Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy

David R. Okada, Jason Miller, Jonathan Chrispin, Adityo Prakosa, Natalia Trayanova, Steven Jones, Mauro Maggioni, Katherine C. Wu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)E007975
JournalCirculation: Arrhythmia and Electrophysiology
Issue number4
StatePublished - Apr 1 2020


  • cardiomyopathy
  • machine learning
  • magnetic resonance imaging
  • sudden cardiac death

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)


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