Novel Approach to Risk Stratification in Left Ventricular Non-Compaction Using A Combined Cardiac Imaging and Plasma Biomarker Approach

Advances and challenges in the diagnosis and management of left ventricular noncompaction in adults: A literature review

In the realm of cardiovascular health, isolated left ventricular noncompaction (LVNC) stands out for its distinct morphological features and the clinical challenges it presents, particularly in adults. This literature review explores the intricacies of LVNC, aiming to unravel its epidemiological spread, diagnostic hurdles, and therapeutic strategies. Despite technological advancements in cardiac imaging that have improved the recognition of LVNC, a significant gap persists alongside a fragmented understanding of its pathogenesis. The studies scrutinized reveal a broad spectrum of prevalence rates influenced by diverse diagnostic tools and demographic variables. This variation underscores the complexity of accurately identifying LVNC and the resultant implications for clinical management. The review succinctly addresses the need for precise guidelines to navigate the diagnosis of LVNC and outlines the imperative for tailored clinical management approaches that cater to the wide array of patient presentations, from asymptomatic cases to those with severe cardiac dysfunction. By highlighting the critical gaps in current literature-namely the absence of standardized diagnostic criteria and a comprehensive pathogenic model-the review sets the stage for future research directions. These endeavors are essential for enhancing diagnostic accuracy, refining management protocols, and ultimately improving patient outcomes in this complex subset of cardiomyopathy, thus contributing significantly to the advancement of cardiovascular medicine.

Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics

Background

Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC.

Methods

In total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared.

Results

The machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003).

Conclusions

The machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

Can left ventricular entropy by cardiac magnetic resonance late gadolinium enhancement be a prognostic predictor in patients with left ventricular non-compaction?

PURPOSE

Left ventricular non-compaction (LVNC) is considered rare; however, the use of cardiac magnetic resonance (CMR) has shown that its incidence is not uncommon, and its clinical presentation remains variable, with an uncertain prognosis. Risk stratification of major adverse cardiac events (MACE) in patients with LVNC remains complex. Therefore, this study aims to determine whether tissue heterogeneity from late gadolinium enhancement-derived entropy is associated with MACE in patients with LVNC.

METHODS

This study was registered in the Clinical Trial Registry (CTR2200062045). Consecutive patients who underwent CMR imaging and were diagnosed with LVNC were followed up for MACE, which was defined by heart failure, arrhythmias, systemic embolism, and cardiac death. The patients were divided into MACE and non-MACE groups. The CMR parameters included left ventricular (LV) entropy, LV ejection fraction (LVEF), LV end-diastolic volume, LV end-systolic volume (LVESV), and LV mass (LVM).

RESULTS

Eighty-six patients (age: 45.48 ± 16.64 years; female: 62.7%; LVEF: 42.58 ± 17.20%) were followed up for a median of 18 months and experienced 30 MACE events (34.9%). The MACE group showed higher LV entropy, LVESV, and LVM and lower LVEF than the non-MACE group. LV entropy [hazard ratio (HR): 1.710, 95% confidence interval (CI): 1.078-2.714, P = 0.023] and LVEF (HR: 0.961, 95% CI: 0.936-0.988, P = 0.004) were independent predictors of MACE (P <0.050) according to the Cox regression analysis. Receiver operating characteristic curve analysis revealed that the area under the curve of LV entropy was 0.789 (95% CI: 0.687-0.869, P < 0.001), LVEF was 0.804 (95% CI: 0.699-0.878, P < 0.001), and the combined model of LV entropy and LVEF was 0.845 (95% CI: 0.751-0.914, P < 0.050).

CONCLUSION

LGE-derived LV entropy and LVEF are independent risk indicators of MACE in patients with LVNC. The combination of the two factors was more conducive to improving the prediction of MACE.

Prognostic Value of Left Atrial Reservoir Strain in Left Ventricular Myocardial Noncompaction: A 3.0 T Cardiac Magnetic Resonance Feature Tracking Study

BACKGROUND

The relationship of left atrial (LA) strain to high-risk heart failure (HF) events in patients with left ventricular myocardial noncompaction (LVNC) remains to be thoroughly investigated.

PURPOSE

To evaluate the LA performance in patients with LVNC, and to investigate the prognostic value of LA phasic strain on high-risk HF events, and its influencing factors.

STUDY TYPE

Retrospective.

POPULATION

A total of 95 LVNC patients (74 with LA enlargement [LAE] and 21 without LAE) and 50 healthy controls.

FIELD STRENGTH/SEQUENCE

A 3.0 T, balanced steady-state free-precession cine imaging.

ASSESSMENT

LA longitudinal strains were measured by cardiac MRI feature tracking technique. LA volume index (LAVI) and LA ejection fraction (LAEF) were calculated. Their intraobserver and interobserver reproducibility were evaluated. The primary outcome was high-risk HF events, a composite of first HF hospitalization, hospitalization for worsening HF and death from HF.

