively and, importantly, to identify subgroups whose morphology is associated with poorer clinical outcomes post-operatively. Statistical shape modelling (SSM) and unsupervised hierarchical clustering are two statistical methods that can be used to assess morphology, show morphological variations, with the latter being able to identify subgroups within a population. These methods have been applied to the population of aortic valve replacement (AVR) patients since there are different surgical procedures (traditional AVR, Ozaki, Ross, and valve-sparing). The aim is to evaluate aortic morphology and identify subgroups within this population before and after surgery. Computed tomography and cardiac magnetic resonance images were reconstructed into 3D models of the ascending aorta and whole aorta, which were then input into SSM and hierarchical clustering. The results show that the Ross aortic morphology is quite different from the other aortas. The clustering did not classify the aortas based on the surgical procedures; however, most of the Ross group did cluster together, indicating low variability within this surgical group. Full article
(This article belongs to the Special Issue Clinical Applications of Novel Tools to Personalize the Follow-Up and Predict the Outcomes in Congenital Heart Disease)
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14 pages, 524 KiB  
Article
Patient Reported Outcome Measures in Adults with Fontan Circulatory Failure
by Guillermo Agorrody, Isaac Begun, Subodh Verma, C. David Mazer, Maria Luz Garagiola, Beatriz Fernandez-Campos, Ronald Acuña, Katherine Kearney, Alvan Buckley, Nitish K. Dhingra, Ehsan Ghamarian, S. Lucy Roche, Rafael Alonso-Gonzalez and Rachel M. Wald
J. Clin. Med. 2024, 13(14), 4175; https://doi.org/10.3390/jcm13144175 - 17 Jul 2024
Viewed by 1212
Abstract
Background: Patient reported outcomes (PROs) are important measures in acquired heart disease but have not been well defined in Adult Congenital Heart Disease (ACHD). Our aim was to explore the discriminatory capacity of PRO survey tools in Fontan circulatory failure (FCF). Methods: Consecutive [...] Read more.
Background: Patient reported outcomes (PROs) are important measures in acquired heart disease but have not been well defined in Adult Congenital Heart Disease (ACHD). Our aim was to explore the discriminatory capacity of PRO survey tools in Fontan circulatory failure (FCF). Methods: Consecutive adults were enrolled from our ambulatory clinics. Inclusion criteria were age ≥18 years, a Fontan circulation or a hemodynamically insignificant shunt lesion, and sufficient cognitive/language abilities to complete PROs. A comprehensive package of PRO measures, designed to assess perceived health-related quality of life (HRQOL) was administered (including the Kansas City Cardiomyopathy Questionnaire [KCCQ-12], EuroQol-5-dimension [EQ5D], Short Form Health Status Survey [SF-12], self-reported New York Heart Association [NYHA] Functional Class, and Specific Activity Scale [SAS]). Results: We compared 54 Fontan patients (35 ± 10 years) to 25 simple shunt lesion patients (34 ± 11 years). The KCCQ-12 score was lower in Fontan versus shunt lesion patients (87 [IQR 79, 95] versus 100 [IQR 97, 100], p-value < 0.001). The FCF subgroup was associated with lower KCCQ-12 scores as compared with the non-FCF subgroup (82 [IQR 56, 89] versus 93 [IQR 81, 98], p-value = 0.002). Although the KCCQ-12 had the best discriminatory capacity for determination of FCF of all PRO tools studied (c-statistic 0.75 [CI 0.62, 0.88]), superior FCF discrimination was achieved when the KCCQ-12 was combined with all PRO tools (c-statistic 0.82 [CI 0.71, 0.93]). Conclusions: The KCCQ-12 questionnaire demonstrated good discriminatory capacity for the identification of FCF, which was further improved through the addition of complementary PRO tools. Further research will establish the value of PRO tools to guide management strategies in ACHD. Full article
(This article belongs to the Special Issue Clinical Applications of Novel Tools to Personalize the Follow-Up and Predict the Outcomes in Congenital Heart Disease)
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Graphical abstract

10 pages, 628 KiB  
Article
Predicting Long-Term Childhood Survival of Newborns with Congenital Heart Defects: A Population-Based, Prospective Cohort Study (EPICARD)
by Makan Rahshenas, Nathalie Lelong, Damien Bonnet, Lucile Houyel, Babak Choodari-Oskooei, Mithat Gonen, Francois Goffinet and Babak Khoshnood
J. Clin. Med. 2024, 13(6), 1623; https://doi.org/10.3390/jcm13061623 - 12 Mar 2024
Cited by 1 | Viewed by 1629
Abstract
Backgroud: Congenital heart defects (CHDs) are the most frequent group of major congenital anomalies, accounting for almost 1% of all births. They comprise a very heterogeneous group of birth defects in terms of their severity, clinical management, epidemiology, and embryologic origins. Taking this [...] Read more.
