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Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death
Eric Xie1, Eric Sung1,2, Elie Saad1
1Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Insights
Sudden cardiac death (SCD) risk stratification is improving with advanced cardiac imaging. Machine learning combined with techniques like cardiac magnetic resonance imaging (CMR) enhances prediction of fatal arrhythmias.
Area of Science:
- Cardiology
- Medical Imaging
- Computational Biology
Background:
- Sudden cardiac death (SCD) accounts for about half of cardiovascular disease deaths.
- Ischemic cardiomyopathy (ICM) is the primary cause of SCD in the US, but non-ischemic cardiomyopathy (NICM) also contributes, particularly in younger patients.
- Ejection fraction (EF) via echocardiography is the conventional method for implantable cardiac defibrillator (ICD) candidacy.
Purpose of the Study:
- To explore advanced cardiac imaging techniques for SCD risk stratification.
- To evaluate the role of myocardial substrate characterization in predicting SCD.
- To investigate the potential of machine learning (ML) in improving SCD risk prediction.
Main Methods:
- Utilizing advanced imaging like cardiac magnetic resonance imaging (CMR), positron emission tomography (PET), single-photon emission computerized tomography (SPECT), and computed tomography (CT).
- Assessing myocardial scar (late gadolinium enhancement on CMR), viability, innervation, inflammation (PET/SPECT), and fat (CT).
- Developing electrophysiologic modeling and integrating ML algorithms with imaging data.
Main Results:
- Advanced imaging methods non-invasively characterize myocardial substrates linked to SCD.
- CMR-detected scar burden, region, and heterogeneity inform risk stratification.
- PET, SPECT, and CT provide insights into myocardial viability, innervation, inflammation, and fat content.
Conclusions:
- Advanced cardiac imaging is becoming a well-established tool for SCD risk stratification.
- These techniques offer a more detailed understanding of arrhythmogenic substrates than EF alone.
- The integration of ML with advanced imaging holds significant promise for enhancing predictive accuracy and clinical decision-making in SCD prevention.
Abstract:
Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
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