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1.
ESC Heart Fail ; 7(5): 2431-2439, 2020 10.
Article in English | MEDLINE | ID: mdl-32608172

ABSTRACT

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.


Subject(s)
Cardiomyopathies , Contrast Media , Cardiomyopathies/diagnosis , Female , Gadolinium , Humans , Machine Learning , Male , Retrospective Studies
2.
BMC Cardiovasc Disord ; 15: 7, 2015 Jan 24.
Article in English | MEDLINE | ID: mdl-25618133

ABSTRACT

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.


Subject(s)
Arrhythmia, Sinus/etiology , Cardiomyopathies/complications , Cardiomyopathies/diagnosis , Coronary Thrombosis/etiology , Pulmonary Embolism/etiology , Ventricular Dysfunction/etiology , Adult , Arrhythmia, Sinus/diagnostic imaging , Coronary Angiography , Echocardiography , Heart/diagnostic imaging , Humans , Magnetic Resonance Angiography , Male , Myocardium/pathology , Tomography, X-Ray Computed
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