Image-based left ventricular shape analysis for sudden cardiac death risk stratification

Cohort-based nomogram for forensic prediction of SCD: a single-center pilot study

Forensic diagnosis of sudden cardiac death (SCD) is an extremely important part of routine forensic practice. The present study aimed to develop and validate nomograms for predicting the probability of SCD with special regards to ischemic heart disease-induced SCD (IHD-induced SCD) based on multiple autopsy variables. A total of 3322 cases, were enrolled and randomly assigned into a training cohort (n = 2325) and a validation cohort (n = 997), respectively. Prediction models of SCD and IHD-induced SCD were developed through multivariable logistic regression based on variables selected by LASSO regression or ridge regression, and prediction model with higher area under the curve (AUC) of the receiver operating characteristic (ROC) curve in the validation cohort was used to establish nomograms. For SCD prediction, discrimination of the nomogram was determined based on the ROC with AUC of 0.751 (95% CI, 0.726-0.775) and 0.735 (95% CI, 0.696-0.774) in the training cohort and validation cohort respectively. The AUC of IHD-induced SCD prediction nomogram in the training cohort and validation cohort were 0.742 (95% CI, 0.716-0.768) and 0.738 (95% CI, 0.698-0.777). To facilitate the use of nomograms in routine casework in forensic practice, web calculators ( https://forensic.shinyapps.io/Forensic_SCD/ , https://forensic.shinyapps.io/Forensic_IHDinducedSCD/ ) were constructed. In conclusion, the present study developed and validated simple and practical nomograms for predicting the probability of SCD and IHD-induced SCD based on multiple autopsy variables. The nomograms have certain efficiency for discrimination and calibration to provide a novel approach to diagnose cause of death, and may become a valuable tool in forensic practice.

Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events

Background

Machine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD).

Objective

To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD.

Methods

Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous ('peri-infarct') and homogeneous ('core') fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling.

Results

Of 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81-0.82) vs. 0.64 (0.63-0.65), p = 0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF<35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1.88, 95% confidence interval (CI) 1.38-2.56, p < 0.001; number of PIZ components HR 1.34, 95% CI 1.08-1.67, p = 0.009; core interface area HR 1.6, 95% CI 1.29-1.99, p = <0.001.

Conclusion

Machine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantable cardioverter defibrillators in stable CAD.

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.

Left ventricular shape predicts arrhythmic risk in fibrotic dilated cardiomyopathy

AIMS

Remodelling of the left ventricular (LV) shape is one of the hallmarks of non-ischaemic dilated cardiomyopathy (DCM) and may contribute to ventricular arrhythmias and sudden cardiac death. We sought to investigate a novel three dimensional (3D) shape analysis approach to quantify LV remodelling for arrhythmia prediction in DCM.

METHODS AND RESULTS

We created 3D LV shape models from end-diastolic cardiac magnetic resonance images of 156 patients with DCM and late gadolinium enhancement (LGE). Using the shape models, principle component analysis, and Cox-Lasso regression, we derived a prognostic LV arrhythmic shape (LVAS) score which identified patients who reached a composite arrhythmic endpoint of sudden cardiac death, aborted sudden cardiac death, and sustained ventricular tachycardia. We also extracted geometrical metrics to look for potential prognostic markers. During a follow-up period of up to 16 years (median 7.7, interquartile range: 3.9), 25 patients met the arrhythmic endpoint. The optimally prognostic LV shape for predicting the time-to arrhythmic event was a paraboloidal longitudinal profile, with a relatively wide base. The corresponding LVAS was associated with arrhythmic events in univariate Cox regression (hazard ratio = 2.0 per quartile; 95% confidence interval: 1.3-2.9), in univariate Cox regression with propensity score adjustment, and in three multivariate models; with LV ejection fraction, New York Heart Association Class III/IV (Model 1), implantable cardioverter-defibrillator receipt (Model 2), and cardiac resynchronization therapy (Model 3).

