Detection Of Flutter Research Articles

Atrial fibrillation detection on implantable loop recorders in embolic stroke of undetermined source

Abstract Introduction Embolic stroke of undetermined source (ESUS) represents 17% of all ischaemic strokes, and has a 5% annual stroke recurrence risk. Recent large randomised control trials failed to demonstrate a benefit of empirical anticoagulation for all ESUS patients, with sub-analyses supporting the need for a tailored approach to those patients at greatest risk of atrial fibrillation (AF) development. Understanding how the increased incidence of ILR-detected AF in the ESUS cohort, compares with ILR-detected AF in other cohorts is an essential step in understanding the significance of this finding. Purpose To compare the incidence of ILR-detected AF following ESUS against patients undergoing ILR implantation for non-stroke indications for example syncope and palpitations. Methods We retrospectively studied all patients without known AF who were referred to our institution for ILR implantation following ESUS, syncope or palpitations from 2009 to 2019. The primary endpoint was any detection of AF or atrial flutter (AFL) by ILR. Kaplan-Meier and multivariable Cox-regression analyses were utilised to account for time-to-event. Results A total of 750 patients were included and followed up for a mean of 731 days (SD 443). An ILR was implanted following ESUS in 323 patients and for another reason in 427 patients (figure 1). The incidence of AF was significantly higher among patients with ESUS compared to the non-ESUS group; 48.6% versus 13.8% (for any duration of AF) and 32.2% versus 12.4% (for AF lasting > 30 seconds) both p <0.001. Kaplan-Meier analysis (figure 2), showed significantly higher incidence of AF for ESUS for any duration of AF. This was driven predominantly by AF duration <6 minutes and between 5.5 hours and 24 hours. Importantly, in a multivariable Cox regression analysis, ESUS independently conferred an almost 5-fold increase in the hazard for any duration of AF (Hazard Ratio 4.948, 95% confidence interval 3.587 – 6.825, p<0.001). Conclusion The incidence of AF is significantly higher amongst ESUS versus non-ESUS patients monitored constantly by an ILR. A high number of ESUS survivors have short duration AF episodes. Further work is needed to determine the optimal treatment strategy of these AF episodes in ESUS considering AF duration and overall burden.

The incidence of atrial fibrillation detected by implantable loop recorders: a comparison between patients with and without embolic stroke of undetermined source.

Stroke is the most debilitating outcome of atrial fibrillation (AF). The use of implantable loop recorders increases the detection of AF episodes among patients with embolic stroke of undetermined source. The significance of device-detected AF, or subclinical AF, is unknown. This study aimed to compare the incidence of AF detected by implantable loop recorder in patients with and without embolic stroke of undetermined source. We retrospectively studied all patients without known AF who were referred to our institution for implantable loop recorder implantation following embolic stroke of undetermined source, syncope, or palpitations from March 2009 to November 2019. The primary endpoint was any detection of AF or atrial flutter by implantable loop recorder. Seven hundred and fifty patients were included and followed up for a mean duration of 731 days (SD 443). An implantable loop recorder was implanted following embolic stroke of undetermined source in 323 and for assessment of syncope, palpitations, or another reason in 427 patients. The incidence of AF was significantly (P < 0.001) higher among patients with embolic stroke of undetermined source compared with the non-embolic stroke of undetermined source group; 48.6% vs. 13.8% (for any duration of AF) and 32.2% vs. 12.4% (for AF lasting ≥30 s) both P < 0.001. Kaplan-Meier analysis showed significantly higher incidence of AF for incremental durations of AF up to >5.5 h, but not >24 h. This was driven by longest AF durations of <6 min and between 5.5 h and 24 h, suggesting a bimodal distribution. In a multivariable Cox regression analysis, embolic stroke of undetermined source independently conferred an almost 5-fold increase in the hazard for any duration of AF. The incidence of AF is significantly higher amongst embolic stroke of undetermined source vs. non-embolic stroke of undetermined source patients monitored constantly by an implantable loop recorder. A high number of embolic stroke of undetermined source survivors have short-duration AF episodes. Further work is needed to determine the optimal treatment strategy of these AF episodes in embolic stroke of undetermined source.

Performance of a vectorcardiographic deep learning algorithm compared to single-lead and 12-lead ECG for atrial flutter detection: implications for wearable devices

