Po-01-073 tracking changes in af cycle length and complexity: validation of a signal complexity algorithm

For patients with persistent atrial fibrillation (AF), several ablation strategies in addition to pulmonary vein isolation (PVI) have been proposed. While some strategies may aim to achieve termination of AF by ablation, no standardized analytics exist to quantify and visualize change to AF rhythm throughout the procedure. This study explored a new signal complexity algorithm for continuous analysis and visualization of signal parameters using data collected during ablation of persistent AF (PsAF). Data was collected from patients diagnosed with PsAF and scheduled for ablation at NYU Langone Medical Center between July and November 2022. The ablation procedure consisted of a 3D navigation guided wide area circumferential ablation followed by an inferior posterior line and a roof line to complete isolation of the posterior wall of the left atrium. After DC cardioversion, additional ablation based on electrogram voltage and induction techniques was performed. Intracardiac signals from the CS catheter were recorded simultaneously on the Claris EP recording system (Abbott, USA) and a novel EP Recording System (ECGenius System, CathVision, Denmark). Timestamps for crucial procedure steps, including completion of PVI and anatomical lines, were noted in the ECGenius System event log. During each step of the procedure, the operator was asked to note any visual changes to the AF rhythm. Data was analyzed off-line using a proprietary Signal Complexity algorithm to calculate signal cycle length, variability, fractionation, and dominant frequency. Values were calculated for distal and proximal CS electrodes and visualized as a color map. The study enrolled 12 patients (5 females) with a mean age of 68.1 years (SD = 12.2). A mean of 42 minutes of AF electrogram recordings were analyzed per patient. The Signal Complexity algorithm was able to quantify the baseline variability of each measured parameter and in 10 patients one or more post-ablation parameters were significantly different (p < 0.01, see fig. 1). On five occasions, these changes in rhythm were not detected by the operator. This study showed that a Signal Complexity algorithm is capable of continuous analysis of AF rhythm parameters and visualize them in a format that may help the operator to see changes in rhythm related to ablation strategy that were otherwise unnoticed. Whilst changes coincided with ablation procedure steps, their clinical and procedural relevance remain to be determined.