CONCLUSION

Biomarkers of LV shape remodelling in DCM can help to identify the patients at greatest risk of lethal ventricular arrhythmias.

Assessment of arrhythmia mechanism and burden of the infarcted ventricles following remuscularization with pluripotent stem cell-derived cardiomyocyte patches using patient-derived models

AIMS

Direct remuscularization with pluripotent stem cell-derived cardiomyocytes (PSC-CMs) seeks to address the onset of heart failure post-myocardial infarction (MI) by treating the persistent muscle deficiency that underlies it. However, direct remuscularization with PSC-CMs could potentially be arrhythmogenic. We investigated two possible mechanisms of arrhythmogenesis-focal vs. re-entrant-arising from direct remuscularization with PSC-CM patches in two personalized, human ventricular computer models of post-MI. Moreover, we developed a principled approach for evaluating arrhythmogenicity of direct remuscularization that factors in the VT propensity of the patient-specific post-MI fibrotic substrate and use it to investigate different conditions of patch remuscularization.

METHODS AND RESULTS

Two personalized, human ventricular models of post-MI (P1 and P2) were constructed from late gadolinium enhanced (LGE)-magnetic resonance images (MRIs). In each model, remuscularization with PSC-CM patches was simulated under different treatment conditions that included patch engraftment, patch myofibril orientation, remuscularization site, patch size (thickness and diameter), and patch maturation. To determine arrhythmogenicity of treatment conditions, VT burden of heart models was quantified prior to and after simulated remuscularization and compared. VT burden was quantified based on inducibility (i.e. weighted sum of pacing sites that induced) and severity (i.e. the number of distinct VT morphologies induced). Prior to remuscularization, VT burden was significant in P1 (0.275) and not in P2 (0.0, not VT inducible). We highlight that re-entrant VT mechanisms would dominate over focal mechanisms; spontaneous beats emerging from PSC-CM grafts were always a fraction of resting sinus rate. Moreover, incomplete patch engraftment can be particularly arrhythmogenic, giving rise to particularly aberrant electrical activation and conduction slowing across the PSC-CM patches along with elevated VT burden when compared with complete engraftment. Under conditions of complete patch engraftment, remuscularization was almost always arrhythmogenic in P2 but certain treatment conditions could be anti-arrhythmogenic in P1. Moreover, the remuscularization site was the most important factor affecting VT burden in both P1 and P2. Complete maturation of PSC-CM patches, both ionically and electrotonically, at the appropriate site could completely alleviate VT burden.

CONCLUSION

We identified that re-entrant VT would be the primary VT mechanism in patch remuscularization. To evaluate the arrhythmogenicity of remuscularization, we developed a principled approach that factors in the propensity of the patient-specific fibrotic substrate for VT. We showed that arrhythmogenicity is sensitive to the patient-specific fibrotic substrate and remuscularization site. We demonstrate that targeted remuscularization can be safe in the appropriate individual and holds the potential to non-destructively eliminate VT post-MI in addition to addressing muscle deficiency underlying heart failure progression.

Beyond Ejection Fraction: Novel Clinical Approaches Towards Sudden Cardiac Death Risk Stratification in Patients with Dilated Cardiomyopathy

Implantable Cardioverter-Defibrillator (ICD) therapy is indicated for patients at risk for sudden cardiac death due to ventricular tachyarrhythmia. The most commonly used risk stratification algorithms use Left Ventricular Ejection Fraction (LVEF) to determine which patients qualify for ICD therapy, even though LVEF is a better marker of total mortality than ventricular tachyarrhythmias mortality. This review evaluates imaging tools and novel biomarkers proposed for better risk stratifying arrhythmic substrate, thereby identifying optimal ICD therapy candidates.

