Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis

BACKGROUND

Implantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death after myocardial infarction. However, improved risk stratification for device requirement is still needed.

OBJECTIVE

The purpose of this study was to improve assessment of postinfarct ventricular electropathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modeling.

METHODS

ADAS 3D LV (ADAS LV Medical, Barcelona, Spain) and custom-made software were used to generate 3-dimensional patient-specific ventricular models in a prospective cohort of patients with a myocardial infarction (N = 40) having undergone LGE imaging before ICD implantation. Corridor metrics and 3-dimensional surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced ventricular tachycardias and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up.

RESULTS

Patients with an event (n = 17) had a larger interface between healthy myocardium and scar and higher VITA metrics. Cox regression analysis demonstrated a significant independent association with an event: interface (hazard ratio [HR] 2.79; 95% confidence interval [CI] 1.44-5.44; P < .01), unique ventricular tachycardias (HR 1.67; 95% CI 1.04-2.68; P = .03), mean RTT (HR 2.14; 95% CI 1.11-4.12; P = .02), and maximum RTT (HR 2.13; 95% CI 1.19-3.81; P = .01).

CONCLUSION

A detailed quantitative analysis of LGE-based scar maps, combined with advanced computational modeling, can accurately predict ICD therapy and could facilitate the early identification of high-risk patients in addition to left ventricular ejection fraction.

Strain-controlled electrophysiological wave propagation alters in silico scar-based substrate for ventricular tachycardia

Introduction: Assessing a patient's risk of scar-based ventricular tachycardia (VT) after myocardial infarction is a challenging task. It can take months to years after infarction for VT to occur. Also, if selected for ablation therapy, success rates are low. Methods: Computational ventricular models have been presented previously to support VT risk assessment and to provide ablation guidance. In this study, an extension to such virtual-heart models is proposed to phenomenologically incorporate tissue remodeling driven by mechanical load. Strain amplitudes in the heart muscle are obtained from simulations of mechanics and are used to adjust the electrical conductivity. Results: The mechanics-driven adaptation of electrophysiology resulted in a more heterogeneous distribution of propagation velocities than that of standard models, which adapt electrophysiology in the structural substrate from medical images only. Moreover, conduction slowing was not only present in such a structural substrate, but extended in the adjacent functional border zone with impaired mechanics. This enlarged the volumes with high repolarization time gradients (≥10 ms/mm). However, maximum gradient values were not significantly affected. The enlarged volumes were localized along the structural substrate border, which lengthened the line of conduction block. The prolonged reentry pathways together with conduction slowing in functional regions increased VT cycle time, such that VT was easier to induce, and the number of recommended ablation sites increased from 3 to 5 locations. Discussion: Sensitivity testing showed an accurate model of strain-dependency to be critical for low ranges of conductivity. The model extension with mechanics-driven tissue remodeling is a potential approach to capture the evolution of the functional substrate and may offer insight into the progression of VT risk over time.

A transferable in-silico augmented ischemic model for virtual myocardial perfusion imaging and myocardial infarction detection

This paper proposes an innovative approach to generate a generalized myocardial ischemia database by modeling the virtual electrophysiology of the heart and the 12-lead electrocardiography projected by the in-silico model can serve as a ready-to-use database for automatic myocardial infarction/ischemia (MI) localization and classification. Although the virtual heart can be created by an established technique combining the cell model with personalized heart geometry to observe the spatial propagation of depolarization and repolarization waves, we developed a strategy based on the clinical pathophysiology of MI to generate a heterogeneous database with a generic heart while maintaining clinical relevance and reduced computational complexity. First, the virtual heart is simplified into 11 regions that match the types and locations, which can be diagnosed by 12-lead ECG; the major arteries were divided into 3-5 segments from the upstream to the downstream based on the general anatomy. Second, the stenosis or infarction of the major or minor coronary artery branches can cause different perfusion drops and infarct sizes. We simulated the ischemic sites in different branches of the arteries by meandering the infarction location to elaborate on possible ECG representations, which alters the infraction's size and changes the transmembrane potential (TMP) of the myocytes associated with different levels of perfusion drop. A total of 8190 different case combinations of cardiac potentials with ischemia and MI were simulated, and the corresponding ECGs were generated by forward calculations. Finally, we trained and validated our in-silico database with a sparse representation classification (SRC) and tested the transferability of the model on the real-world Physikalisch Technische Bundesanstalt (PTB) database. The overall accuracies for localizing the MI region on the PTB data achieved 0.86, which is only 2% drop compared to that derived from the simulated database (0.88). In summary, we have shown a proof-of-concept for transferring an in-silico model to real-world database to compensate for insufficient data.