Backgroud: Congenital heart defects (CHDs) are the most frequent group of major congenital anomalies, accounting for almost 1% of all births. They comprise a very heterogeneous group of birth defects in terms of their severity, clinical management, epidemiology, and embryologic origins. Taking this heterogeneity into account is an important imperative to provide reliable prognostic information to patients and their caregivers, as well as to compare results between centers or to assess alternative diagnostic and treatment strategies. The Anatomic and Clinical Classification of CHD (ACC-CHD) aims to facilitate both the CHD coding process and data analysis in clinical and epidemiological studies. The objectives of the study were to (1) Describe the long-term childhood survival of newborns with CHD, and (2) Develop and validate predictive models of infant mortality based on the ACC-CHD. Methods: This study wasbased on data from a population-based, prospective cohort study: Epidemiological Study of Children with Congenital Heart Defects (EPICARD). The final study population comprised 1881 newborns with CHDs after excluding cases that were associated with chromosomal and other anomalies. Statistical analysis included non-parametric survival analysis and flexible parametric survival models. The predictive performance of models was assessed by Harrell’s C index and the Royston–Sauerbrei RD2, with internal validation by bootstrap. Results: The overall 8-year survival rate for newborns with isolated CHDs was 0.96 [0.93–0.95]. There was a substantial difference between the survival rate of the categories of ACC-CHD. The highest and lowest 8-year survival rates were 0.995 [0.989–0.997] and 0.34 [0.21–0.50] for “interatrial communication abnormalities and ventricular septal defects” and “functionally univentricular heart”, respectively. Model discrimination, as measured by Harrell’s C, was 87% and 89% for the model with ACC-CHD alone and the full model, which included other known predictors of infant mortality, respectively. The predictive performance, as measured by RD2, was 45% and 50% for the ACC-CHD alone and the full model. These measures were essentially the same after internal validation by bootstrap. Conclusions: The ACC-CHD classification provided the basis of a highly discriminant survival model with good predictive ability for the 8-year survival of newborns with CHDs. Prediction of individual outcomes remains an important clinical and statistical challenge. Full article
(This article belongs to the Special Issue Clinical Applications of Novel Tools to Personalize the Follow-Up and Predict the Outcomes in Congenital Heart Disease)
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Review

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15 pages, 795 KiB  
Review
How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?
by Alice Pozza, Luca Zanella, Biagio Castaldi and Giovanni Di Salvo
J. Clin. Med. 2024, 13(10), 2996; https://doi.org/10.3390/jcm13102996 - 20 May 2024
Cited by 8 | Viewed by 2541
Abstract
Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the [...] Read more.
Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD. Full article
(This article belongs to the Special Issue Clinical Applications of Novel Tools to Personalize the Follow-Up and Predict the Outcomes in Congenital Heart Disease)
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18 pages, 1814 KiB  
Review
Repaired Tetralogy of Fallot: Have We Understood the Right Timing of PVR?
by Benedetta Leonardi, Marco Perrone, Giuseppe Calcaterra, Jolanda Sabatino, Isabella Leo, Martina Aversani, Pier Paolo Bassareo, Alice Pozza, Lilia Oreto, Sara Moscatelli, Nunzia Borrelli, Francesco Bianco and Giovanni Di Salvo
J. Clin. Med. 2024, 13(9), 2682; https://doi.org/10.3390/jcm13092682 - 2 May 2024
Cited by 5 | Viewed by 2305
Abstract
Despite many advances in surgical repair during the past few decades, the majority of tetralogy of Fallot patients continue to experience residual hemodynamic and electrophysiological abnormalities. The actual issue, which has yet to be solved, is understanding how this disease evolves in each [...] Read more.
