A machine learning-derived substrate spatial complexity score from cardiac MRI was independently associated with ventricular arrhythmia events at 5 years (HR 1.5; 95% CI 1.2-2.0; P=0.002).
Cohort (n=122)
Effect estimate: HR 1.5 (95% CI 1.2-2.0)
p-value: p=0.002
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.
Okada et al. (Thu,) conducted a cohort in ischemic cardiomyopathy (n=122). Substrate spatial complexity analysis of late gadolinium enhanced cardiac magnetic resonance images was evaluated on Ventricular arrhythmia events (appropriate implantable cardioverter-defibrillator firings and arrhythmic sudden cardiac death) (HR 1.5, 95% CI 1.2-2.0, p=0.002). A machine learning-derived substrate spatial complexity score from cardiac MRI was independently associated with ventricular arrhythmia events at 5 years (HR 1.5; 95% CI 1.2-2.0; P=0.002).