Cardiovascular Magnetic Resonance to Predict Appropriate Implantable Cardioverter Defibrillator Therapy in Ischemic and Nonischemic Cardiomyopathy Patients Using Late Gadolinium Enhancement Border Zone: Comparison of Four Analysis Methods

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.

Impact of a Chronic Total Coronary Occlusion on the Incidence of Appropriate Implantable Cardioverter-Defibrillator Shocks and Mortality: A Substudy of the Dutch Outcome in ICD Therapy (DO-IT)) Registry

BACKGROUND

Chronic total coronary occlusions (CTO) substantially increase the risk for sudden cardiac death. Among patients with chronic ischemic heart disease at risk for sudden cardiac death, an implantable cardioverter defibrillator (ICD) is the favored therapy for primary prevention of sudden cardiac death. This study sought to investigate the impact of CTOs on the risk for appropriate ICD shocks and mortality within a nationwide prospective cohort.

METHODS AND RESULTS

This is a subanalysis of the nationwide Dutch-Outcome in ICD Therapy (DO-IT) registry of primary prevention ICD recipients in The Netherlands between September 2014 and June 2016 (n=1442). We identified patients with chronic ischemic heart disease (n=663) and assessed available coronary angiograms for CTO presence (n=415). Patients with revascularized CTOs were excluded (n=79). The primary end point was the composite of all-cause mortality and appropriate ICD shocks. Clinical follow-up was conducted for at least 2 years. A total of 336 patients were included, with an average age of 67±9 years, and 20.5% was female (n=69). An unrevascularized CTO was identified in 110 patients (32.7%). During a median follow-up period of 27 months (interquartile range, 24-32), the primary end point occurred in 21.1% of patients with CTO (n=23) compared with 11.9% in patients without CTO (n=27; P=0.034). Corrected for baseline characteristics including left ventricular ejection fraction, and the presence of a CTO was an independent predictor for the primary end point (hazard ratio, 1.82 [95% CI, 1.03-3.22]; P=0.038).

CONCLUSIONS

Within this nationwide prospective registry of primary prevention ICD recipients, the presence of an unrevascularized CTO was an independent predictor for the composite outcome of all-cause mortality and appropriate ICD shocks.

The Role of Cardiovascular Magnetic Resonance Imaging in Patients with Cardiac Arrhythmias

Cardiac arrhythmias are associated with significant morbidity, mortality and poor quality of life. Cardiovascular magnetic resonance (CMR) imaging, with its unsurpassed capability of non-invasive tissue characterisation, high accuracy, and reproducibility of measurements, plays an integral role in determining the underlying aetiology of cardiac arrhytmias. CMR can reliably diagnose previous myocardial infarction, non-ischemic cardiomyopathy, characterise congenital heart disease and valvular pathologies, and also detect the underlying substrate concealed on conventional investigations in a significant proportion of patients with arrhythmias. Determining the underlying substrate of arrhythmia is of paramount importance for treatment planning and prognosis. However, CMR imaging in patients with irregular heart rates can be problematic. Understanding the different ways to overcome the limitations of CMR in arrhythmia is essential for providing high-quality imaging, comprehensive information, and definitive answers in this diverse group of patients.

Prognostic Value of Different Thresholds for Myocardial Scar Quantification on Cardiac MRI Late Gadolinium Enhancement Images in Patients Receiving Implantable Cardioverter Defibrillators

Purpose

To compare the predictive value of different myocardial scar quantification thresholds using cardiac MRI for appropriate implantable cardioverter defibrillator (ICD) shock and mortality.

Materials and Methods

In this retrospective, two-center observational cohort study, patients with ischemic or nonischemic cardiomyopathy underwent cardiac MRI prior to ICD implantation. Late gadolinium enhancement (LGE) was first determined visually and then quantified by blinded cardiac MRI readers using different SDs above the mean signal of normal myocardium, full-width half-maximum, and manual thresholding. The intermediate signal "gray zone" was calculated as the differences between different SDs.

Results

Among 374 consecutive eligible patients (mean age, 61 years ± 13 [SD]; mean left ventricular ejection fraction, 32% ± 14; secondary prevention, 62.7%), those with LGE had a higher rate of appropriate ICD shock or death than those without (37.5% vs 26.6%, log-rank P = .04) over a median follow-up of 61 months. In multivariable analysis, none of the thresholds for quantifying scar was a significant predictor of mortality or appropriate ICD shock, while the extent of gray zone was an independent predictor (adjusted hazard ratio per 1 g = 1.025; 95% CI: 1.008, 1.043; P = .005) regardless of the presence or absence of ischemic heart disease (P interaction = .57). Model discrimination was highest for the model incorporating the gray zone (between 2 SD and 4 SD).