Digital twins in medicine

Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

Up digital and personal: How heart digital twins can transform heart patient care

Precision medicine is the vision of health care where therapy is tailored to each patient. As part of this vision, digital twinning technology promises to deliver a digital representation of organs or even patients by using tools capable of simulating personal health conditions and predicting patient or disease trajectories on the basis of relationships learned both from data and from biophysics knowledge. Such virtual replicas would update themselves with data from monitoring devices and medical tests and assessments, reflecting dynamically the changes in our health conditions and the responses to treatment. In precision cardiology, the concepts and initial applications of heart digital twins have slowly been gaining popularity and the trust of the clinical community. In this article, we review the advancement in heart digital twinning and its initial translation to the management of heart rhythm disorders.

Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review

Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.

Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Current Applications of Artificial Intelligence in Sarcoidosis

PURPOSE

Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis.

METHODS

Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded.

CONCLUSIONS

Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.

Twenty-five years of research in cardiac imaging in electrophysiology procedures for atrial and ventricular arrhythmias

Catheter ablation is nowadays considered the treatment of choice for numerous cardiac arrhythmias in different clinical scenarios. Fluoroscopy has traditionally been the primary imaging modality for catheter ablation, providing real-time visualization of catheter navigation. However, its limitations, such as inadequate soft tissue visualization and exposure to ionizing radiation, have prompted the integration of alternative imaging modalities. Over the years, advancements in imaging techniques have played a pivotal role in enhancing the safety, efficacy, and efficiency of catheter ablation procedures. This manuscript aims to explore the utility of imaging, including electroanatomical mapping, cardiac computed tomography, echocardiography, cardiac magnetic resonance, and nuclear cardiology exams, in helping electrophysiology procedures. These techniques enable accurate anatomical guidance, identification of critical structures and substrates, and real-time monitoring of complications, ultimately enhancing procedural safety and success rates. Incorporating advanced imaging technologies into routine clinical practice has the potential to further improve clinical outcomes of catheter ablation procedures and pave the way for more personalized and precise ablation therapies in the future.

Risk of Arrhythmic Death in Patients With Nonischemic Cardiomyopathy: JACC Review Topic of the Week

Nonischemic cardiomyopathy (NICM) is common and patients are at significant risk for early mortality secondary to ventricular arrhythmias. Current guidelines recommend implantable cardioverter-defibrillator (ICD) therapy to decrease sudden cardiac death (SCD) in patients with heart failure and reduced left ventricular ejection fraction. However, in randomized clinical trials comprised solely of patients with NICM, primary prevention ICDs did not confer significant mortality benefit. Moreover, left ventricular ejection fraction has limited sensitivity and specificity for predicting SCD. Therefore, precise risk stratification algorithms are needed to define those at the highest risk of SCD. This review examines mechanisms of sudden arrhythmic death in patients with NICM, discusses the role of ICD therapy and treatment of heart failure for prevention of SCD in patients with NICM, examines the role of cardiac magnetic resonance imaging and computational modeling for SCD risk stratification, and proposes new strategies to guide future clinical trials on SCD risk assessment in patients with NICM.

Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling

AIMS

Substrate assessment of scar-mediated ventricular tachycardia (VT) is frequently performed using late gadolinium enhancement (LGE) images. Although this provides structural information about critical pathways through the scar, assessing the vulnerability of these pathways for sustaining VT is not possible with imaging alone.This study evaluated the performance of a novel automated re-entrant pathway finding algorithm to non-invasively predict VT circuit and inducibility.

METHODS

Twenty post-infarct VT-ablation patients were included for retrospective analysis. Commercially available software (ADAS3D left ventricular) was used to generate scar maps from 2D-LGE images using the default 40-60 pixel-signal-intensity (PSI) threshold. In addition, algorithm sensitivity for altered thresholds was explored using PSI 45-55, 35-65, and 30-70. Simulations were performed on the Virtual Induction and Treatment of Arrhythmias (VITA) framework to identify potential sites of block and assess their vulnerability depending on the automatically computed round-trip-time (RTT). Metrics, indicative of substrate complexity, were correlated with VT-recurrence during follow-up.

