Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping.

BACKGROUND: Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings. METHODS: Temporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm2 resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier transform analysis into 28 407 total Fourier spectra. Thirty-five features for machine learning were extracted from each Fourier spectrum. RESULTS: Targeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation, including driver periphery electrodes, were added to driver center annotation. Notably, f1-score for the binary classification of higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04, P<0.05). The trained algorithm correctly highlighted 86% of driver regions with higher density but only 80% with lower-density MEM arrays (81% for lower-density+higher-density arrays together). CONCLUSIONS: The machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.

Implantation of subcutaneous defibrillator is feasible and safe with monitored anesthesia care.

BACKGROUND: The perioperative anesthesia care during subcutaneous implantable cardioverter-defibrillator (S-ICD) implantation is still evolving. OBJECTIVE: To assess the feasibility and safety of S-ICD implantation with monitored anesthesia care (MAC) versus general anesthesia (GA) in a tertiary care center. METHODS: This is a single-center retrospective study of patients undergoing S-ICD implantation between October 2012 and May 2019. Patients were categorized into MAC and GA group based on the mode of anesthesia. Procedural success without escalation to GA was the primary endpoint of the study, whereas intraprocedural hemodynamics, need of pharmacological support for hypotension and bradycardia, length of the procedure, stay in the post-anesthesia care unit, and postoperative pain were assessed as secondary endpoints. RESULTS: The study comprises 287 patients with MAC in 111 and GA in 176 patients. Compared to MAC, patients in GA group were younger and had a higher body mass index. All patients had successful S-ICD implantation. Only one patient (0.9%) in the MAC group was converted to GA. Despite a similar baseline heart rate (HR) and mean arterial blood pressure (MAP) in both groups, patients with GA had significantly lower HR and MAP during the procedure and more frequently required pharmacological hemodynamic support. Length of the procedure, stay in the postanesthesia care unit, and postoperative pain was similar in both groups. CONCLUSION: This retrospective experience suggests that implantation of S-ICD is feasible and safe with MAC. Use of GA is associated with more frequent administration of hemodynamic drugs during S-ICD implantation.

Human Atrial Fibrillation Drivers Resolved With Integrated Functional and Structural Imaging to Benefit Clinical Mapping.

OBJECTIVES: This study sought to improve atrial fibrillation (AF) driver identification by integrating clinical multielectrode mapping with driver fingerprints defined by high-resolution ex vivo 3-dimensional (3D) functional and structural imaging. BACKGROUND: Clinical multielectrode mapping of AF drivers suffers from variable contact, signal processing, and structural complexity within the 3D human atrial wall, raising questions on the validity of such drivers. METHODS: Sustained AF was mapped in coronary-perfused explanted human hearts (n = 11) with transmural near-infrared optical mapping (∼0.3 mm2 resolution). Simultaneously, custom FIRMap catheters (∼9 × 9 mm2 resolution) mapped endocardial and epicardial surfaces, which were analyzed by Focal Impulse and Rotor Mapping activation and Rotational Activity Profile (Abbott Labs, Chicago, Illinois). Functional maps were integrated with contrast-enhanced cardiac magnetic resonance imaging (∼0.1 mm3 resolution) analysis of 3D fibrosis architecture. RESULTS: During sustained AF, near-infrared optical mapping identified 1 to 2 intramural, spatially stable re-entrant AF drivers per heart. Driver targeted ablation affecting 2.2 ± 1.1% of the atrial surface terminated and prevented AF. Driver regions had significantly higher phase singularity density and dominant frequency than neighboring nondriver regions. Focal Impulse and Rotor Mapping had 80% sensitivity to near-infrared optical mapping-defined driver locations (16 of 20), and matched 14 of 20 driver visualizations: 10 of 14 re-entries seen with Rotational Activity Profile; and 4 of 6 breakthrough/focal patterns. Focal Impulse and Rotor Mapping detected 1.1 ± 0.9 false-positive rotational activity profiles per recording, but these regions had lower intramural contrast-enhanced cardiac magnetic resonance imaging fibrosis than did driver regions (14.9 ± 7.9% vs. 23.2 ± 10.5%; p < 0.005). CONCLUSIONS: The study revealed that both re-entrant and breakthrough/focal AF driver patterns visualized by surface-only clinical multielectrodes can represent projections of 3D intramural microanatomic re-entries. Integration of multielectrode mapping and 3D fibrosis analysis may enhance AF driver detection, thereby improving the efficacy of driver-targeted ablation.

Identification of Repetitive Activation Patterns Using Novel Computational Analysis of Multielectrode Recordings During Atrial Fibrillation and Flutter in Humans.

OBJECTIVES: The purpose of this study was to assess computational analysis of 64-electrode basket catheter (BC) recordings of atrial fibrillation (AF) and atrial flutter using novel software, CARTOFINDER (CF). BACKGROUND: Repetitive patterns have been recorded during AF and reported to be an important mechanism of AF. CF was used to identify rotational repetitive activation patterns (RAPs) in the right (RA) and left atrium (LA). METHODS: To assess for presence of RAPs, multiple 1-min BC maps of the RA and LA were obtained before and after radiofrequency ablation (RFA) around the pulmonary veins in 14 patients undergoing AF ablation. Validation of the CF algorithm was based on analysis of BC recordings of the cavotricuspid isthmus flutter. RESULTS: There were 2.9 rotational RAPs per patient (1.3 RA; 1.6 LA). No RAPs were noted in 2 patients. RFA was delivered on top of (n = 10), within 5 mm (n = 5), or distant (n = 10) from any RAP. Reproducibility of the BC to identify the same RAP was 82%. Post-pulmonary vein (PV) isolation, there was a 45% reduction in RAP versus pre-RFA. CF was validated by 4 electrophysiologists blindly reviewing 32 RA CF maps. Electrophysiologists correctly categorized presence/absence of RAP in 122 of 128 maps (95%). CONCLUSIONS: CF is novel software incorporated into CARTO that identifies rotational RAP in the RA and LA with 82% reproducibility. PV RFA results in 45% reduction of RAP, suggesting that RFA beyond PV isolation is required to eliminate the bulk of RAP. Electrophysiologists who were first-time users of CF could readily identify RAPs.