LAUSR

BACKGROUND Antitachycardia pacing (ATP) success rates have been reported as low as 50% for fast VTs, providing an opportunity for improved ATP to decrease shocks. A new self-adapting ATP algorithm, AATP, was compared to traditional Burst ATP using computer modeling to conduct a virtual study. OBJECTIVE The purpose of the modeling study was to understand how a new automated ATP (AATP) therapy would perform compared with traditional burst ATP. METHODS Virtual patient scenarios were constructed from MRI and electrophysiology (EP) data. Cardiac EP simulation software (CARPEntry) was used to generate reentrant VT. Simulated VTs exit site were physician adjudicated against corresponding clinical 12 lead ECGs. Burst ATP was comprised of 3 sequences of 8 pulses at 88% of VT cycle length, each decremented by 10ms. AATP was limited to 3 sequences with each sequence learning from the previous sequences. RESULTS Two hundred and fifty-nine unique ATP scenarios were generated from 7 unique scared hearts. Burst ATP terminated 145/259 VTs (56%) and accelerated 2.0%. AATP terminated 189/259 VTs (73%) with the same acceleration rate. The two dominant ATP failure mechanisms were identified as 1) insufficient prematurity to close the excitable gap, and 2) failure to reach the critical isthmus of the VT. AATP reduced failures in these categories from 101 to 63 (44% reduction) without increasing acceleration. CONCLUSION AATP successfully adapted ATP sequences to terminate VT episodes that Burst ATP failed to terminate. AATP was successful with complex scar geometries and electrophysiology heterogeneity as seen in the real world.