Conclusion

Presence of LGE was associated with a higher rate of appropriate ICD shock or death. Although none of the scar quantification techniques predicted outcomes, the gray zone both in infarct and nonischemic scar was an independent predictor and may refine risk stratification.Keywords: MRI, Scar Quantification, Implantable Cardioverter Defibrillator, Sudden Cardiac Death Supplemental material is available for this article. © RSNA, 2023.

Infarct quantification with cardiovascular magnetic resonance using "standard deviation from remote" is unreliable: validation in multi-centre multi-vendor data

BACKGROUND

The objective of the study was to investigate variability and agreement of the commonly used image processing method "n-SD from remote" and in particular for quantifying myocardial infarction by late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR). LGE-CMR in tandem with the analysis method "n-SD from remote" represents the current reference standard for infarct quantification. This analytic method utilizes regions of interest (ROIs) and defines infarct as the tissue with a set number of standard deviations (SD) above the signal intensity of remote nulled myocardium. There is no consensus on what the set number of SD is supposed to be. Little is known about how size and location of ROIs and underlying signal properties in the LGE images affect results. Furthermore, the method is frequently used elsewhere in medical imaging often without careful validation. Therefore, the usage of the "n-SD" method warrants a thorough validation.

METHODS

Data from 214 patients from two multi-center cardioprotection trials were included. Infarct size from different remote ROI positions, ROI size, and number of standard deviations ("n-SD") were compared with reference core lab delineations.

RESULTS

Variability in infarct size caused by varying ROI position, ROI size, and "n-SD" was 47%, 48%, and 40%, respectively. The agreement between the "n-SD from remote" method and the reference infarct size by core lab delineations was low. Optimal "n-SD" threshold computed on a slice-by-slice basis showed high variability, n = 5.3 ± 2.2.

CONCLUSION

The "n-SD from remote" method is unreliable for infarct quantification due to high variability which depends on different placement and size of remote ROI, number "n-SD", and image signal properties related to the CMR-scanner and sequence used. Therefore, the "n-SD from remote" method should not be used, instead methods validated against an independent standard are recommended.

Predicting arrhythmia recurrence following catheter ablation for ventricular tachycardia using late gadolinium enhancement magnetic resonance imaging: Implications of varying scar ranges

BACKGROUND

Thresholding-based analysis of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) can create scar maps and identify corridors that might provide a reentrant substrate for ventricular tachycardia (VT). Current recommendations use a full-width-at-half-maximum approach, effectively classifying areas with a pixel signal intensity (PSI) >40% as border zone (BZ) and >60% as core.

OBJECTIVE

The purpose of this study was to investigate the impact of 4 different threshold settings on scar and corridor quantification and to correlate this with postablation VT recurrence.

METHODS

Twenty-seven patients with ischemic cardiomyopathy who had undergone catheter ablation for VT were included for retrospective analysis. LGE-CMR images were analyzed using ADAS3D LV. Scar maps were created for 4 PSI thresholds (40-60, 35-65, 30-70, and 45-55), and the extent of variation in BZ and core, as well as the number and weight of conduction corridors, were quantified. Three-dimensional representations were reconstructed from exported segmentations and used to quantify the surface area between healthy myocardium and scar (BZ + core), and between BZ and core.

RESULTS

A wider PSI threshold was associated with an increase in BZ mass and decrease in scar (P <.001). No significant differences were observed for the total number of corridors and their mass with increasing PSI threshold. The best correlation in predicting arrhythmia recurrence was observed for PSI 45-55 (area under the curve 0.807; P = .001).

CONCLUSION

Varying PSI has a significant impact on quantification of LGE-CMR parameters and may have incremental clinical value in predicting arrhythmia recurrence. Further prospective investigation is warranted to clarify the functional implications of these findings for LGE-CMR-guided ventricular ablation.

Role of advanced imaging with cardiac computed tomography and MRI in atrial and ventricular ablation

PURPOSE OF REVIEW

Increasing evidence supports the use of advanced imaging with cardiac computed tomography (CCT) and cardiac magnetic resonance (CMR) in the work-up of patients with arrythmias being considered for ablation.

RECENT FINDINGS

Advances in imaging technology and postprocessing are facilitating the use of advanced imaging before, during and after ablation in patients with both atrial and ventricular arrhythmias.In atrial arrythmias, quantitative assessment of left atrial wall thickness on CCT and quantification of late gadolinium enhancement (LGE) on CMR identify patients more likely to develop recurrent atrial arrythmias following ablation. In addition, in patients with recurrent arrythmia post ablation, LGE CMR can potentially identify targets for repeat ablation.In ventricular arrythmias, qualitative assessment of LGE can aide in determining the optimal ablation approach and predicts likelihood of ventricular arrythmias inducibility. Quantitative assessment of LGE can identify conduction channels that can be targeted for ablation. On CCT, quantitative assessment of left ventricular wall thickness can demonstrate myocardial ridges associated with re-entrant circuits for ablation.

