Predictive utility of the impedance drop on AF recurrence using digital intraprocedural data linked to electronic health record data.

Background: Local impedance drop in cardiac tissue during catheter ablation may be a valuable measure to guide atrial fibrillation (AF) ablation procedures for greater effectiveness. Objective: The study sought to assess whether local impedance drop during catheter ablation to treat AF predicts 1-year AF recurrence and what threshold of impedance drop is most predictive. Methods: We identified patients with AF undergoing catheter ablation in the Mercy healthcare system. We downloaded AF ablation procedural data recorded by the CARTO system from a cloud-based analytical tool (CARTONET) and linked them to individual patient electronic health records. Average impedance drops in anatomical region of right and left pulmonary veins were calculated. Effectiveness was measured by a composite outcome of repeat ablation, AF rehospitalization, direct current cardioversion, or initialization of a new antiarrhythmic drug post-blanking period. The association between impedance drop and 1-year AF recurrence was assessed by logistic regression adjusting for demographics, clinical, and ablation characteristics. Bootstrapping was used to determine the most predictive threshold for impedance drop based on the Youden index. Results: Among 242 patients, 23.6% (n = 57) experienced 1-year AF recurrence. Patients in the lower third vs upper third of average impedance drop had a 5.9-fold (95% confidence interval [CI] 1.81-21.8) higher risk of recurrence (37.0% vs 12.5%). The threshold of 7.2 Ω (95% CI 5.75-7.7 Ω) impedance drop best predicted AF recurrence, with sensitivity of 0.73 and positive predictive value of 0.33. Patients with impedance drop ≤7.2 Ω had 3.5-fold (95% CI 1.39-9.50) higher risk of recurrence than patients with impedance drop >7.2 Ω, and there was no statistical difference in adverse events between the 2 groups of patients. Sensitivity analysis on right and left wide antral circumferential ablation impedance drop was consistent. Conclusion: Average impedance drop is a strong predictor of clinical success in reducing AF recurrence but as a single criterion for predicting recurrence only reached 73% sensitivity and 33% positive predictive value.

Does radiofrequency ablation procedural data improve the accuracy of identifying atrial fibrillation recurrence?

Radiofrequency ablation (RFA) using the CARTO 3D mapping system is a common approach for pulmonary vein isolation to treat atrial fibrillation (AF). Linkage between CARTO procedural data and patients' electronical health records (EHR) provides an opportunity to identify the ablation-related parameters that would predict AF recurrence. The objective of this study is to assess the incremental accuracy of RFA procedural data to predict post-ablation AF recurrence using machine learning model. Procedural data generated during RFA procedure were downloaded from CARTONET and linked to deidentified Mercy Health EHR data. Data were divided into train (70%) and test (30%) data for model development and validation. Automate machine learning (AutoML) was used to predict 1 year AF recurrence, defined as a composite of repeat ablation, electrical cardioversion, and AF hospitalization. At first, AutoML model only included Patients' demographic and clinical characteristics. Second, an AutoML model with procedural variables and demographical/clinical variables was developed. Area under receiver operating characteristic curve (AUROC) and net reclassification improvement (NRI) were used to compare model performances using test data. Among 306 patients, 67 (21.9%) patients experienced 1-year AF recurrence. AUROC increased from 0.66 to 0.78 after adding procedural data in the AutoML model based on test data. For patients with AF recurrence, NRI was 32% for model with procedural data. Nine of 10 important predictive features were CARTO procedural data. From CARTO procedural data, patients with lower contact force in right inferior site, long ablation duration, and low number of left inferior and right roof lesions had a higher risk of AF recurrence. Patients with persistent AF were more likely to have AF recurrence. The machine learning model with procedural data better predicted 1-year AF recurrence than the model without procedural data. The model could be used for identification of patients with high risk of AF recurrence post ablation.

Validation of a novel CARTOSEG™ segmentation module software for contrast-enhanced computed tomography-guided radiofrequency ablation in patients with atrial fibrillation.

INTRODUCTION: Visualization of left atrial (LA) anatomy using image integration modules has been associated with decreased radiation exposure and improved procedural outcome when used for guidance of pulmonary vein isolation (PVI) in atrial fibrillation (AF) ablation. We evaluated the CARTOSEG™ CT Segmentation Module (Biosense Webster, Inc.) that offers a new CT-specific semiautomatic reconstruction of the atrial endocardium. METHODS: The CARTOSEG™ CT Segmentation Module software was assessed prospectively in 80 patients undergoing AF ablation. Using preprocedural contrast-enhanced computed tomography (CE-CT), cardiac chambers, coronary sinus (CS), and esophagus were semiautomatically segmented. Segmentation quality was assessed from 1 (poor) to 4 (excellent). The reconstructed structures were registered with the electroanatomic map (EAM). PVI was performed using the registered 3D images. RESULTS: Semiautomatic reconstruction of the heart chambers was successfully performed in all 80 patients with AF. CE-CT DICOM file import, semiautomatic segmentation of cardiac chambers, esophagus, and CS was performed in 185 ± 105, 18 ± 5, 119 ± 47, and 69 ± 19 seconds, respectively. Average segmentation quality was 3.9 ± 0.2, 3.8 ± 0.3, and 3.8 ± 0.2 for LA, esophagus, and CS, respectively. Registration accuracy between the EAM and CE-CT-derived segmentation was 4.2 ± 0.9 mm. Complications consisted of one perforation (1%) which required pericardiocentesis, one increased pericardial effusion treated conservatively (1%), and one early termination of ablation due to thrombus formation on the ablation sheath without TIA/stroke (1%). All targeted PVs (n  =  309) were successfully isolated. CONCLUSIONS: The novel CT- CARTOSEG™ CT Segmentation Module enables a rapid and reliable semiautomatic 3D reconstruction of cardiac chambers and adjacent anatomy, which facilitates successful and safe PVI.