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.

Characterization and significance of localized sources identified by a novel automated algorithm during mapping of human persistent atrial fibrillation.

BACKGROUND: Automated algorithms may identify focal (FA) and rotational (RoA) activations during persistent atrial fibrillation (PeAF). OBJECTIVE: To evaluate an automated algorithm for characterizing and assessing significance of FA/RoA. METHODS: Eighty-six PeAF ablation patients (1411 maps) were analyzed. Maps were obtained with a 64-electrode basket using CARTOFINDER, which filters/annotates atrial unipolar electrograms over 30 seconds. Operators ablated FA/RoA followed by pulmonary vein isolation (PVI). The automated algorithm was retrospectively applied using QS patterns to identify FA and sequential activation gradients for RoA without phase mapping. Algorithm-identified FA and RoA were validated against blinded adjudicators. Ablation of algorithm-identified FA/RoA was related to procedural AF termination. RESULTS: 73% ± 18% of electrodes (65% ± 11% atrial surface area) were adequate for analysis. Compared with adjudicators, the algorithm had a sensitivity of 84% for FA and 86% for RoA. There were 4 ± 2 FA and 2 ± 2 RoA per patient. FA occurred 8 ± 6 times during the 30-second window (cumulative duration 8 ± 6 seconds). RoA occurred 5 ± 3 times (median 2, consecutive rotations) with a cumulative duration of 3 ± 2 seconds. Compared to patients without procedural AF termination, patients with termination had more FA ablated (75% vs 38%, P = 0.006). AF termination was not predicted by percentage of RoA ablated although there was a trend towards a higher percentage of left atrial RoA ablated ( P = 0.06). CONCLUSION: An automated algorithm had high sensitivity for FA and RoA. Acute AF termination was associated with FA ablation but not RoA ablation. Future studies need to define the significance of FA and RoA and whether they are overlapping or separate mechanisms.

Sleep disordered breathing analysis in a general population using standard pulse oximeter signals.

OBJECTIVES: Obstructive sleep apnea reported as the apnea-hypopnea index (AHI) is usually measured in sleep laboratories using a high number of electrodes connected to the patient's body. In this study, we examined the use of a standard pulse oximeter system with an automated analysis based on the photoplethysmograph (PPG) signal for the diagnosis of sleep disordered breathing. Using a standard and simple device with high accuracy might provide a convenient diagnostic or screening solution for patient evaluation at home or in other out of center testing environments. METHODS: The study included 140 consecutive patients that were referred routinely to a sleep laboratory [SleepMed Inc.] for the diagnosis of sleep disordered breathing. Each patient underwent an overnight polysomnography (PSG) study according to AASM guidelines in an AASM-accredited sleep laboratory. The automatic analysis is based on photoplethysmographic and saturation signals only. Those two signals were recorded for the entire night as part of the full overnight PSG sleep study. The AHI calculated from the PPG analysis is compared to the AHI calculated from the manual scoring gold standard full PSG. RESULTS: The AHI and total respiratory events measured by the pulse oximeter analysis correlated very well with the corresponding results obtained by the gold standard full PSG. The sensitivity and specificity of AHI = or > 5 and 15 levels measured by the analysis are both above 90 %. The sensitivity and positive predictive value for the detection of respiratory event are both above 84 %. CONCLUSIONS: The tested system in this study yielded an acceptable result of sleep disordered breathing compared to the gold standard PSG in patients with moderate to severe sleep apnea. Accordingly and given the convenience and simplicity of the standard pulse oximeter device, the new system can be considered suitable for home and ambulatory diagnosis or screening of sleep disordered breathing patients.

An automated sleep-analysis system operated through a standard hospital monitor.

STUDY OBJECTIVES: Sleep disordered breathing (SDB), a cause of clinically important cardiovascular comorbidity, is often not recognized and diagnosed. An automated system that detects SDB using signals from a standard hospital monitor might provide useful information about the presence and severity of SDB without the need to evaluate the patient in a sleep laboratory and without additional hardware. The aim of this study was to examine the feasibility and accuracy of routine overnight sleep testing for SDB detection by an automated analysis system that operates by analyzing signals derived from standard hospital monitors. METHODS: Comparison of SDB detection by simultaneous "gold-standard" polysomnography and by Morpheus Hx (WideMed, Ltd., Herzliya, Israel), a bedside computerized analysis system (CAS) connected to a standard hospital monitor (ECG, respiratory impedance, end-tidal carbon dioxide (ETCO2), and SpO2). A total of 53 subjects were examined, 36 men and 17 women, all with suspected SDB. Each subject underwent an overnight sleep study, scored both by polysomnography and by CAS. The study was conducted in Brigham and Women's Hospital, Newton Center, MA. RESULTS: CAS-derived values for apnea-hypopnea index and total sleep time, were each found to be highly correlated with the corresponding polysomnography results, with linear regression values of r = 0.96 and r = 0.82, respectively. Mean apnea-hypopnea index values were also quite similar (CAS of 15.5 +/- 20.0 vs polysomnography of 15.4 +/- 24.0). CONCLUSIONS: An automated sleep-analysis system utilizing signals derived from a standard hospital monitor can be considered as a feasible and accurate method to detect and quantify SDB.