SUMMARY

This review focuses on the utility of CCT and CMR in identifying key anatomical components and arrhythmogenic substrate contributing to both atrial and ventricular arrhythmias in patients being considered for ablation. Advanced imaging has the potential to improve procedural outcomes, decrease complications and shorten procedural time.

ACR Appropriateness Criteria® Dyspnea-Suspected Cardiac Origin (Ischemia Already Excluded): 2021 Update

Dyspnea is the symptom of perceived breathing discomfort and is commonly encountered in a variety of clinical settings. Cardiac etiologies of dyspnea are an important consideration; among these, valvular heart disease (Variant 1), arrhythmia (Variant 2), and pericardial disease (Variant 3) are reviewed in this document. Imaging plays an important role in the clinical assessment of these suspected abnormalities, with usually appropriate procedures including resting transthoracic echocardiography in all three variants, radiography for Variants 1 and 3, MRI heart function and morphology in Variants 2 and 3, and CT heart function and morphology with intravenous contrast for Variant 3. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Recent Non-Invasive Parameters to Identify Subjects at High Risk of Sudden Cardiac Death

Cardiovascular diseases remain among the leading causes of death worldwide and sudden cardiac death (SCD) accounts for ~25% of these deaths. Despite its epidemiologic relevance, there are very few diagnostic strategies available useful to prevent SCD mainly focused on patients already affected by specific cardiovascular diseases. Unfortunately, most of these parameters exhibit poor positive predictive accuracy. Moreover, there is also a need to identify parameters to stratify the risk of SCD among otherwise healthy subjects. This review aims to provide an update on the most relevant non-invasive diagnostic features to identify patients at higher risk of developing malignant ventricular arrhythmias and SCD.

Anatomically informed deep learning on contrast-enhanced cardiac magnetic resonance imaging for scar segmentation and clinical feature extraction

Background

Visualizing fibrosis on cardiac magnetic resonance (CMR) imaging with contrast enhancement (late gadolinium enhancement; LGE) is paramount in characterizing disease progression and identifying arrhythmia substrates. Segmentation and fibrosis quantification from LGE-CMR is intensive, manual, and prone to interobserver variability. There is an unmet need for automated LGE-CMR image segmentation that ensures anatomical accuracy and seamless extraction of clinical features.

Objective

This study aimed to develop a novel deep learning solution for analysis of contrast-enhanced CMR images that produces anatomically accurate myocardium and scar/fibrosis segmentations and uses these to calculate features of clinical interest.

Methods

Data sources were 155 2-dimensional LGE-CMR patient scans (1124 slices) and 246 synthetic "LGE-like" scans (1360 slices) obtained from cine CMR using a novel style-transfer algorithm. We trained and tested a 3-stage neural network that identified the left ventricle (LV) region of interest (ROI), segmented ROI into viable myocardium and regions of enhancement, and postprocessed the segmentation results to enforce conforming to anatomical constraints. The segmentations were used to directly compute clinical features, such as LV volume and scar burden.

Results

Predicted LV and scar segmentations achieved 96% and 75% balanced accuracy, respectively, and 0.93 and 0.57 Dice coefficient when compared to trained expert segmentations. The mean scar burden difference between manual and predicted segmentations was 2%.

Conclusion

We developed and validated a deep neural network for automatic, anatomically accurate expert-level LGE- CMR myocardium and scar/fibrosis segmentation, allowing direct calculation of clinical measures. Given the training set heterogeneity, our approach could be extended to multiple imaging modalities and patient pathologies.

The Role of AI in Characterizing the DCM Phenotype

Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.

Time-efficient three-dimensional transmural scar assessment provides relevant substrate characterization for ventricular tachycardia features and long-term recurrences in ischemic cardiomyopathy

Delayed gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging requires novel and time-efficient approaches to characterize the myocardial substrate associated with ventricular arrhythmia in patients with ischemic cardiomyopathy. Using a translational approach in pigs and patients with established myocardial infarction, we tested and validated a novel 3D methodology to assess ventricular scar using custom transmural criteria and a semiautomatic approach to obtain transmural scar maps in ventricular models reconstructed from both 3D-acquired and 3D-upsampled-2D-acquired LGE-CMR images. The results showed that 3D-upsampled models from 2D LGE-CMR images provided a time-efficient alternative to 3D-acquired sequences to assess the myocardial substrate associated with ischemic cardiomyopathy. Scar assessment from 2D-LGE-CMR sequences using 3D-upsampled models was superior to conventional 2D assessment to identify scar sizes associated with the cycle length of spontaneous ventricular tachycardia episodes and long-term ventricular tachycardia recurrences after catheter ablation. This novel methodology may represent an efficient approach in clinical practice after manual or automatic segmentation of myocardial borders in a small number of conventional 2D LGE-CMR slices and automatic scar detection.

