Background: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. Methods: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. Results: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). Conclusion: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.

Low atrial endocardial bipolar voltage, measured during catheter ablation for atrial fibrillation (AF), is a commonly used surrogate marker for the presence of atrial fibrosis. Low voltage shows many useful associations with clinical outcomes, comorbidities and has links to trigger sites for AF. Several contemporary trials have shown promise in targeting low voltage areas as the substrate for AF ablation; however, the results have been mixed. In order to understand these results, a thorough understanding of voltage mapping techniques, the relationship between low voltage and the pathophysiology of AF, as well as the inherent limitations in voltage measurement are needed. Two key questions must be answered in order to optimally apply voltage mapping as the road map for ablation. First, are the inherent limitations of voltage mapping small enough as to be ignored when targeting specific tissue based on voltage? Second, can conventional criteria, using a binary threshold for voltage amplitude, truly define the extent of the atrial fibrotic substrate? Here, we review the latest clinical evidence with regard to voltage-based ablation procedures before analysing the utility and limitations of voltage mapping. Finally, we discuss omnipole mapping and dynamic voltage attenuation as two possible approaches to resolving these issues.

Cardiovascular magnetic resonance (CMR) imaging has been used to visualise post-ablation atrial scar (PAAS), generally employing a three-dimensional (3D) late gadolinium enhancement (LGE) technique. However the reproducibility of PAAS imaging has not been determined. This cross-over study is the first to investigate the reproducibility of the technique, crucial for both future research design and clinical implementation. Forty subjects undergoing first time ablation for atrial fibrillation (AF) had detailed CMR assessment of PAAS. Following baseline pre-ablation scan, two scans (separated by 48 h) were performed at three months post-ablation. Each scan session included 3D LGE acquisition at 10, 20 and 30 min post administration of gadolinium-based contrast agent (GBCA). Subjects were allocated at second scan post-ablation to identical imaging parameters (‘Repro’, n = 10), 3 T scanner (‘3 T’, n = 10), half-slice thickness (‘Half-slice’, n = 10) or half GBCA dose (‘Half-gad’, n = 10). PAAS was compared to baseline scar and then reproducibility was assessed for two measures of thresholded scar (% left atrial (LA) occupied by PAAS (%LA PAAS) and Pulmonary Vein Encirclement (PVE)), and then four measures of non-thresholded scar (point-by-point assessment of PAAS, four normalisation methods). Thresholded measures of PAAS were evaluated against procedural outcome (AF recurrence). A total of 271 3D acquisitions (out of maximum 280, 96.7%) were acquired. At 20 and 30 min, inter-scan reproducibility was good to excellent (coefficient of variation at 20 min and 30 min: %LA PAAS 0.41 and 0.20; PVE 0.13 and 0.04 respectively for ‘Repro’ group). Changes in imaging parameters, especially reduced GBCA dose, reduced inter-scan reproducibility, but for most measures remained good to excellent (ICC for %LA PAAS 0.454–0.825, PVE 0.618–0.809 at 30 min). For non-thresholded scar, highest reproducibility was observed using blood pool z-score normalisation technique: inter-scan ICC 0.759 (absolute agreement, ‘Repro’ group). There was no significant relationship between indices of PAAS and AF recurrence. PAAS imaging is a reproducible finding. Imaging should be performed at least 20 min post-GBCA injection, and a blood pool z-score should be considered for normalisation of signal intensities. The clinical implications of these findings remain to be established in the absence of a simple correlation with arrhythmia outcome. United Kingdom National Research Ethics Service 08/H0802/68 – 30th September 2008.

Biophysical models of the atrium provide a physically constrained framework for describing the current state of an atrium and allow predictions of how that atrium will respond to therapy. We propose a work flow to simulate patient specific electrophysiological heterogeneity from clinical data and validate the resulting biophysical models. In 7 patients, we recorded the atrial anatomy with an electroanatomical mapping system (St Jude Velocity); we then applied an S1-S2 electrical stimulation protocol from the coronary sinus (CS) and the high right atrium (HRA) whilst recording the activation patterns using a PentaRay catheter with 10 bipolar electrodes at 12 ± 2 sites across the atrium. Using only the activation times measured with a PentaRay catheter and caused by a stimulus applied in the CS with a remote catheter we fitted the four parameters for a modified Mitchell-Schaeffer model and the tissue conductivity to the recorded local conduction velocity restitution curve and estimated local effective refractory period. Model parameters were then interpolated across each atrium. The fitted model recapitulated the S1-S2 activation times for CS pacing giving a correlation ranging between 0.81 and 0.98. The model was validated by comparing simulated activations times with the independently recorded HRA pacing S1-S2 activation times, giving a correlation ranging between 0.65 and 0.96. The resulting work flow provides the first validated cohort of models that capture clinically measured patient specific electrophysiological heterogeneity.

Cardiovascular magnetic resonance (CMR) imaging may be used to visualize post-ablation atrial scar (PAAS), and three-dimensional late gadolinium enhancement (3D LGE) is the most widely employed technique for imaging of chronic scar. Detection of PAAS provides a unique non-invasive insight into the effects of the ablation and may help guide further ablation procedures. However, there is evidence that PAAS is often not detected by CMR, implying a significant sensitivity problem, and imaging parameters vary between leading centres. Therefore, there is a need to establish the optimal imaging parameters to detect PAAS. Forty subjects undergoing their first pulmonary vein isolation procedure for AF had detailed CMR assessment of atrial scar: one scan pre-ablation, and two scans post-ablation at 3 months (separated by 48 h). Each scan session included ECG- and respiratory-navigated 3D LGE acquisition at 10, 20 and 30 min post injection of a gadolinium-based contrast agent (GBCA). The first post-procedural scan was performed on a 1.5 T scanner with standard acquisition parameters, including double dose (0.2 mmol/kg) Gadovist and 4 mm slice thickness. Ten patients subsequently underwent identical scan as controls, and the other 30 underwent imaging with a reduced, single, dose GBCA (n = 10), half slice thickness (n = 10) or on a 3 T scanner (n = 10). Apparent signal-to-noise (aSNR), contrast-to-noise (aCNR) and imaging quality (Likert Scale, 3 independent observers) were assessed. PAAS location and area (%PAAS scar) were assessed following manual segmentation. Atrial shells with standardised %PAAS at each timepoint were then compared to ablation lesion locations to assess quality of scar delineation. A total of 271 3D acquisitions (out of maximum 280, 96.7%) were acquired. Likert scale of imaging quality had high interobserver and intraobserver intraclass correlation coefficients (0.89 and 0.96 respectively), and showed lower overall imaging quality on 3 T and at half-slice thickness. aCNR, and quality of scar delineation increased significantly with time. aCNR was higher with reduced, single, dose of GBCA (p = 0.005). 3D LGE CMR atrial scar imaging, as assessed qualitatively and quantitatively, improves with time from GBCA administration, with some indices continuing to improve from 20 to 30 min. Imaging should be performed at least 20 min post-GBCA injection, and a single dose of contrast should be considered. Trial registry- United Kingdom National Research Ethics Service 08/H0802/68 – 30th September 2008.

Background: Electroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner.Methods: A data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research.Results: The average time to parse clinical datasets was 400 ± 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 ± 8.7 Mb, vs clinical: 1.46 ± 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: R² = 0.7726, P < 0.0001; Volume: R² = 0.5179, P < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage R² = 0.8708, P < 0.001; local activation time R² = 0.8892, P < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies.Conclusions: We present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.