Prognostic value of cardiovascular magnetic resonance left ventricular volumetry and geometry in patients receiving an implantable cardioverter defibrillator

BACKGROUND

Current indications for implantable cardioverter defibrillator (ICD) implantation for sudden cardiac death prevention rely primarily on left ventricular (LV) ejection fraction (LVEF). Currently, two different contouring methods by cardiovascular magnetic resonance (CMR) are used for LVEF calculation. We evaluated the comparative prognostic value of these two methods in the ICD population, and if measures of LV geometry added predictive value.

METHODS

In this retrospective, 2-center observational cohort study, patients underwent CMR prior to ICD implantation for primary or secondary prevention from January 2005 to December 2018. Two readers, blinded to all clinical and outcome data assessed CMR studies by: (a) including the LV trabeculae and papillary muscles (TPM) (trabeculated endocardial contours), and (b) excluding LV TPM (rounded endocardial contours) from the total LV mass for calculation of LVEF, LV volumes and mass. LV sphericity and sphere-volume indices were also calculated. The primary outcome was a composite of appropriate ICD shocks or death.

RESULTS

Of the 372 consecutive eligible patients, 129 patients (34.7%) had appropriate ICD shock, and 65 (17.5%) died over a median duration follow-up of 61 months (IQR 38-103). LVEF was higher when including TPM versus excluding TPM (36% vs. 31%, p < 0.001). The rate of appropriate ICD shock or all-cause death was higher among patients with lower LVEF both including and excluding TPM (p for trend = 0.019 and 0.004, respectively). In multivariable models adjusting for age, primary prevention, ischemic heart disease and late gadolinium enhancement, both LVEF (HR per 10% including TPM 0.814 [95%CI 0.688-0.962] p = 0.016, vs. HR per 10% excluding TPM 0.780 [95%CI 0.639-0.951] p = 0.014) and LV mass index (HR per 10 g/m2 including TPM 1.099 [95%CI 1.027-1.175] p = 0.006; HR per 10 g/m2 excluding TPM 1.126 [95%CI 1.032-1.228] p = 0.008) had independent prognostic value. Higher LV end-systolic volumes and LV sphericity were significantly associated with increased mortality but showed no added prognostic value.

CONCLUSION

Both CMR post-processing methods showed similar prognostic value and can be used for LVEF assessment. LVEF and indexed LV mass are independent predictors for appropriate ICD shocks and all-cause mortality in the ICD population.

Increased oxygenation is associated with myocardial inflammation and adverse regional remodeling after acute ST-segment elevation myocardial infarction

OBJECTIVES

To explore the relationships between oxygenation signal intensity (SI) with myocardial inflammation and regional left ventricular (LV) remodeling in reperfused acute ST-segment elevation myocardial infarction (STEMI) using oxygenation-sensitive cardiovascular magnetic resonance (OS-CMR).

METHODS

Thirty-three STEMI patients and 22 age- and sex-matched healthy volunteers underwent CMR. The protocol included cine function, OS imaging, precontrast T1 mapping, T2 mapping, and late gadolinium enhancement (LGE) imaging. A total of 880 LV segments were included for analysis based on the American Heart Association 16-segment model. For validation, 15 pigs (10 myocardial infarction (MI) model animals and 5 controls) received CMR and were sacrificed for immunohistochemical analysis.

RESULTS

In the patient study, the acute oxygenation SI showed a stepwise rise among remote, salvaged, and infarcted segments compared with healthy myocardium. At convalescence, all oxygenation SI values besides those in infarcted segments with microvascular obstruction decreased to similar levels. Acute oxygenation SI was associated with early myocardial injury (T1: r = 0.38; T2: r = 0.41; all p < 0.05). Segments with higher acute oxygenation SI values exhibited thinner diastolic walls and decreased wall thickening during follow-up. Multivariable regression modeling indicated that acute oxygenation SI (β = 2.66; p < 0.05) independently predicted convalescent segment adverse remodeling (LV wall thinning). In the animal study, alterations in oxygenation SI were correlated with histological inflammatory infiltrates (r = 0.59; p < 0.001).

