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

Circ Arrhythm Electrophysiol. 2020 Apr;13(4):e007975. doi: 10.1161/CIRCEP.119.007975. Epub 2020 Mar 18.

Abstract

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. Visual Overview: A visual overview is available for this article.

Keywords: cardiomyopathy; machine learning; magnetic resonance imaging; sudden cardiac death.

Publication types

  • Multicenter Study
  • Observational Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials
  • Aged
  • Arrhythmias, Cardiac / diagnosis
  • Arrhythmias, Cardiac / etiology*
  • Arrhythmias, Cardiac / physiopathology
  • Cardiomyopathies / complications
  • Cardiomyopathies / diagnostic imaging*
  • Cardiomyopathies / physiopathology
  • Contrast Media / administration & dosage
  • Death, Sudden, Cardiac / etiology
  • Diagnosis, Computer-Assisted*
  • Female
  • Fourier Analysis
  • Gadolinium DTPA / administration & dosage
  • Heart Rate
  • Humans
  • Imaging, Three-Dimensional
  • Machine Learning*
  • Magnetic Resonance Imaging*
  • Male
  • Middle Aged
  • Myocardial Ischemia / complications*
  • Myocardial Ischemia / diagnostic imaging
  • Myocardial Ischemia / physiopathology
  • Predictive Value of Tests
  • Prognosis
  • Registries
  • Retrospective Studies
  • Risk Assessment
  • Risk Factors
  • Stroke Volume
  • United States
  • Ventricular Function, Left

Substances

  • Contrast Media
  • gadodiamide
  • Gadolinium DTPA