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

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.

Figures

Figure 1.

Substrate Spatial Complexity Analysis Workflow. Signal intensity patterns from LGE-CMR imaging were analyzed using a Fourier-like technique for assessment of global irregularity. This analysis generated features that were used in a statistical machine learning algorithm, which yielded a complexity score ranging from 0 to 1, where 0 represents low arrhythmic risk and 1 represents high arrhythmic risk.

Figure 2.

Fourier-Like Analysis. Signal intensity patterns from LGE-CMR imaging were analyzed using a novel Fourier-like technique. Eigenvectors (sine-like functions) of varying frequencies oscillating over graphs encoding patient-specific LV size and geometry were compared with signal intensity patterns to generate Fourier coefficients. These coefficients were then used in the ML algorithm. Each panel (A, B and C) shows a different eigenvector frequency. Colors represent amplitude of the sine-like function.

Figure 3.

Results of Multivariable Cox Regression Model. In a multivariable Cox regression analysis, the CS was independently associated with VA events on a time-to-event basis and showed a stronger association than any other variable.

Figure 4.

Example SCC analysis from 2 patients. Panel A shows the LGE-CMR-derived scar pattern from a patient with a low scar burden (12.1 g) but a high CS (0.99) who ultimately had a VA event. Panel B shows the LGE-CMR-derived scar pattern from a patient with a high scar burden (84 g) but a low CS (0.00) who did not have a VA event.