Pediatric/Adult Congenital Heart Disease -> Pediatric Cardiology D-MP08 - Pediatric and Arrhythmia & Device Management (ID 12) Moderated ePoster

D-MP08-05 - Incorporation Of Native T1 Maps Improves Accuracy Of Cardiac MRI-based Virtual Heart Arrhythmia Risk Prediction In Pediatric Myocarditis (ID 819)

Disclosure
 A.N. Doshi: Nothing relevant to disclose.
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Abstract

Background: Clinical criteria alone cannot identify pediatric myocarditis patients at increased risk of ventricular arrhythmia (VA). Late gadolinium enhancement (LGE) MRI-based virtual heart arrhythmia risk prediction (VARP) can assess VA risk in ischemic cardiomyopathy and has potential for VA risk assessment in myocarditis. MRI native T1 maps provide tissue characterization that may improve VARP.
Objective: Evaluate use of VARP with incorporation of native T1 maps for VA prediction in pediatric myocarditis.
Methods: In this retrospective study of 9 myocarditis patients (age 11.1 ± 6.6 years), 4 had suffered VA (sustained VT or VF) during index hospitalization. All had undergone routine 1.5 T MRI, including native T1 map acquired in a midventricular short axis slice. For each patient, 2 models were created: An LGE-based model with EP properties based on LGE signal intensity, and a T1-adjusted model with LGE signal intensities shifted based on the patient’s mean T1 value relative to a reference T1 value. Programmed virtual pacing from 26 sites throughout the LV and RV was used to assess inducibility of sustained reentry in all models.
Results: There was no significant difference between VA+ and VA- patients in LV EF (45.5±11.5 vs 56.7±7.6%), RV EF (48.4±10.6 vs 53.2±9.7%), or peak troponin (12.1±20 vs 8±4.1 ng/mL). LGE-based models correctly predicted clinical outcome in 3/9 patients (0/4 VA+ and 3/5 VA-, accuracy 0.33). T1-adjusted models correctly predicted clinical outcome in 6/9 patients (2/4 VA+ and 4/5 VA-, accuracy 0.67).
Conclusion: In the absence of clinical predictors for VA, VARP with incorporation of native T1 mapping has potential for VA risk prediction in pediatric myocarditis.
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