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.

The prognostic role of late gadolinium enhancement on cardiac magnetic resonance in patients with nonischemic cardiomyopathy and reduced ejection fraction, implanted with cardioverter defibrillators for primary prevention. A systematic review and meta-analysis

BACKGROUND

Previous studies suggest that late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) is associated with arrhythmic events in patients with nonischemic cardiomyopathy (NICM), while others have questioned the role of left ventricular ejection fraction (LVEF) as a sole predictor of future events.

OBJECTIVES

To evaluate the role of LGE on CMR in identifying patients with NICM and reduced LVEF for whom a benefit from defibrillator implantation for primary prevention is not anticipated, thus they are mainly exposed to potential risks.

METHODS

Major electronic databases were searched for studies reporting the incidence of appropriate device therapy (ADT), sudden cardiac death (SCD), and cardiac death based on the presence of LGE on CMR, among patients with NICM and reduced LVEF, implanted with a cardioverter defibrillator for primary prevention.

RESULTS

Eleven studies (1652 patients, 947 with LGE) were included in the final analysis. LGE presence was strongly associated with ADT (logOR: 1.95, 95%CI: 1.21-2.69) and cardiac death (logOR: 0.91, 95%CI: 0.14-1.68), but not with SCD (logOR: 0.26, 95%CI: -1.09-1.6). Diagnostic accuracy analysis demonstrated that contrast enhancement is a sensitive marker of future ADT and cardiac death (93%, 95%CI: 85.8-96.7%; 82.9%, 95%CI: 70.6-90.7%; respectively), with moderate specificity ( 44%, 95%CI: 27.2-62.6%; 37.7%, 95%CI: 23.4-54.6%; respectively).

CONCLUSION

LGE is a highly sensitive predictor of ADT and cardiac death in NICM patients implanted with a defibrillator for primary prevention. However, due to moderate specificity, derivation of a cutoff with adequate predictive values and probably a multifactorial approach are needed to improve discrimination of patients who will not benefit from ICDs.

Risk stratification for sudden cardiac death in patients with heart failure : Emerging role of imaging parameters

BACKGROUND

Heart failure with reduced ejection fraction is a common condition that has a poor prognosis. Accurate selection of patients with ischemic heart disease and idiopathic dilated cardiomyopathy, who are at risk of sudden cardiac death (SCD), remains a challenge. In these cases, current indications for implantable cardioverter-defibrillators (ICD) rely almost entirely on left ventricular ejection fraction. However, this parameter is insufficient. Recently, noninvasive imaging has provided insight into the mechanism underlying SCD using myocardial deformation on echocardiography and magnetic resonance imaging. The aim of this review article was to underline the emerging role of these novel parameters in identifying high-risk patients.

METHODS

A literature search was carried out for reports published with the following terms: "sudden cardiac death," "heart failure," "noninvasive imaging," "echocardiography," "deformation," "magnetic resonance imaging," and "ventricular arrhythmia." The search was restricted to reports published in English.

RESULTS

The findings of this analysis suggest that cardiac magnetic resonance imaging and strain assessment by echocardiography, particularly longitudinal strain, can be promising techniques for cardiovascular risk stratification in patients with heart failure.

CONCLUSION

In future, risk stratification of arrhythmia and patient selection for ICD placement may rely on a multiparametric approach using combinations of imaging modalities in addition to left ventricular ejection fraction.

Cardiac Resynchronization Therapy in Patients with Heart Failure: What is New?

Cardiac resynchronization therapy (CRT) is an established treatment of patients with medically refractory, mild-to-severe systolic heart failure (HF), impaired left ventricular function, and wide QRS complex. The pathologic activation sequence observed in patients with abnormal QRS duration and morphology results in a dyssynchronous ventricular activation and contraction leading to cardiac remodeling, worsening systolic and diastolic function, and progressive HF. In this article, the authors aim to explore the current CRT literature, focusing their attentions on the promising innovation in this field.