Despite many advances in surgical repair during the past few decades, the majority of tetralogy of Fallot patients continue to experience residual hemodynamic and electrophysiological abnormalities. The actual issue, which has yet to be solved, is understanding how this disease evolves in each individual patient and, as a result, who is truly at risk of sudden death, as well as the proper timing of pulmonary valve replacement (PVR). Our responsibility should be to select the most appropriate time for each patient, going above and beyond imaging criteria used up to now to make such a clinically crucial decision. Despite several studies on timing, indications, procedures, and outcomes of PVR, there is still much uncertainty about whether PVR reduces arrhythmia burden or improves survival in these patients and how to appropriately manage this population. This review summarizes the most recent research on the evolution of repaired tetralogy of Fallot (from adolescence onwards) and risk factor variables that may favor or delay PVR. Full article
(This article belongs to the Special Issue Clinical Applications of Novel Tools to Personalize the Follow-Up and Predict the Outcomes in Congenital Heart Disease)
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Other

14 pages, 3093 KiB  
Systematic Review
Right-Sided Aortic Arch: A Computed Tomography Angiography Investigation, A Systematic Review with Meta-Analysis
by George Triantafyllou, Savvas Melissanidis, Marianna Vlychou, George Tsakotos, Nikos Pantazis, Katerina Vassiou, Christos Tsiouris and Maria Piagkou
J. Clin. Med. 2024, 13(11), 3105; https://doi.org/10.3390/jcm13113105 - 25 May 2024
Cited by 1 | Viewed by 1282
Abstract
Background/Objectives: The right-sided aortic arch (RAA) is an uncommon variation of the aortic arch (AA), characterized by the aorta crossing over the right main bronchus. In the RAA, the descending aorta can be found on either the right or left side of [...] Read more.
Background/Objectives: The right-sided aortic arch (RAA) is an uncommon variation of the aortic arch (AA), characterized by the aorta crossing over the right main bronchus. In the RAA, the descending aorta can be found on either the right or left side of the spine. The current study comprises a comprehensive retrospective computed tomography angiography (CTA) investigation into the prevalence of the RAA within the Greek population. Additionally, we will conduct a systematic review and meta-analysis to elucidate both common and rare morphological variants of the RAA. This research is significant as it sheds light on the prevalence and characteristics of the RAA in a specific population, providing valuable insights for clinical practice. Methods: Two hundred CTAs were meticulously investigated for the presence of a RAA. In addition, the PubMed, Google Scholar, and Scopus online databases were thoroughly searched for studies referring to the AA morphology. The R programming language and RStudio were used for the pooled prevalence meta-analysis, while several subgroup analyses were conducted. Results: Original study: A unique case of 200 CTAs (0.5%) was identified with an uncommon morphology. The following branches emanated from the RAA under the sequence: the right subclavian artery (RSA), the right common carotid artery (RCCA), the left common carotid artery (LCCA), and the left vertebral artery (LVA) in common origin with the aberrant left subclavian artery (ALSA). The ALSA originated from a diverticulum (of Kommerell) and followed a retroesophageal course. Systematic Review and Meta-Analysis: Sixty-two studies (72,187 total cases) met the inclusion criteria. The pooled prevalence of the RAA with a mirror-image morphology was estimated at 0.07%, and the RAA with an ALSA was estimated at <0.01%. Conclusions: AA anomalies, specifically the RAA, raise clinical interest due to their coexistence with developmental heart anomalies and possible interventional complications. Congenital heart anomalies, such as the Tetralogy of Fallot and patent foramen ovale, coexisted with RAA mirror-image morphology. Full article
(This article belongs to the Special Issue Clinical Applications of Novel Tools to Personalize the Follow-Up and Predict the Outcomes in Congenital Heart Disease)
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