A machine learning model combining four biventricular imaging parameters predicted major adverse cardiovascular events in LVNC patients with an accuracy of 75.5%.
Cohort (n=108)
No
Does machine learning analysis of biventricular imaging markers predict major adverse cardiovascular events in patients with left ventricular non-compaction cardiomyopathy?
Machine learning analysis of biventricular imaging parameters, particularly incorporating right ventricular metrics, can effectively predict major adverse cardiovascular events in patients with left ventricular non-compaction cardiomyopathy.
Effect estimate: Accuracy 75.5%
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 et al. (Tue,) conducted a cohort in Left ventricular non-compaction cardiomyopathy (LVNC) (n=108). Machine learning analysis of echocardiographic and CMRI parameters was evaluated on Major adverse cardiovascular events (MACEs) (Accuracy 75.5%). A machine learning model combining four biventricular imaging parameters predicted major adverse cardiovascular events in LVNC patients with an accuracy of 75.5%.
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