Key result
CT-based simulation successfully replicates clinically mapped VT circuits in ~60% of patients.
Why the study?
Electrophysiological mapping of VT is tedious and poorly reproducible, and substrate analysis on imaging cannot explicitly display VT circuits.
Can a computed tomography-based model personalization approach accurately simulate postinfarction VT circuits compared to clinical electroanatomical mapping?
Can a computed tomography-based model personalization approach accurately simulate postinfarction VT circuits compared to clinical electroanatomical mapping?
A novel CT-based computational model can efficiently simulate patient-specific postinfarction VT circuits that match clinical electroanatomical maps.
BACKGROUND: Electrophysiological mapping of ventricular tachycardia (VT) is tedious and poorly reproducible. Substrate analysis on imaging cannot explicitly display VT circuits. OBJECTIVES: This study sought to introduce a computed tomography-based model personalization approach, allowing for the simulation of postinfarction VT in a clinically compatible time frame. METHODS: In 10 patients (age 65 ± 11 years, 9 male) referred for post-VT ablation, computed tomography-derived wall thickness maps were registered to 25 electroanatomical maps (sinus rhythm, paced, and VT). The relationship between wall thickness and electrophysiological characteristics (activation-recovery interval) was analyzed. Wall thickness was then employed to parameterize a fast and tractable organ-scale wave propagation model. Pacing protocols were simulated from multiple sites to test VT induction in silico. In silico VTs were compared to VT circuits mapped clinically. RESULTS: Clinically, 6 different VTs could be induced with detailed maps in 9 patients. The proposed model allowed for fast simulation (median: 6 min/pacing site). Simulations of steady pacing (600 milliseconds) from 100 different sites/patient never triggered any arrhythmia. Applying S1-S2 or S1-S2-S3 induction schemes allowed for the induction of in silico VTs in the 9 of 10 patients who were clinically inducible. The patient who was not inducible clinically was also noninducible in silico. A total of 42 different VTs were simulated (4.2 ± 2 per patient). Six in silico VTs matched a VT circuit mapped clinically. CONCLUSIONS: The proposed framework allows for personalized simulations in a matter of hours. In 6 of 9 patients, simulations show re-entrant patterns matching intracardiac recordings.
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Cedilnik et al. (2023) studied postinfarction ventricular tachycardia (n=10). Computed tomography-based model personalization approach vs. Clinical electroanatomical mapping was evaluated on Induction of in silico VTs matching clinically mapped VT circuits.
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