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

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.

Heart failure: an update from the last years and a look at the near future

“…Machine learning procedures have been successfully used to guide diagnosis, 78 therapeutic strategies, 79 and prognosis 80 . Using a machine learning algorithm, Adler et al .…”

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

“…[17][18][19] Therefore, investigating the prognosis of patients with LVNC has significant clinical implications. Many studies have been conducted to predict the risk of MACE in patients with LVNC using the thickness of non-compacted myocardium and LV trabeculated mass, atrial size, LVEF, LGE, brain natriuretic peptide, and genes 3,7,[19][20][21][22][23] with LVEF and LGE being the most commonly used predictors. 3,24 Howev-er, Yu et al 25 found that even patients with LVEF-preserved LVNC could have impaired LV systolic function and were at risk for MACE.…”

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

“…ML techniques have been implemented for improving risk stratification in these patients. Specifically, echocardiographic and CMR data were analyzed using ML algorithms to identify predictors of adverse events in non-compaction cardiomyopathy patients [ 37 ]. The combination of CMR-derived left ventricular ejection fraction, CMR-derived right ventricular end systolic volume, echocardiogram-derived right ventricular systolic dysfunction and CMR-derived right ventricular lower diameter was found to achieve the better performance in predicting major adverse events in these patients [ 37 ].…”