Open AccessPredicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier
Author(s) -
Julie K. Shade,
Adityo Prakosa,
Dan Popescu,
Rebecca Yu,
David R. Okada,
Jonathan Chrispin,
Natalia A. Trayanova
Publication year - 2021
Publication title -
science advances
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.928
H-Index - 146
ISSN - 2375-2548
DOI - 10.1126/sciadv.abi8020
Subject(s) - medicine , sudden cardiac death , cardiology , confidence interval , magnetic resonance imaging , receiver operating characteristic , positron emission tomography , cardiac magnetic resonance imaging , risk stratification , cardiac pet , retrospective cohort study , cardiac magnetic resonance , radiology
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.