CONCLUSIONS

Myocardial oxygenation by OS-CMR could be used as a quantitative imaging biomarker to assess myocardial inflammation and predict convalescent segment adverse remodeling after STEMI.

KEY POINTS

• Oxygenation signal intensity (SI) may be an imaging biomarker of inflammatory infiltration that could be used to assess the response to anti-inflammatory therapies in the future. • Oxygenation SI early after myocardial infarction (MI) was associated with left ventricular segment injury at acute phase and could predict regional functional recovery and adverse remodeling late after acute MI. • Oxygenation SI demonstrated a stepwise increase among remote, salvaged, and infarcted segments. Infarcted zones with microvascular obstruction demonstrated a higher oxygenation SI than those without. However, the former showed less pronounced changes over time.

Characterizing Conduction Channels in Postinfarction Patients Using a Personalized Virtual Heart

Patients with myocardial infarction have an abundance of conduction channels (CC); however, only a small subset of these CCs sustain ventricular tachycardia (VT). Identifying these critical CCs (CCCs) in the clinic so that they can be targeted by ablation remains a significant challenge. The objective of this study is to use a personalized virtual-heart approach to conduct a three-dimensional (3D) assessment of CCCs sustaining VTs of different morphologies in these patients, to investigate their 3D structural features, and to determine the optimal ablation strategy for each VT. To achieve these goals, ventricular models were constructed from contrast enhanced magnetic resonance imagings of six postinfarction patients. Rapid pacing induced VTs in each model. CCCs that sustained different VT morphologies were identified. CCCs' 3D structure and type and the resulting rotational electrical activity were examined. Ablation was performed at the optimal part of each CCC, aiming to terminate each VT with a minimal lesion size. Predicted ablation locations were compared to clinical. Analyzing the simulation results, we found that the observed VTs in each patient model were sustained by a limited number (2.7 ± 1.2) of CCCs. Further, we identified three types of CCCs sustaining VTs: I-type and T-type channels, with all channel branches bounded by scar, and functional reentry channels, which were fully or partially bounded by conduction block surfaces. The different types of CCCs accounted for 43.8, 18.8, and 37.4% of all CCCs, respectively. The mean narrowest width of CCCs or a branch of CCC was 9.7 ± 3.6 mm. Ablation of the narrowest part of each CCC was sufficient to terminate VT. Our results demonstrate that a personalized virtual-heart approach can determine the possible VT morphologies in each patient and identify the CCCs that sustain reentry. The approach can aid clinicians in identifying accurately the optimal VT ablation targets in postinfarction patients.

A comprehensive, multiscale framework for evaluation of arrhythmias arising from cell therapy in the whole post-myocardial infarcted heart

Direct remuscularization approaches to cell-based heart repair seek to restore ventricular contractility following myocardial infarction (MI) by introducing new cardiomyocytes (CMs) to replace lost or injured ones. However, despite promising improvements in cardiac function, high incidences of ventricular arrhythmias have been observed in animal models of MI injected with pluripotent stem cell-derived cardiomyocytes (PSC-CMs). The mechanisms of arrhythmogenesis remain unclear. Here, we present a comprehensive framework for computational modeling of direct remuscularization approaches to cell therapy. Our multiscale 3D whole-heart modeling framework integrates realistic representations of cell delivery and transdifferentiation therapy modalities as well as representation of spatial distributions of engrafted cells, enabling simulation of clinical therapy and the prediction of emergent electrophysiological behavior and arrhythmogenensis. We employ this framework to explore how varying parameters of cell delivery and transdifferentiation could result in three mechanisms of arrhythmogenesis: focal ectopy, heart block, and reentry.

Sensitivity of Ablation Targets Prediction to Electrophysiological Parameter Variability in Image-Based Computational Models of Ventricular Tachycardia in Post-infarction Patients

Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EP_avg) or with ±10% APD or CV (i.e., EP_var); additional simulations were also executed in some models for an extended range of these parameters. The results showed that: (1) a subset of reentries (76.2–100%, depending on EP parameter set) conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EP_avg cases; (2) emergent VTs could be induced sometimes after ablation in EP_avg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EP_var parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.