Primary Prevention Implantable Cardioverter-Defibrillator Therapy in Heart Failure with Recovered Ejection Fraction

Given recent advances in both pharmacologic and nonpharmacologic strategies for improving outcomes related to chronic systolic heart failure, heart failure with recovered ejection fraction (HFrecEF) is now recognized as a distinct clinical entity with increasing prevalence. In many patients who once had an indication for active implantable cardioverter-defibrillator (ICD) therapy, questions remain regarding the usefulness of this primary prevention strategy to protect against syncope and cardiac arrest after they have achieved myocardial recovery. Early, small studies provide convincing evidence for continued guideline-directed medical therapy (GDMT) in segments of the HFrecEF population to promote persistent left ventricular myocardial recovery. Retrospective data suggest that the risk of sudden cardiac death is lower, but still present, in HFrecEF as compared with HF with reduced ejection fraction, with reports of up to 5 appropriate ICD therapies delivered per 100 patient-years. The usefulness of continued ICD therapy is weighed against the unfavorable effects of this strategy, which include a cumulative risk of infection, inappropriate discharge, and patient-level anxiety. Historically, many surrogate measures for risk stratification have been explored, but few have demonstrated efficacy and widespread availability. We found that the available data to inform decisions surrounding the continued use of active ICD therapies in this population are incomplete, and more advanced tools such as genetic testing, evaluation of high-risk structural cardiomyopathies (such as noncompaction), and cardiac magnetic resonance imaging have emerged as vital in risk stratification. Clinicians and patients should engage in shared decision-making to evaluate the appropriateness of active ICD therapy for any given individual. In this article, we explore the definition of HFrecEF, data underlying continuation of guideline-directed medical therapy in patients who have achieved left ventricular ejection fraction recovery, the benefits and risks of active ICD therapy, and surrogate measures that may have a role in risk stratification.

Late-Gadolinium Enhancement Interface Area and Electrophysiological Simulations Predict Arrhythmic Events in Patients With Nonischemic Dilated Cardiomyopathy

OBJECTIVES

This study sought to investigate whether shape-based late gadolinium enhancement (LGE) metrics and simulations of re-entrant electrical activity are associated with arrhythmic events in patients with nonischemic dilated cardiomyopathy (NIDCM).

BACKGROUND

The presence of LGE predicts life-threatening ventricular arrhythmias in NIDCM; however, risk stratification remains imprecise. LGE shape and simulations of electrical activity may be able to provide additional prognostic information.

METHODS

Cardiac magnetic resonance (CMR)-LGE shape metrics were computed for a cohort of 156 patients with NIDCM and visible LGE and tested retrospectively for an association with an arrhythmic composite endpoint of sudden cardiac death and ventricular tachycardia. Computational models were created from images and used in conjunction with simulated stimulation protocols to assess the potential for re-entry induction in each patient's scar morphology. A mechanistic analysis of the simulations was carried out to explain the associations.

RESULTS

During a median follow-up of 1,611 (interquartile range: 881 to 2,341) days, 16 patients (10.3%) met the primary endpoint. In an inverse probability weighted Cox regression, the LGE-myocardial interface area (hazard ratio [HR]: 1.75; 95% confidence interval [CI]: 1.24 to 2.47; p = 0.001), number of simulated re-entries (HR: 1.40; 95% CI: 1.23 to 1.59; p < 0.01) and LGE volume (HR: 1.44; 95% CI: 1.07 to 1.94; p = 0.02) were associated with arrhythmic events. Computational modeling revealed repolarization heterogeneity and rate-dependent block of electrical wavefronts at the LGE-myocardial interface as putative arrhythmogenic mechanisms directly related to the LGE interface area.

CONCLUSIONS

The area of interface between scar and surviving myocardium, as well as simulated re-entrant activity, are associated with an elevated risk of major arrhythmic events in patients with NIDCM and LGE and represent novel risk predictors.

Updating the Risk Stratification for Sudden Cardiac Death in Cardiomyopathies: The Evolving Role of Cardiac Magnetic Resonance Imaging. An Approach for the Electrophysiologist

The prevention of sudden cardiac death (SCD) in cardiomyopathies (CM) remains a challenge. The current guidelines still favor the implantation of devices for the primary prevention of SCD only in patients with severely reduced left ventricular ejection fraction (LVEF) and heart failure (HF) symptoms. The implantation of an implantable cardioverter-defibrillator (ICD) is a protective barrier against arrhythmic events in CMs, but the benefit does not outweigh the cost in low risk patients. The identification of high risk patients is the key to an individualized prevention strategy. Cardiac magnetic resonance (CMR) provides reliable and reproducible information about biventricular function and tissue characterization. Furthermore, late gadolinium enhancement (LGE) quantification and pattern of distribution, as well as abnormal T1 mapping and extracellular volume (ECV), representing indices of diffuse fibrosis, can enhance our ability to detect high risk patients. CMR can also complement electro-anatomical mapping (EAM), a technique already applied in the risk evaluation and in the ventricular arrhythmias ablation therapy of CM patients, providing a more accurate assessment of fibrosis and arrhythmic corridors. As a result, CMR provides a new insight into the pathological substrate of CM. CMR may help identify high risk CM patients and, combined with EAM, can provide an integrated evaluation of scar and arrhythmic corridors in the ablative therapy of ventricular arrhythmias.

