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.
['Rocon|Camila|C|', 'Tabassian|Mahdi|M|', 'Tavares de Melo|Marcelo Dantas|MD|', 'de Araujo Filho|Jose Arimateia|JA|', 'Grupi|Cesar José|CJ|', 'Parga Filho|Jose Rodrigues|JR|', 'Bocchi|Edimar Alcides|EA|', "D'hooge|Jan|J|", 'Salemi|Vera Maria Cury|VMC|']
D009202:Cardiomyopathies D003287:Contrast Media D005260:Female D005682:Gadolinium D006801:Humans D000069550:Machine Learning D008297:Male D012189:Retrospective Studies
Non-compaction Cardiomyopathy Machine learning Magnetic resonance imaging Follow-up Echocardiography