RESULTS

Total VTs (85 ± 43 vs. 42 ± 27) and unique VTs (9 ± 4 vs. 5 ± 4) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with area under the curve of 0.820 and 0.770, respectively. VITA was robust to scar threshold variations with no significant impact on total and unique VTs, and mean RTT between the four models. Simulation metrics derived from PSI 45-55 model had the highest number of parameters predictive for post-ablation VT-recurrence.

CONCLUSION

Advanced computational metrics can non-invasively and robustly assess VT substrate complexity, which may aid personalized clinical planning and decision-making in the treatment of post-infarction VT.

Predicting Ventricular Tachycardia Circuits in Patients with Arrhythmogenic Right Ventricular Cardiomyopathy using Genotype-specific Heart Digital Twins

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Multi-modality cardiac image computing: A survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.

Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Introduction: The 30-50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance D_PS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance D_PS was shorter in the responders. The max ML-score and D_PS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and D_PS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.

Primer on Machine Learning in Electrophysiology

Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.

FDG PET/CT Imaging of Sarcoidosis

Sarcoidosis is a multisystemic granulomatous disease of unknown etiology. The diagnostic can be made by histological identification of non-caseous granuloma or by a combination of clinical criteria. Active inflammatory granuloma can lead to fibrotic damage. Although 50% of cases resolve spontaneously, systemic treatments are often necessary to decrease symptoms and avoid permanent organ dysfunction, notably in cardiac sarcoidosis. The course of the disease can be punctuated by exacerbations and relapses and the prognostic depends mainly on affected sites and patient management. FDG-PET/CT along with newer FDG-PET/MR have emerged as key imaging modalities in sarcoidosis, namely for certain diagnostic purposes, staging and biopsy guiding. By identifying with a high sensitivity inflammatory active granuloma, FDG hybrid imaging is a main prognostic tool and therapeutic ally in sarcoidosis. This review aims to highlight the actual critical roles of hybrid PET imaging in sarcoidosis and display a brief perspective for the future which appears to include other radiotracers and artificial intelligence applications.

Hydrogel Encapsulated Core-Shell Photonic Barcodes for Multiplex Biomarker Quantification

Acute myocardial infarction (AMI) is one of the most fatal diseases in the world in recent decades. Because rapid and accurate determination of AMI has the potential to save millions of lives globally, the development of new diagnostic method is of great significance. Here, we designed a magnetic responsive structural color core-shell hydrogel microcarrier as a novel platform for a high-throughput detection of a variety of cardiovascular biomarkers. The composite hydrogel shell was formed from methacrylated gelatin, acrylic acid, and poly(ethylene glycol diacrylate), and the core silica photonic crystals acted as a detector. Fe₃O₄ nanoparticles were infused into the void of the core-shell structure to impart magnetic response properties to the encoded carrier. The findings indicated that our method possessed high sensitivity and reliable specificity in the high-throughput detection of AMI-related biomarkers Myo, cTnI, and BNP. In addition, the developed method not only showed good specificity and high sensitivity in clinical samples but also was comparable to the clinical gold standard method. Therefore, the magnetic response structural color core-shell hydrogel carriers were regarded as a potential approach to detect some AMI disease-related biomarkers.

Emerging role of artificial intelligence in cardiac electrophysiology

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

Cardiovascular magnetic resonance in autoimmune rheumatic diseases: a clinical consensus document by the European Association of Cardiovascular Imaging

Autoimmune rheumatic diseases (ARDs) involve multiple organs including the heart and vasculature. Despite novel treatments, patients with ARDs still experience a reduced life expectancy, partly caused by the higher prevalence of cardiovascular disease (CVD). This includes CV inflammation, rhythm disturbances, perfusion abnormalities (ischaemia/infarction), dysregulation of vasoreactivity, myocardial fibrosis, coagulation abnormalities, pulmonary hypertension, valvular disease, and side-effects of immunomodulatory therapy. Currently, the evaluation of CV involvement in patients with ARDs is based on the assessment of cardiac symptoms, coupled with electrocardiography, blood testing, and echocardiography. However, CVD may not become overt until late in the course of the disease, thus potentially limiting the therapeutic window for intervention. More recently, cardiovascular magnetic resonance (CMR) has allowed for the early identification of pathophysiologic structural/functional alterations that take place before the onset of clinically overt CVD. CMR allows for detailed evaluation of biventricular function together with tissue characterization of vessels/myocardium in the same examination, yielding a reliable assessment of disease activity that might not be mirrored by blood biomarkers and other imaging modalities. Therefore, CMR provides diagnostic information that enables timely clinical decision-making and facilitates the tailoring of treatment to individual patients. Here we review the role of CMR in the early and accurate diagnosis of CVD in patients with ARDs compared with other non-invasive imaging modalities. Furthermore, we present a consensus-based decision algorithm for when a CMR study could be considered in patients with ARDs, together with a standardized study protocol. Lastly, we discuss the clinical implications of findings from a CMR examination.