Arrhythmic risk stratification by cardiac magnetic resonance tissue characterization: disclosing the arrhythmic substrate within the heart muscle

Sudden cardiac death (SCD) is a pivotal health problem worldwide. The identification of subjects at increased risk of SCD is crucial for the accurate selection of candidates for implantable cardioverter defibrillator (ICD) therapy. Current strategies for arrhythmic stratification largely rely on left ventricular (LV) ejection fraction (EF), mostly measured by echocardiography, and New York Heart Association functional status for heart failure with reduced EF. For specific diseases, such as hypertrophic and arrhythmogenic cardiomyopathy, some risk scores have been proposed; however, these scores take into account some parameters that are a partial reflection of the global arrhythmic risk and show a suboptimal accuracy. Thanks to a more comprehensive evaluation, cardiac magnetic resonance (CMR) provides insights into the heart muscle (the so-called tissue characterization) identifying cardiac fibrosis as an arrhythmic substrate. Combining sequences before and after administration of contrast media and mapping techniques, CMR is able to characterize the myocardial tissue composition, shedding light on both intracellular and extracellular alterations. Over time, late gadolinium enhancement (LGE) emerged as solid prognostic marker, strongly associated with major arrhythmic events regardless of LVEF, adding incremental value over current strategy in ischemic heart disease and non-ischemic cardiomyopathies. The evidence on a potential prognostic role of mapping imaging is promising. However, mapping techniques require further investigation and standardization. Disclosing the arrhythmic substrate within the myocardium, CMR should be considered as part of a multiparametric approach to personalized arrhythmic stratification.

Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

BACKGROUND

Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms.

OBJECTIVE

The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML.

METHODS

To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees and effect plots. We illustrated the approach based on postprocessing of ML predictions, in this case random forest predictions, and applied the method to data from the Left Ventricular (LV) Structural Predictors of Sudden Cardiac Death (SCD) Registry for individualized risk prediction of SCD, a leading cause of death.

RESULTS

With the LV Structural Predictors of SCD Registry data, SCD risk predictions are obtained from a random forest algorithm that identifies the most important predictors, nonlinearities, and interactions among a large number of variables while naturally accounting for missing data. The black box predictions are postprocessed using classification and regression trees into a clinically relevant and interpretable visualization. The method also quantifies the relative importance of an individual or a combination of predictors. Several risk factors (heart failure hospitalization, cardiac magnetic resonance imaging indices, and serum concentration of systemic inflammation) can be clearly visualized as branch points of a decision tree to discriminate between low-, intermediate-, and high-risk patients.

CONCLUSIONS

Through a clinically important example, we illustrate a general and simple approach to increase the clinical translation of ML through clinician-tailored visual displays of results from black box algorithms. We illustrate this general model-agnostic framework by applying it to SCD risk prediction. Although we illustrate the methods using SCD prediction with random forest, the methods presented are applicable more broadly to improving the clinical translation of ML, regardless of the specific ML algorithm or clinical application. As any trained predictive model can be summarized in this manner to a prespecified level of precision, we encourage the use of simplified visual displays as an adjunct to the complex predictive model. Overall, this framework can allow clinicians to peek inside the black box and develop a deeper understanding of the most important features from a model to gain trust in the predictions and confidence in applying them to clinical care.

Value of CMR and PET in Predicting Ventricular Arrhythmias in Ischemic Cardiomyopathy Patients Eligible for ICD

OBJECTIVES

This study presents a head-to-head comparison of the value of cardiac magnetic resonance (CMR)-derived left-ventricular (LV) function and scar burden and positron emission tomography (PET)-derived perfusion and innervation in predicting ventricular arrhythmias (VAs).

BACKGROUND

Improved risk stratification of VA is important to identify patients who should benefit of prophylactic implantable cardioverter-defibrillator (ICD) implantation. Perfusion abnormalities, sympathetic denervation, and scar burden have all been linked to VA, although comparative studies are lacking.

METHODS

Seventy-four patients with ischemic cardiomyopathy and left-ventricular ejection fraction (LVEF) ≤35%, referred for primary prevention ICD placement were enrolled prospectively. Late gadolinium-enhanced (LGE) CMR was performed to assess LV function and scar characteristics. [¹⁵O]H₂O and [¹¹C]hydroxyephedrine positron emission tomography (PET) were performed to quantify resting and hyperemic myocardial blood flow (MBF), coronary flow reserve (CFR), and sympathetic innervation. During follow-up of 5.4 ± 1.9 years, the occurrence of sustained VA, appropriate ICD therapy, and mortality were evaluated.

