Noncompaction cardiomyopathy: a substrate for a thromboembolic event.

BACKGROUND: Noncompaction cardiomyopathy (NCC) is a rare genetic cardiomyopathy characterized by a thin, compacted epicardial layer and an extensive noncompacted endocardial layer. The clinical manifestations of this disease include ventricular arrhythmia, heart failure, and systemic thromboembolism. CASE PRESENTATION: A 43-year-old male was anticoagulated by pulmonary thromboembolism for 1 year when he developed progressive dyspnea. Cardiovascular magnetic resonance imaging showed severe biventricular trabeculation with an ejection fraction of 15%, ratio of maximum noncompacted/compacted diastolic myocardial thickness of 3.2 and the presence of exuberant biventricular apical thrombus. CONCLUSION: Still under discussion is the issue of which patients and when they should be anticoagulated. It is generally recommended to those presenting ventricular systolic dysfunction, antecedent of systemic embolism, presence of cardiac thrombus and atrial fibrillation. In clinical practice the patients with NCC and ventricular dysfunction have been given oral anticoagulation, although there are no clinical trials showing the real safety and benefit of this treatment.

A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.

AIMS: Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathophysiological mechanisms. We propose to evaluate echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based model. METHODS AND RESULTS: Forty-nine outpatients with NCC diagnosis by echocardiography and magnetic resonance imaging (21 men, 42.8±14.8 years) were included. A comprehensive echocardiogram was performed. The layer-specific strain was analyzed from the apical two-, three, four-chamber views, short axis, and focused right ventricle views using 2D echocardiography (2DE) software. RBR was present in 44.9% of patients, and this group presented increased LV mass indexed (118±43.4 vs. 94.1±27.1g/m2, P = 0.034), LV end-diastolic and end-systolic volumes (P< 0.001), E/e' (12.2±8.68 vs. 7.69±3.13, P = 0.034), and decreased LV ejection fraction (40.7±8.71 vs. 58.9±8.76%, P < 0.001) when compared to patients without RBR. Also, patients with RBR presented a significant decrease of global longitudinal, radial, and circumferential strain. When ML model based on a random forest algorithm and a neural network model was applied, it found that twist, NC/C, torsion, LV ejection fraction, and diastolic dysfunction are the strongest predictors to RBR with accuracy, sensitivity, specificity, area under the curve of 0.93, 0.99, 0.80, and 0.88, respectively. CONCLUSION: In this study, a random forest algorithm was capable of selecting the best echocardiographic predictors to RBR pattern in NCC patients, which was consistent with worse systolic, diastolic, and myocardium deformation indices. Prospective studies are warranted to evaluate the role of this tool for NCC risk stratification.

Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study.

AIMS: Left ventricular non-compaction cardiomyopathy (LVNC) is a genetic heart disease, with heart failure, arrhythmias, and embolic events as main clinical manifestations. The goal of this study was to analyse a large set of echocardiographic (echo) and cardiac magnetic resonance imaging (CMRI) parameters using machine learning (ML) techniques to find imaging predictors of clinical outcomes in a long-term follow-up of LVNC patients. METHODS AND RESULTS: Patients with echo and/or CMRI criteria of LVNC, followed from January 2011 to December 2017 in the heart failure section of a tertiary referral cardiologic hospital, were enrolled in a retrospective study. Two-dimensional colour Doppler echocardiography and subsequent CMRI were carried out. Twenty-four hour Holter monitoring was also performed in all patients. Death, cardiac transplantation, heart failure hospitalization, aborted sudden cardiac death, complex ventricular arrhythmias (sustained and non-sustained ventricular tachycardia), and embolisms (i.e. stroke, pulmonary thromboembolism and/or peripheral arterial embolism) were registered and were referred to as major adverse cardiovascular events (MACEs) in this study. Recruited for the study were 108 LVNC patients, aged 38.3 ± 15.5 years, 48.1% men, diagnosed by echo and CMRI criteria. They were followed for 5.8 ± 3.9 years, and MACEs were registered. CMRI and echo parameters were analysed via a supervised ML methodology. Forty-seven (43.5%) patients had at least one MACE. The best performance of imaging variables was achieved by combining four parameters: left ventricular (LV) ejection fraction (by CMRI), right ventricular (RV) end-systolic volume (by CMRI), RV systolic dysfunction (by echo), and RV lower diameter (by CMRI) with accuracy, sensitivity, and specificity rates of 75.5%, 77%, 75%, respectively. CONCLUSIONS: Our findings show the importance of biventricular assessment to detect the severity of this cardiomyopathy and to plan for early clinical intervention. In addition, this study shows that even patients with normal LV function and negative late gadolinium enhancement had MACE. ML is a promising tool for analysing a large set of parameters to stratify and predict prognosis in LVNC patients.

Decreased glycolytic metabolism in non-compaction cardiomyopathy by 18F-fluoro-2-deoxyglucose positron emission tomography: new insights into pathophysiological mechanisms and clinical implications.

AIMS: The pathophysiological mechanisms of left ventricular non-compaction cardiomyopathy (LVNC) remain controversial. This study performed combined 18F-fluoro-2-deoxyglucose dynamic positron emission tomography (FDG-PET) and 99mTc-sestamibi single-photon emission computed tomography (SPECT) studies to evaluate myocardial glucose metabolism and perfusion in patients with LVNC and their clinical implications. METHODS AND RESULTS: Thirty patients (41 ± 12 years, 53% male) with LVNC, diagnosed by cardiovascular magnetic resonance (CMR) criteria, and eight age-matched healthy controls (42 ± 12 years, 50% male) were prospectively recruited to undergo FDG-PET with measurement of the myocardial glucose uptake rate (MGU) and SPECT to investigate perfusion-metabolism patterns. Patients with LVNC had lower global MGU compared with that in controls (36.9 ± 8.8 vs. 44.6 ± 5.4 µmol/min/100 g, respectively, P = 0.02). Of 17 LV segments, MGU levels were significantly reduced in 8, and also a reduction was observed when compacted segments from LVNC were compared with the segments from control subjects (P < 0.001). Perfusion defects were also found in 15 (50%) patients (45 LV segments: 64.4% match, and 35.6% mismatch perfusion-metabolism pattern). Univariate and multivariate analyses showed that beta-blocker therapy was associated with increased MGU (beta coefficient = 10.1, P = 0.008). Moreover, a gradual increase occurred in MGU across the beta-blocker dose groups (P for trend = 0.01). CONCLUSION: The reduction of MGU documented by FDG-PET in LVNC supports the hypothesis that a cellular metabolic pathway may play a role in the pathophysiology of LVNC. The beneficial effect of beta-blocker mediating myocardial metabolism in the clinical course of LVNC requires further investigation.