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.

Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning

Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual differences and are affected by factors such as aging and disease and its progression. Histological analysis, modern in situ imaging, and biomechanical testing have deepened our understanding of these complex interrelations, yet two key questions remain: (1) Given the specific microstructure, can one predict the macroscopic mechanical properties without mechanical testing? (2) Can one quantify individual contributions of the different microstructural features to the macroscopic mechanical properties in an automated, systematic and largely unbiased way? Here we propose a bidirectional deep learning architecture to address these two questions. Our architecture uses data from standard histological analyses, two-photon microscopy and biaxial biomechanical testing. Its capabilities are demonstrated by predicting with high accuracy (R²=0.92) the evolving mechanical properties of the murine aorta during maturation and aging. Moreover, our architecture reveals that the extracellular matrix composition and organization are the most prominent factors governing the macroscopic mechanical properties of the tissues studied herein. STATEMENT OF SIGNIFICANCE:  .

Prevalence of Sarcoidosis-Associated Pulmonary Hypertension: A Systematic Review and Meta-Analysis

Background

Sarcoidosis-associated pulmonary hypertension (SAPH) is associated with poor prognosis, conferring up to a 10-fold increase in mortality in patients with sarcoidosis, but the actual prevalence of SAPH is unknown.

Methods

The PubMed, Embase, and Cochrane Library databases were systematically searched for epidemiological studies reporting the prevalence of SAPH up to July 2021. Two reviewers independently performed the study selection, data extraction, and quality assessment. Studies were pooled using random-effects meta-analysis.

Results

This meta-analysis included 25 high-quality studies from 12 countries, with a pooled sample of 632,368 patients with sarcoidosis. The prevalence of SAPH by transthoracic echocardiography in Europe, the United States and Asia was 18.8% [95% confidence interval (CI): 11.1–26.5%], 13.9% (95% CI: 5.4–22.4%) and 16.2% (95% CI: 7.1–25.4%) separately, and the overall pooled prevalence was 16.4% (95%CI: 12.2–20.5%). By right heart catheterization (RHC), the pooled prevalence of SAPH was 6.4% (95% CI: 3.6–9.1%) in general sarcoidosis population, and subgroup analyses showed that the prevalence of SAPH was 6.7% (95% CI: 2.4–11.0%) in Europe and 8.6% (95% CI: −4.1 to 21.3%) in the United States. Further, the prevalence of pre-capillary PH was 6.5% (95% CI: 2.9–10.2%). For the population with advanced sarcoidosis, the pooled prevalence of SAPH and pre-capillary PH by RHC was as high as 62.3% (95% CI: 46.9–77.6%) and 55.9% (95% CI: 20.1–91.7%), respectively. Finally, the pooled prevalence of SAPH in large databases with documented diagnoses (6.1%, 95% CI: 2.6–9.5%) was similar to that of RHC. Substantial heterogeneity across studies was observed for all analyses (I² > 80%, P < 0.001).

Conclusions

The sarcoidosis population has a relatively low burden of PH, mainly pre-capillary PH. However, as the disease progresses to advanced sarcoidosis, the prevalence of SAPH increases significantly.

Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs) arising from the proliferation of fibrosis in the heart. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic resonance imaging and T1 mapping data to construct digital replicas of HCM patient hearts that represent the patient-specific distribution of focal and diffuse fibrosis and evaluate the substrate propensity to VA. Our analysis indicates that the presence of diffuse fibrosis, which is rarely assessed in these patients, increases arrhythmogenic propensity. In forecasting future VA events in HCM patients, the imaging-based computational heart approach achieved 84.6%, 76.9%, and 80.1% sensitivity, specificity, and accuracy, respectively, and significantly outperformed current clinical risk predictors. This novel VA risk assessment may have the potential to prevent SCD and help deploy primary prevention appropriately in HCM patients.