RESULTS

In total, 20 (26%) patients experienced VA. CMR and PET parameters showed considerable overlap between patients with VA and patients without VA, caused by substantial heterogeneity within groups. Univariable analyses showed that lower LVEF (hazard ratio [HR]: 0.92; p = 0.03), higher left-ventricular end-diastolic volume index (LVEDVi) (HR 1.02; p < 0.01), and larger scar border zone (HR 1.11; p = 0.03) were related to VA. Scar core size, resting MBF, hyperemic MBF, perfusion defect size, innervation defect size, and the innervation-perfusion mismatch were not found to be associated with VA.

CONCLUSIONS

In patients with ischemic cardiomyopathy, lower LVEF, higher LVEDVi, and larger scar border zone were related to VA. PET-derived perfusion and sympathetic innervation, as well as CMR-derived scar core size were not associated with VA. These results suggest that improved prediction of VA by advanced imaging remains challenging for the individual patient.

Cardiac Magnetic Resonance in Primary Prevention of Sudden Cardiac Death

Sudden death accounts for 400,000 deaths annually in the United States. Most sudden deaths are cardiac and are related to arrhythmias secondary to structural heart disease or primary electrical abnormalities of the heart. Implantable cardioverter defibrillator significantly improves survival in patients at increased risk of life-threatening arrhythmias, but better selection of eligible patients is required to avoid unnecessary implantation and identify those patients who may benefit most from this therapy. Left ventricular (LV) ejection fraction (EF) measured by echocardiography has been considered the most reliable parameter for long-term outcome in many cardiac diseases. However, LVEF is an inaccurate parameter for arrhythmic risk assessment as patients with normal or mildly reduced LV systolic function could experience sudden cardiac death (SCD). Among other tools for arrhythmic stratification, magnetic resonance (CMR) provides the most comprehensive cardiac evaluation including in vivo tissue characterization and significantly aids in the identification of patients at higher SCD risk. Most of the evidence are related to late gadolinium enhancement (LGE), which was proven to detect cardiac fibrosis. LGE has been reported to add incremental value for prognostic stratification and SCD prediction across a wide range of cardiac diseases, including both ischemic and nonischemic cardiomyopathies. In addition, T1, T2 mapping and extracellular volume assessment were reported to add incremental value for arrhythmic assessment despite suffering from several technical limitations. CMR should be part of a multiparametric approach for patients' evaluation, and it will play a pivotal role in prognostic stratification according to the current evidence.

Peri-Infarct Quantification by Cardiac Magnetic Resonance to Predict Outcomes in Ischemic Cardiomyopathy

Background:

In ischemic cardiomyopathy, cardiac magnetic resonance assessment of the peri-infarct zone, a potential substrate for arrhythmogenesis, may serve as a novel prognosticator and guide the optimal use of implantable cardioverter-defibrillators. We undertook a systematic review and meta-analysis assessing the prognostic value of the peri-infarct zone on late gadolinium enhancement cardiac magnetic resonance in ischemic cardiomyopathy.

Methods:

We searched MEDLINE (Medical Literature Analysis and Retrieval System Online), EMBASE (Medical Literature Analysis and Retrieval System Online), and CENTRAL (Medical Literature Analysis and Retrieval System Online) from inception to January 2019 for prognostic studies relating peri-infarct size with clinical outcomes in ischemic cardiomyopathy. Two authors independently performed study selection and data extraction. Pooled effect estimates were calculated with random effects models, risk of bias and strength of evidence were assessed by the Quality in Prognostic Studies tool and Grading of Recommendations Assessment, Development, and Education, respectively.

Results:

Twenty studies were eligible, representing 14 cohort studies (n=1518) with mean follow-up of 3.6 years and 6 cross-sectional studies (n=189). The extent of the peri-infarct zone was significantly predictive of all-cause mortality (3 studies; n=539; hazard ratio, 1.34/10 g [95% CI, 1.13–1.59]; I 2 =0%; high-quality evidence), appropriate implantable cardioverter-defibrillator therapy (5 studies; n=361; hazard ratio, 1.31/10 g [95% CI, 1.17–1.47]; I 2 =0%; high-quality evidence), and inducibility of ventricular tachycardia on electrophysiological study (5 studies; n=167; OR, 2.63/g [95% CI, 1.39–4.96]; I 2 =14%; low-quality evidence). After adjusting for age and left ventricular ejection fraction, the peri-infarct zone, as a percentage of total infarct size, remained an independent predictor of all-cause mortality (2 studies; n=445; hazard ratio, 1.29/10% [95% CI, 1.15–1.44]; I 2 =0%; high-quality evidence).

