Digital-heart identification of fat-based ablation targeting (DIFAT) overlapped with clinical ablations in 79% of patients, providing significantly smaller lesions.
Observational
Does DIFAT technology accurately predict VT ablation targets compared to clinical ablation data in patients with ischemic cardiomyopathy?
DIFAT technology using contrast-enhanced CT accurately predicts VT ablation targets and may allow for smaller lesions and fewer redo procedures in patients with ischemic cardiomyopathy.
p-value: p=<0.0005
BACKGROUND: Infiltrating adipose tissue (inFAT) is a newly recognized proarrhythmic substrate for postinfarct ventricular tachycardias (VT) identifiable on contrast-enhanced computed tomography. This study presents novel digital-heart technology that incorporates inFAT from contrast-enhanced computed tomography to noninvasively predict VT ablation targets and assesses the capability of the technology by comparing its predictions with VT ablation procedure data from patients with ischemic cardiomyopathy. METHODS: Digital-heart models reflecting patient-specific inFAT distributions were reconstructed from contrast-enhanced computed tomography. The digital-heart identification of fat-based ablation targeting (DIFAT) technology evaluated the rapid-pacing-induced VTs in each personalized inFAT-based substrate. DIFAT targets that render the inFAT substrate noninducible to VT, including VTs that arise postablation, were determined. DIFAT predictions were compared with corresponding clinical ablations to assess the capabilities of the technology. RESULTS: <0.0005). DIFAT targets overlapped with clinical ablations in 79% of patients, mostly in the apex (72%) and inferior/inferolateral (74%). In 3 patients, DIFAT targets colocalized with redo ablations delivered years after the index procedure. CONCLUSIONS: DIFAT is a novel digital-heart technology for individualized VT ablation guidance designed to eliminate VT inducibility following initial ablation. DIFAT predictions colocalized well with clinical ablation locations but provided significantly smaller lesions. DIFAT also predicted VTs targeted in redo procedures years later. As DIFAT uses widely accessible computed tomography, its integration into clinical workflows may augment therapeutic precision and reduce redo procedures.
Sung et al. (Tue,) conducted a observational in Ischemic cardiomyopathy with postinfarct ventricular tachycardia. Digital-heart identification of fat-based ablation targeting (DIFAT) vs. Clinical ablation procedure data was evaluated on Overlap of DIFAT targets with clinical ablations (p=<0.0005). Digital-heart identification of fat-based ablation targeting (DIFAT) overlapped with clinical ablations in 79% of patients, providing significantly smaller lesions.
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