Computational Identification of Ventricular Arrhythmia Risk in Pediatric Myocarditis

Children with myocarditis have increased risk of ventricular tachycardia (VT) due to myocardial inflammation and remodeling. There is currently no accepted method for VT risk stratification in this population. We hypothesized that personalized models developed from cardiac late gadolinium enhancement magnetic resonance imaging (LGE-MRI) could determine VT risk in patients with myocarditis using a previously-validated protocol. Personalized three-dimensional computational cardiac models were reconstructed from LGE-MRI scans of 12 patients diagnosed with myocarditis. Four patients with clinical VT and eight patients without VT were included in this retrospective analysis. In each model, we incorporated a personalized spatial distribution of fibrosis and myocardial fiber orientations. Then, VT inducibility was assessed in each model by pacing rapidly from 26 sites distributed throughout both ventricles. Sustained reentrant VT was induced from multiple pacing sites in all patients with clinical VT. In the eight patients without clinical VT, we were unable to induce sustained reentry in our simulations using rapid ventricular pacing. Application of our non-invasive approach in children with myocarditis has the potential to correctly identify those at risk for developing VT.

Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models

Patient specific models created from contrast-enhanced (i.e. late-gadolinium, LGE) MRI images can be used for prediction of reentry location and clinical ablation planning. However, there is still a need for direct and systematic comparison between characteristics of ventricular tachycardia (VT) morphologies predicted in computational models and those acquired in clinical or experimental protocols. In this study, we aimed to: 1) assess the differences in VT morphologies predicted by modeling and recorded in experiments in terms of patterns and location of reentries, earliest and latest activation sites, and cycle lengths; and 2) define the optimal range of infarct tissue threshold values which provide best match between simulation and experimental results. To achieve these goals, we utilized LGE-MRI images from 4 swine hearts with inducible monomorphic VT. The images were segmented to identify non-infarcted myocardium, semi viable gray zone (GZ), and core scar based on pixel intensity. Several models were reconstructed from each LGE-MRI scan, with voxels of intensity between that of non-infarcted myocardium and 20-50% of the maximum intensity (in 10% increments) in the infarct region classified as GZ. VT induction was simulated in each model. Our simulation results showed that using GZ intensity thresholds of 20% or 30% resulted in the best match of simulated propagation patterns and reentry locations with those from the experiment. Overall, we matched 70% (7/10) morphologies for all the hearts. Our simulation shows that MRI-based computational models of hearts with myocardial infarction can accurately reproduce the majority of experimentally recorded post-infarction VTs.

Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia

Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radiofrequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (5 swine) and human studies (21 patients) and in a prospective feasibility study (5 patients). We first assessed in retrospective studies (one of which included a proportion of clinical images with artifacts) the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart prior to the clinical procedure.

Risk Stratification of Sudden Cardiac Death After Acute Myocardial Infarction

Despite advances in the diagnosis and treatment of acute coronary syndromes and an overall improvement in outcomes, mortality after myocardial infarction (MI) remains high. Sudden death, which is most frequently due to ventricular tachycardia or ventricular fibrillation, is the cause of death in 25% to 50% of patients with prior MI, and therefore represents an important public health problem. Use of the implantable cardioverter-defibrillator (ICD), which is the primary method of reducing the chance of arrhythmic sudden death after MI, is costly to the medical system and is associated with procedural and long-term risks. Additionally, assessment of left ventricular ejection fraction (LVEF), which is the primary method of assessing a patient's post-MI sudden death risk and appropriateness for ICD implantation, lacks both sensitivity and specificity for sudden death, and may not be the optimal way to select the subgroup of post-MI patients who are most likely to benefit from ICD implantation. To optimally utilize ICDs, it is therefore critical to develop and prospectively validate sudden death risk stratification methods beyond measuring LVEF. A variety of tests that assess left ventricular systolic function/morphology, potential triggers for ventricular arrhythmias, ventricular conduction/repolarization, and autonomic tone have been proposed as sudden death risk stratification tools. Multivariable models have also been developed to assess the competing risks of arrhythmic and non-arrhythmic death so that ICDs can be utilized more effectively. This manuscript will review the epidemiology of sudden death after MI, and will discuss the current state of sudden death risk stratification in this population.