STATISTICAL TESTS

Student's t/Mann-Whitney U, one-way analysis of variance/Kruskal-Wallis, Chi-squared, receiver operating characteristic, Kaplan-Meier, log-rank, Cox regression, Pearson and Spearman correlation and linear regression analyses were performed. The significance threshold was set at P < 0 .05.

RESULTS

LAEF and LA longitudinal strains decreased in LVNC patients irrespective of the presence of LAE. During a median follow-up of 32.17 months, high-risk HF occurred in 13 (13.68%) patients. Patients with increased LAVI, decreased LAEF and decreased LA longitudinal strain had significantly higher risks of high-risk HF events. In patients with LVNC, LA reservoir strain (εs) was independently associated with high-risk HF (hazard ratio = 23.208 [95% CI: 2.993-179.967]). LV global longitudinal strain (LV GLS) (β = -1.783 [95% CI: -2.493 to -1.073]) was significantly and independently associated with εs. Intraobserver and interobserver reproducibility was excellent for LAVI, LAEF, and LA strain.

CONCLUSION

In patients with LVNC, εs was an independent predictor for high-risk HF events. LV GLS was an independent determinant of εs in LVNC.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 4.

Three-dimensional Whole-Heart Cardiac MRI Sequence for Measuring Trabeculation in Left Ventricular Noncompaction

PURPOSE

To compare three-dimensional (3D) whole-heart MRI with isotropic submillimeter resolution with standard two-dimensional (2D) cine MRI in measuring the bilayered myocardium in left ventricular noncompaction (LVNC).

MATERIALS AND METHODS

Twenty-four patients with LVNC (mean age, 42 years ± 16 [SD]) were retrospectively enrolled between October 2011 and July 2020. Compacted myocardium (CM) and noncompacted myocardium (NCM) were measured in long axis (Petersen approach) and short axis (Jacquier approach) at 3D whole-heart and 2D cine MRI by two independent readers. Image quality (1 = excellent, 2 = adequate, 3 = nondiagnostic), considering discrimination between NCM and CM and CM and adjacent tissue, was evaluated. Pearson, Spearman, and intraclass correlation tests were used as statistical tests.

RESULTS

In long-axis measurements, the correlation between both sequences was moderate to strong for CM (Pearson, 0.66-0.79; Spearman, 0.61-0.68) and strong to very strong for NCM (Pearson, 0.90-0.97; Spearman, 0.77-0.91). Intraclass correlation coefficient (ICC) in 3D whole-heart MRI was 0.90 (95% CI: 0.78, 0.95) for CM and 0.94 (95% CI: 0.84, 0.97) for NCM, while ICC in 2D cine MRI was 0.77 (95% CI: 0.55, 0.89) for CM and 0.87 (95% CI: 0.72, 0.94) for NCM. Short-axis CM and NCM measurements had a strong to very strong correlation between both sequences (Pearson, 0.86-0.98; Spearman, 0.82-0.98). ICC in 3D whole-heart MRI was 0.96 (95% CI: 0.94, 0.99) for CM and 0.98 (95% CI: 0.97, 0.99) for NCM, while ICC in 2D cine MRI was 0.82 (95% CI: 0.63, 0.92) for CM and 0.87 (95% CI: 0.72, 0.94) for NCM. 3D whole-heart MRI demonstrated higher image quality than did 2D cine MRI (P < .001).

CONCLUSION

3D whole-heart MRI revealed higher image quality, with better structure discrimination and interobserver agreement in LVNC measurements, compared with standard 2D cine images.Keywords: MR Imaging, Cardiac, Cardiovascular Magnetic Resonance, Left Ventricular Noncompaction, Free-breathing Imaging Technique Supplemental material is available for this article. © RSNA, 2022See also the commentary by Jensen and Petersen in this issue.

Left ventricular noncompaction: a disease or a phenotypic trait?

Left ventricular noncompaction is a poorly defined and controversial entity, with wide phenotypic expression: from a simple anatomical trait to a disease with overt cardiac affection. Current diagnostic criteria rely exclusively on morphologic features of hypertrabeculation, which have low specificity for identifying true cardiomyopathy cases. The management of left ventricular noncompaction is also heterogeneous, and there are no dedicated clinical practice guidelines. The most common cardiovascular complications are heart failure, ventricular arrhythmias, and systemic embolisms. In this review, we discuss the diagnostic limitations of the available criteria, and propose a comprehensive alternative approach (including functional imaging variables, tissue characterization, genetics, and family screening) that may help in the differential diagnosis of hypertrabeculation cases. We also describe the genetic background of the disease and discuss the overlap with other cardiomyopathies. Finally, we focus on controversial issues in clinical management and suggest the use of the previously-mentioned variables for risk stratification and for individualization of patient follow-up.

