Journal
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY
Volume 13, Issue 4, Pages -Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1161/CIRCEP.119.007975
Keywords
cardiomyopathy; machine learning; magnetic resonance imaging; sudden cardiac death
Categories
Funding
- National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute [R01HL103812]
- NIH Predoctoral Training Program in Computational Medicine [T32GM119998]
- NIH [DP1-HL123271, R01-HL126802, R01-HL142893, R01-HL142496]
- Leducq Foundation
- Robert E. Meyerhoff Assistant Professorship
- Johns Hopkins University Discovery Awards Program
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Background: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-dimensional spatial complexity of grayscale patterns on late gadolinium enhanced cardiac magnetic resonance images to predict VAs in patients with ischemic cardiomyopathy. Methods: One hundred twenty-two consecutive ischemic cardiomyopathy patients with left ventricular ejection fraction <= 35% without prior history of VAs underwent late gadolinium enhanced cardiac magnetic resonance images. From raw grayscale data, we generated graphs encoding the 3-dimensional geometry of the left ventricle. A novel technique, adapted to these graphs, assessed global regularity of signal intensity patterns using Fourier-like analysis and generated a substrate spatial complexity profile for each patient. A machine learning statistical algorithm was employed to discern which substrate spatial complexity profiles correlated with VA events (appropriate implantable cardioverter-defibrillator firings and arrhythmic sudden cardiac death) at 5 years of follow-up. From the statistical machine learning results, a complexity score ranging from 0 to 1 was calculated for each patient and tested using multivariable Cox regression models. Results: At 5 years of follow-up, 40 patients had VA events. The machine learning algorithm classified with 81% overall accuracy and correctly classified 86% of those without VAs. Overall negative predictive value was 91%. Average complexity score was significantly higher in patients with VA events versus those without (0.5 +/- 0.5 versus 0.1 +/- 0.2; P<0.0001) and was independently associated with VA events in a multivariable model (hazard ratio, 1.5 [1.2-2.0]; P=0.002). Conclusions: Substrate spatial complexity analysis of late gadolinium enhanced cardiac magnetic resonance images may be helpful in refining VA risk in patients with ischemic cardiomyopathy, particularly to identify low-risk patients who may not benefit from prophylactic implantable cardioverter-defibrillator therapy.
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