Does a two-step precision risk prediction technology using MRI/PET fusion and a supervised classifier improve prediction of sudden cardiac death in patients with cardiac sarcoidosis?
A novel personalized MRI and PET fusion mechanistic model combined with a supervised classifier showed promising performance (AUC 0.754) for predicting sudden cardiac death risk in cardiac sarcoidosis, outperforming standard clinical metrics.
Cardiac sarcoidosis (CS), an inflammatory disease characterized by formation of granulomas in the heart, is associated with high risk of sudden cardiac death (SCD) from ventricular arrhythmias. Current "one-size-fits-all" guidelines for SCD risk assessment in CS result in insufficient appropriate primary prevention. Here, we present a two-step precision risk prediction technology for patients with CS. First, a patient's arrhythmogenic propensity arising from heterogeneous CS-induced ventricular remodeling is assessed using a novel personalized magnetic-resonance imaging and positron-emission tomography fusion mechanistic model. The resulting simulations of arrhythmogenesis are fed, together with a set of imaging and clinical biomarkers, into a supervised classifier. In a retrospective study of 45 patients, the technology achieved testing results of 60% sensitivity 95% confidence interval (CI): 57-63%, 72% specificity 95% CI: 70-74%, and 0.754 area under the receiver operating characteristic curve 95% CI: 0.710-0.797. It outperformed clinical metrics, highlighting its potential to transform CS risk stratification.
Shade et al. (Wed,) studied this question.