Certainties and Uncertainties of Cardiac Magnetic Resonance Imaging in Athletes

Prolonged and intensive exercise induces remodeling of all four cardiac chambers, a physiological process which is coined as the “athlete’s heart”. This cardiac adaptation, however, shows overlapping features with non-ischemic cardiomyopathies, such as dilated, arrhythmogenic and hypertrophic cardiomyopathy, also associated with athlete’s sudden cardiac death. Cardiac magnetic resonance (CMR) is a well-suited, highly reproducible imaging modality that can help differentiate athlete’s heart from cardiomyopathy. CMR allows accurate characterization of the morphology and function of cardiac chambers, providing full coverage of the ventricles. Moreover, it permits an in-depth understanding of the myocardial changes through specific techniques such as mapping or late gadolinium enhancement. In this narrative review, we will focus on the certainties and uncertainties of the role of CMR in sports cardiology. The main aspects of physiological adaptation due to regular and intensive sports activity and the application of CMR in highly trained athletes will be summarized.

The role of multimodality imaging in patients with heart failure with reduced and preserved ejection fraction

PURPOSE OF REVIEW

The burden of clinical heart failure, both heart failure with a reduced ejection fraction (HFrEF) and with a preserved ejection fraction (HFpEF), continues to increase both nationally and globally. This review summarizes the expanding role of multimodality imaging techniques in the evaluation and management these patients.

RECENT FINDINGS

Echocardiographic assessment for heart failure continues to expand and should include a robust hemodynamic and strain assessment. Nuclear techniques have also continued to evolve and advances including computed tomography attenuation correction for single photon emission-computed tomography positron-emission tomography increase diagnostic accuracy as well as provide information such as myocardial blood flow and viability assessment. Computed tomography imaging, already well established in the assessment of coronary and valvular disease, has increasing utility in the characterization of myopathy, and cardiac magnetic resonance imaging (MRI) continues to expand its role in tissue characterization to a wider breadth of diseases, including right ventricular cardiomyopathy and left ventricle noncompaction.

SUMMARY

Although heart failure remains a clinical diagnosis based on history and examination, early imaging is critical for further assessment. Due to its widespread availability, affordability, and safety, transthoracic echocardiography has long been the mainstay tool for both initial evaluation as well as for periodic surveillance of heart failure patients, but advances in multimodality imaging are occurring at a rapid pace and promise to provide an increasing wealth of data to help manage such patients.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

Multimodality Cardiac Imaging in Cardiomyopathies: From Diagnosis to Prognosis

Cardiomyopathies are a group of structural and/or functional myocardial disorders which encompasses hypertrophic, dilated, arrhythmogenic, restrictive, and other cardiomyopathies. Multimodality cardiac imaging techniques are the cornerstone of cardiomyopathy diagnosis; transthoracic echocardiography should be the first-line imaging modality due to its availability, and diagnosis should be confirmed by cardiovascular magnetic resonance, which will provide more accurate morphologic and functional information, as well as extensive tissue characterization. Multimodality cardiac imaging techniques are also essential in assessing the prognosis of patients with cardiomyopathies; left ventricular ejection fraction and late gadolinium enhancement are two of the main variables used for risk stratification, and they are incorporated into clinical practice guidelines. Finally, periodic testing with cardiac imaging techniques should also be performed due to the evolving and progressive natural history of most cardiomyopathies.

Novel Approach to Risk Stratification in Left Ventricular Non-Compaction Using A Combined Cardiac Imaging and Plasma Biomarker Approach

Background

Left ventricular non‐compaction remains a poorly described entity, which has led to challenges of overdiagnosis. We aimed to evaluate if the presence of a thin compacted myocardial layer portends poorer outcomes in individuals meeting cardiac magnetic resonance criteria for left ventricular non‐compaction .

Methods and Results

This was an observational, retrospective cohort study involving individuals selected from the Cleveland Clinic Foundation cardiac magnetic resonance database (N=26 531). Between 2000 and 2018, 328 individuals ≥12 years, with left ventricular non‐compaction or excessive trabeculations based on the cardiac magnetic resonance Petersen criteria were included. The cohort comprised 42% women, mean age 43 years. We assessed the predictive ability of myocardial thinning for the primary composite end point of major adverse cardiac events (composite of all‐cause mortality, heart failure hospitalization, left ventricular assist device implantation/heart transplant, ventricular tachycardia, or ischemic stroke). At mean follow‐up of 3.1 years, major adverse cardiac events occurred in 102 (31%) patients. After adjusting for comorbidities, the risk of major adverse cardiac events was nearly doubled in the presence of significant compacted myocardial thinning (hazard ratio [HR], 1.88 [95% CI, 1.18‒3.00]; P=0.016), tripled in the presence of elevated plasma B‐type natriuretic peptide (HR, 3.29 [95% CI, 1.52‒7.11]; P=0.006), and increased by 5% for every 10‐unit increase in left ventricular end‐systolic volume (HR, 1.01 [95% CI, 1.00‒1.01]; P=0.041).

Conclusions

The risk of adverse clinical events is increased in the presence of significant compacted myocardial thinning, an elevated B‐type natriuretic peptide or increased left ventricular dimensions. The combination of these markers may enhance risk assessment to minimize left ventricular non‐compaction overdiagnosis whilst facilitating appropriate diagnoses in those with true disease.