2D and 3D Echocardiography-Derived Indices of Left Ventricular Function and Shape: Relationship With Mortality

OBJECTIVES

This study hypothesized that left ventricular (LV) ejection fraction (EF) and global longitudinal strain (GLS) derived from 3-dimensional echocardiographic (3DE) images would better predict mortality than those obtained by 2-dimensional echocardiographic (2DE) measurements, and that 3DE-based LV shape analysis may have added prognostic value.

BACKGROUND

Previous studies have shown that both LVEF and GLS derived from 2DE images predict mortality. Recently, 3DE measurements of these parameters were found to be more accurate and reproducible because of independence of imaging plane and geometric assumptions. Also, 3DE analysis offers an opportunity to accurately quantify LV shape.

METHODS

We retrospectively studied 416 inpatients (60 ± 18 years of age) referred for transthoracic echocardiography between 2006 and 2010, who had good-quality 2DE and 3DE images were available. Mortality data through 2016 were collected. Both 2DE and 3DE images were analyzed to measure LVEF and GLS. Additionally, 3DE-derived LV endocardial surface information was analyzed to obtain global shape indices (sphericity and conicity) and regional curvature (anterior, septal, inferior, lateral walls). Cardiovascular (CV) mortality risks related to these indices were determined using Cox regression.

RESULTS

Of the 416 patients, 208 (50%) died, including 114 (27%) CV-related deaths over a mean follow-up period of 5 ± 3 years. Cox regression revealed that age and body surface area, all 4 LV function indices (2D EF, 3D EF, 2D GLS, 3D GLS), and regional shape indices (septal and inferior wall curvatures) were independently associated with increased risk of CV mortality. GLS was the strongest prognosticator of CV mortality, superior to EF for both 2DE and 3DE analyses, and 2D EF was the weakest among the 4 functional indices. A 1% decrease in GLS magnitude was associated with an 11.3% increase in CV mortality risk.

CONCLUSIONS

GLS predicts mortality better than EF by both 3DE and 2DE analysis, whereas 3D EF is a better predictor than 2D EF. Also, LV shape indices provide additional risk assessment.

A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction

AIM

To predict arrhythmia susceptibility in myocardial infarction (MI) patients with left ventricular ejection fraction (LVEF)  >35% using a personalized virtual heart simulation approach.

METHODS AND RESULTS

A total of four contrast enhanced magnetic resonance imaging (MRI) datasets of patient hearts with MI and average LVEF of 44.0 ± 2.6% were used in this study. Because of the preserved LVEF, the patients were not indicated for implantable cardioverter defibrillator (ICD) insertion. One patient had spontaneous ventricular tachycardia (VT) prior to the MRI scan; the others had no arrhythmic events. Simulations of arrhythmia susceptibility were blind to clinical outcome. Models were constructed from patient MRI images segmented to identify myocardium, grey zone, and scar based on pixel intensity. Grey zone was modelled as having altered electrophysiology. Programmed electrical stimulation (PES) was performed to assess VT inducibility from 19 bi-ventricular sites in each heart model. Simulations successfully predicted arrhythmia risk in all four patients. For the patient with arrhythmic event, in-silico PES resulted in VT induction. Simulations correctly predicted that VT was non-inducible for the three patients with no recorded VT events.

CONCLUSIONS

Results demonstrate that the personalized virtual heart simulation approach may provide a novel risk stratification modality to non-invasively and effectively identify patients with LVEF  >35% who could benefit from ICD implantation.