Arrhythmia in hypertrophic cardiomyopathy: Risk prediction using contrast enhanced MRI, T1 mapping, and personalized virtual heart technology

BACKGROUND

Hypertrophic cardiomyopathy (HCM), a disease with myocardial fibrosis manifestation, is a common cause of sudden cardiac death (SCD) due to ventricular arrhythmias (VA). Current clinical risk stratification criteria are inadequate in identifying patients who are at risk for VA and in need of an implantable cardioverter defibrillator (ICD) for primary prevention.

OBJECTIVE

We aimed to develop a risk prediction approach based on imaging biomarkers from the combination of late gadolinium contrast-enhanced (LGE) MRI and T1 mapping. We then aimed to compare the prediction to a virtual heart computational risk assessment approach based on LGE-T1 virtual heart models.

METHODS

The methodology involved combining short-axis LGE-MRI with post-contrast T1 maps to define personalized thresholds for diffuse and dense fibrosis. The combined LGE-T1 maps were used to evaluate imaging biomarkers for VA risk prediction. The risk prediction capability of the biomarkers was compared with that of the LGE-T1 virtual heart arrhythmia inducibility simulation. VA risk prediction performance from both approaches was compared to clinical outcome (presence of clinical VA).

RESULTS

Image-based biomarkers, including hypertrophy, signal intensity heterogeneity, and fibrotic border complexity, could not discriminate high vs low VA risk. LGE-T1 virtual heart technology outperformed all the image-based biomarker metrics and was statistically significant in predicting VA risk in HCM.

CONCLUSIONS

We combined two MR imaging techniques to analyze imaging biomarkers in HCM. Raw and processed image-based biomarkers cannot discriminate patients with VA from those without VA. Hybrid LGE-T1 virtual heart models could correctly predict VA risk for this cohort and may improve SCD risk stratification to better identify HCM patients for primary preventative ICD implantation.

Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data

Background: Up to 30–50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge.

Objective: The main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology.

Materials and Methods: Retrospective data from 57 patients who underwent CRT device implantation was utilized. Positive response to CRT was defined by a 10% increase in the left ventricular ejection fraction in a year after implantation. For each patient, an anatomical model of the heart and torso was reconstructed from MRI and CT images and tailored to ECG recorded in the participant. The models were used to compute ventricular activation time, ECG duration and electrical dyssynchrony indices during intrinsic rhythm and BiV pacing from the sites of implanted leads. For building a predictive model of CRT response, we used clinical data recorded before CRT device implantation together with model-derived biomarkers of ventricular excitation in the left bundle branch block mode of activation and under BiV stimulation. Several Machine Learning (ML) classifiers and feature selection algorithms were tested on the hybrid dataset, and the quality of predictors was assessed using the area under receiver operating curve (ROC AUC). The classifiers on the hybrid data were compared with ML models built on clinical data only.

Results: The best ML classifier utilizing a hybrid set of clinical and model-driven data demonstrated ROC AUC of 0.82, an accuracy of 0.82, sensitivity of 0.85, and specificity of 0.78, improving quality over that of ML predictors built on clinical data from much larger datasets by more than 0.1. Distance from the LV pacing site to the post-infarction zone and ventricular activation characteristics under BiV pacing were shown as the most relevant model-driven features for CRT response classification.

Conclusion: Our results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes.

Multiscale simulations of left ventricular growth and remodeling

Cardiomyocytes can adapt their size, shape, and orientation in response to altered biomechanical or biochemical stimuli. The process by which the heart undergoes structural changes-affecting both geometry and material properties-in response to altered ventricular loading, altered hormonal levels, or mutant sarcomeric proteins is broadly known as cardiac growth and remodeling (G&R). Although it is likely that cardiac G&R initially occurs as an adaptive response of the heart to the underlying stimuli, prolonged pathological changes can lead to increased risk of atrial fibrillation, heart failure, and sudden death. During the past few decades, computational models have been extensively used to investigate the mechanisms of cardiac G&R, as a complement to experimental measurements. These models have provided an opportunity to quantitatively study the relationships between the underlying stimuli (primarily mechanical) and the adverse outcomes of cardiac G&R, i.e., alterations in ventricular size and function. State-of-the-art computational models have shown promise in predicting the progression of cardiac G&R. However, there are still limitations that need to be addressed in future works to advance the field. In this review, we first outline the current state of computational models of cardiac growth and myofiber remodeling. Then, we discuss the potential limitations of current models of cardiac G&R that need to be addressed before they can be utilized in clinical care. Finally, we briefly discuss the next feasible steps and future directions that could advance the field of cardiac G&R.