Conclusions:

There is limited but consistent evidence that quantification of the peri-infarct zone predicts long-term mortality and appropriate implantable cardioverter-defibrillator therapy in ischemic cardiomyopathy. Future studies should confirm whether late gadolinium enhancement-cardiac magnetic resonance assessment may improve implantable cardioverter-defibrillator treatment decisions.

Clinical Trial Registration:

URL: https://www.crd.york.ac.uk/prospero/ . Unique identifier: CRD42017077337.

The Role of Cardiac MRI in the Management of Ventricular Arrhythmias in Ischaemic and Non-ischaemic Dilated Cardiomyopathy

Ventricular tachycardia (VT) and VF account for the majority of sudden cardiac deaths worldwide. Treatments for VT/VF include anti-arrhythmic drugs, ICDs and catheter ablation, but these treatments vary in effectiveness and carry substantial risks and/or expense. Current methods of selecting patients for ICD implantation are imprecise and fail to identify some at-risk patients, while leading to others being overtreated. In this article, the authors discuss the current role and future direction of cardiac MRI (CMRI) in refining diagnosis and personalising ventricular arrhythmia management. The capability of CMRI with gadolinium contrast delayed-enhancement patterns and, more recently, T1 mapping to determine the aetiology of patients presenting with heart failure is well established. Although CMRI imaging in patients with ICDs can be challenging, recent technical developments have started to overcome this. CMRI can contribute to risk stratification, with precise and reproducible assessment of ejection fraction, quantification of scar and 'border zone' volumes, and other indices. Detailed tissue characterisation has begun to enable creation of personalised computer models to predict an individual patient's arrhythmia risk. When patients require VT ablation, a substrate-based approach is frequently employed as haemodynamic instability may limit electrophysiological activation mapping. Beyond accurate localisation of substrate, CMRI could be used to predict the location of re-entrant circuits within the scar to guide ablation.

Nonbinary quantification technique accounting for myocardial infarct heterogeneity: Feasibility of applying percent infarct mapping in patients

BACKGROUND

Binary threshold-based quantification techniques ignore myocardial infarct (MI) heterogeneity, yielding substantial misquantification of MI.

PURPOSE

To assess the technical feasibility of MI quantification using percent infarct mapping (PIM), a prototype nonbinary algorithm, in patients with suspected MI.

STUDY TYPE

Prospective cohort POPULATION: Patients (n = 171) with suspected MI referred for cardiac MRI.

FIELD STRENGTH/SEQUENCE

Inversion recovery balanced steady-state free-precession for late gadolinium enhancement (LGE) and modified Look-Locker inversion recovery (MOLLI) T1 -mapping on a 1.5T system.

ASSESSMENT

Infarct volume (IV) and infarct fraction (IF) were quantified by two observers based on manual delineation, binary approaches (2-5 standard deviations [SD] and full-width at half-maximum [FWHM] thresholds) in LGE images, and by applying the PIM algorithm in T1 and LGE images (PIMT1 ; PIMLGE ).

STATISTICAL TEST

IV and IF were analyzed using repeated measures analysis of variance (ANOVA). Agreement between the approaches was determined with Bland-Altman analysis. Interobserver agreement was assessed by intraclass correlation coefficient (ICC) analysis.

RESULTS

MI was observed in 89 (54.9%) patients, and 185 (38%) short-axis slices. IF with 2, 3, 4, 5SDs and FWHM techniques were 15.7 ± 6.6, 13.4 ± 5.6, 11.6 ± 5.0, 10.8 ± 5.2, and 10.0 ± 5.2%, respectively. The 5SD and FWHM techniques had the best agreement with manual IF (9.9 ± 4.8%) determination (bias 1.0 and 0.2%; P = 0.1426 and P = 0.8094, respectively). The 2SD and 3SD algorithms significantly overestimated manual IF (9.9 ± 4.8%; both P < 0.0001). PIMLGE measured significantly lower IF (7.8 ± 3.7%) compared to manual values (P < 0.0001). PIMLGE , however, showed the best agreement with the PIMT1 reference (7.6 ± 3.6%, P = 0.3156). Interobserver agreement was rated good to excellent for IV (ICCs between 0.727-0.820) and fair to good for IF (0.589-0.736).

DATA CONCLUSION

The application of the PIMLGE technique for MI quantification in patients is feasible. PIMLGE , with its ability to account for voxelwise MI content, provides significantly smaller IF than any thresholding technique and shows excellent agreement with the T1 -based reference.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.