Sudden Cardiac Death Substrate Imaged by Magnetic Resonance Imaging: From Investigational Tool to Clinical Applications

Sudden cardiac death (SCD) is a devastating event afflicting 350 000 Americans annually despite the availability of life-saving preventive therapy, the implantable cardioverter defibrillator. SCD prevention strategies are hampered by over-reliance on global left ventricular ejection fraction <35% as the most important criterion to determine implantable cardioverter defibrillator candidacy. Annually in the United States alone, this results in ≈130 000 implantable cardioverter defibrillator placements at a cost of >$3 billion but only a 5% incidence per year of appropriate firings. This approach further fails to identify individuals who experience the majority, as many as 80%, of SCD events, which occur in the setting of more preserved left ventricular ejection fraction. Better risk stratification is needed to improve care and should be guided by direct pathophysiologic markers of arrhythmic substrate, such as specific left ventricular structural abnormalities. There is an increasing body of literature to support the prognostic value of cardiac magnetic resonance imaging with late gadolinium enhancement in phenotyping the left ventricular to identify those at highest risk for SCD. Cardiac magnetic resonance has unparalleled tissue characterization ability and provides exquisite detail about myocardial structure and composition, abnormalities of which form the direct, pathophysiologic substrate for SCD. Here, we review the evolution and the current state of cardiac magnetic resonance for imaging the arrhythmic substrate, both as a research tool and for clinical applications.

Novel Computational Analysis of Left Atrial Anatomy Improves Prediction of Atrial Fibrillation Recurrence after Ablation

The left atrium (LA) can change in size and shape due to atrial fibrillation (AF)-induced remodeling. These alterations can be linked to poorer outcomes of AF ablation. In this study, we propose a novel comprehensive computational analysis of LA anatomy to identify what features of LA shape can optimally predict post-ablation AF recurrence. To this end, we construct smooth 3D geometrical models from the segmentation of the LA blood pool captured in pre-procedural MR images. We first apply this methodology to characterize the LA anatomy of 144 AF patients and build a statistical shape model that includes the most salient variations in shape across this cohort. We then perform a discriminant analysis to optimally distinguish between recurrent and non-recurrent patients. From this analysis, we propose a new shape metric called vertical asymmetry, which measures the imbalance of size along the anterior to posterior direction between the superior and inferior left atrial hemispheres. Vertical asymmetry was found, in combination with LA sphericity, to be the best predictor of post-ablation recurrence at both 12 and 24 months (area under the ROC curve: 0.71 and 0.68, respectively) outperforming other shape markers and any of their combinations. We also found that model-derived shape metrics, such as the anterior-posterior radius, were better predictors than equivalent metrics taken directly from MRI or echocardiography, suggesting that the proposed approach leads to a reduction of the impact of data artifacts and noise. This novel methodology contributes to an improved characterization of LA organ remodeling and the reported findings have the potential to improve patient selection and risk stratification for catheter ablations in AF.

Association Between Myocardial Mechanics and Ischemic LV Remodeling

The outcomes associated with heart failure after myocardial infarction are still poor. Both global and regional left ventricular (LV) remodeling are associated with the progression of the post-infarct patient to heart failure, but although global remodeling can be accurately measured, regional LV remodeling has been more difficult to investigate. Preliminary evidence suggests that post-MI assessment of LV mechanics using stress and strain may predict global (and possibly regional) LV remodeling. A method of predicting both global and regional LV remodeling might facilitate earlier, targeted, and more extensive clinical intervention in those most likely to benefit from novel interventions such as cell therapy.

Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models

Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.

Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models

Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.

Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention

Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications.

Principles and techniques of imaging in identifying the substrate of ventricular arrhythmia

Life-threatening ventricular arrhythmias (VA) are a major cause of death in patients with cardiomyopathy. To date, impaired left ventricular ejection fraction remains the primary criterion for implantable cardioverter-defibrillator therapy to prevent sudden cardiac death. In recent years, however, advanced imaging techniques such as nuclear imaging, cardiac magnetic resonance imaging, and computed tomography have allowed for a more detailed evaluation of the underlying substrate of VA. These imaging modalities have emerged as a promising approach to assess the risk of sudden cardiac death. In addition, non-invasive identification of the critical sites of arrhythmias may guide ablation therapy. Typical anatomical substrates that can be evaluated by multiple advanced imaging techniques include perfusion abnormalities, scar and its border zone, and sympathetic denervation. Understanding the principles and techniques of different imaging modalities is essential to gain more insight in their role in identifying the arrhythmic substrate. The current review describes the principles of currently available imaging techniques to identify the substrate of VA.

Predicting the risk of sudden cardiac death

Sudden cardiac death (SCD) is the result of a change of cardiac activity from normal (typically sinus) rhythm to a rhythm that does not pump adequate blood to the brain. The most common rhythms leading to SCD are ventricular tachycardia (VT) or ventricular fibrillation (VF). These result from an accelerated ventricular pacemaker or ventricular reentrant waves. Despite significant efforts to develop accurate predictors for the risk of SCD, current methods for risk stratification still need to be improved. In this article we briefly review current approaches to risk stratification. Then we discuss the mathematical basis for dynamical transitions (called bifurcations) that may lead to VT and VF. One mechanism for transition to VT or VF involves a perturbation by a premature ventricular complex (PVC) during sinus rhythm. We describe the main mechanisms of PVCs (reentry, independent pacemakers and abnormal depolarizations). An emerging approach to risk stratification for SCD involves the development of individualized dynamical models of a patient based on measured anatomy and physiology. Careful analysis and modelling of dynamics of ventricular arrhythmia on an individual basis will be essential in order to improve risk stratification for SCD and to lay a foundation for personalized (precision) medicine in cardiology.

How computer simulations of the human heart can improve anti-arrhythmia therapy

Over the last decade, the state-of-the-art in cardiac computational modelling has progressed rapidly. The electrophysiological function of the heart can now be simulated with a high degree of detail and accuracy, opening the doors for simulation-guided approaches to anti-arrhythmic drug development and patient-specific therapeutic interventions. In this review, we outline the basic methodology for cardiac modelling, which has been developed and validated over decades of research. In addition, we present several recent examples of how computational models of the human heart have been used to address current clinical problems in cardiac electrophysiology. We will explore the use of simulations to improve anti-arrhythmic pacing and defibrillation interventions; to predict optimal sites for clinical ablation procedures; and to aid in the understanding and selection of arrhythmia risk markers. Together, these studies illustrate how the tremendous advances in cardiac modelling are poised to revolutionize medical treatment and prevention of arrhythmia.

Computational modeling of cardiac optogenetics: Methodology overview & review of findings from simulations

Cardiac optogenetics is emerging as an exciting new potential avenue to enable spatiotemporally precise control of excitable cells and tissue in the heart with low-energy optical stimuli. This approach involves the expression of exogenous light-sensitive proteins (opsins) in target heart tissue via viral gene or cell delivery. Preliminary experiments in optogenetically-modified cells, tissue, and organisms have made great strides towards demonstrating the feasibility of basic applications, including the use of light stimuli to pace or disrupt reentrant activity. However, it remains unknown whether techniques based on this intriguing technology could be scaled up and used in humans for novel clinical applications, such as pain-free optical defibrillation or dynamic modulation of action potential shape. A key step towards answering such questions is to explore potential optogenetics-based therapies using sophisticated computer simulation tools capable of realistically representing opsin delivery and light stimulation in biophysically detailed, patient-specific models of the human heart. This review provides (1) a detailed overview of the methodological developments necessary to represent optogenetics-based solutions in existing virtual heart platforms and (2) a survey of findings that have been derived from such simulations and a critical assessment of their significance with respect to the progress of the field.