Abstract Background Wearable devices have developed the capacity to offer single-lead ECG analysis and are already widespread in use, particularly for detection of atrial fibrillation (1,2). Vectorcardiography entails the transformation of electrical vectors into X, Y, and Z axes to constitute a vectorcardiogram (VCG) (3). The VCG has diagnostic promise given the capacity to generate it without requiring clinical presentation for standard 12-lead ECG acquisition. Furthermore, a novel credit-card sized device capable of acquiring a VCG has recently been developed – raising the potential for arrhythmia detection and discrimination beyond wearable single-lead ECG systems. Objective To assess the performance of a deep learning algorithm trained on single lead and 12-lead ECGs and VCGs for detecting atrial flutter. Methods A convolutional neural network (AFL-CNN) was developed on a dataset of 4,000 ECGs and VCGs from a single US academic medical institution (80% training and 20% for testing). Validation was performed on a separate publicly available dataset (2223 ECGs). Model performance was assessed on single lead ECG analysis (lead I, obtained by removing the other leads) and standard 12-lead ECG. The gold standard for AFL diagnosis was physician-confirmed 12-lead ECG diagnoses in the dataset. The VCG was generated from the 12-lead ECG via Kors transformation matrix to which AFL-CNN was also applied. Model performance was assessed with sensitivity, specificity, positive and negative predictive values, accuracy, and F1 statistic. Results On single lead ECG analysis, AFL-CNN achieved 71.2% sensitivity and 96.9% specificity with PPV 84.0% and NPV 93.7% and 92.2% accuracy with F1 83.2%. When AFL-CNN was applied to the VCG, performance improved: 91.0% sensitivity and 98.7% specificity, PPV 94.24%, NPV 98.0%, 97.3% accuracy, and F1 95.31%. Lastly, the performance remained high when AFL-CNN was applied to the 12-lead ECG: 95.4% sensitivity, 98.63% specificity, PPV 94.0%, NPV 99.0%, and accuracy of 98.0%, with F1 97.2%. Conclusion A deep learning algorithm applied to vectorcardiography for the detection of atrial flutter showed improved performance over single lead analysis and comparable performance to standard 12-lead applications. The finding of improved model performance beyond single lead ECG tracings underscores both the limitations of current wearable technologies in arrhythmia detection beyond atrial fibrillation and the potential for VCG-based algorithm prediction to fill gaps in healthcare inequality when standard 12-lead acquisition is challenging to obtain.

Long-term Outcomes of Cryoballoon-based Empirical Superior Vena Cava Isolation in Addition to Pulmonary Vein Isolation in Persistent Atrial Fibrillation.

Superior vena cava (SVC) is atrial fibrillation (AF)'s most common non-pulmonary vein (PV) foci. Studies reported conflictory results when SVC isolation (SVCi) was combined with PVi and long-term outcomes were lacking. Therefore, we aimed to evaluate the long-term efficacy and safety of empirical SVCi as an adjunct to cryoballoon-based PV isolation (PVi) in persistent AF ablation. A total of 40 consecutive persistent AF patients (60.6 ± 8.2 years, 52.5% females) who underwent SVCi in addition to PVi compared with a propensity score matched cohort of 40 persistent AF patients (58.6 ± 8.7 years, 50% female) in whom PVi-only was performed. Second-generation cryoballoon (CB2) was used in all procedures. Atrial tachyarrhythmia (ATa) recurrence was defined as the detection of AF, atrial flutter, or atrial tachycardia (≥30 s) after a 3-month blanking period. Pulmonary veins and SVC were successfully isolated in all patients. At a mean of 46.7 ± 7.8 months follow-up, 22 (55%) patients in the PVi-only group, and 27 (67.5%) patients in the PVi + SVCi group were free of ATa after the index procedure (P =.359). Phrenic nerve injury (PNI) was detected in 2 (5%) patients in the PVi-only group (during right PVi) and 2 (5%) patients in the PVi + SVCi group (during SVCi) (P = 1.00). Cox regression analysis revealed that early recurrence was the only predictor of recurrence (hazard ratio 4.88, 95% confidence interval 1.59-14.96; P =.005). Long-term results of our small sample-sized study revealed that CB-based PVi + SVCi was associated with outcomes similar to the PVi-only strategy in patients with persistent AF. Although complication rates were similar between the groups, close follow-up of diaphragmatic movement is crucial to prevent PNI during SVCi.

On-chip, non-invasive detection of all atrial arrhythmias for immediate and accurate characterization in adult rats

Abstract Background To monitor and record the occurrence and duration of cardiac arrhythmias in freely moving animals, ECG telemetry devices are often used. However, traditional telemetry devices require complex equipment unsuitable for on-site application in most animal housing facilities. Additionally, current atrial arrhythmias detection algorithms are mainly based on ECG R-R interval variability for atrial fibrillation (AF) detection. Such algorithms do not allow the detection of stable R-R interval atrial arrhythmias such as flutter. Thus there is currently a lack of non-invasive methods allowing automated detection of all atrial arrhythmias subtypes, thereby precluding in-depth research into general and subtype-specific mechanisms and treatment. Purpose Here we aim to create a non-invasive system allowing fast and accurate on-site detection and characterization of all atrial arrhythmias in rats. Methods Our atrial arrhythmia detector system was made of an on-skin ECG sensor, a low-power microprocessor unit, a large data storage unit and a battery. All components were assembled within a lightweight rat jacket. The filtered ECG signal was sent to the microprocessor allowing algorithm-based, real-time detection of atrial arrhythmias. The device allowed storage of ECG traces as well as arrhythmia characteristics (including type of arrhythmia and its duration). Atrial arrhythmias were induced by transesophageal atrial burst pacing in Wistar rats. These arrhythmia recordings were used to develop an AI detection method. Next, real-time detection algorithms based on R-R variability and AI were both integrated on-chip and compared using the same ECG data. Results ECG traces measured with our new system were similar to classical ECG measurements. Stored ECG traces provided the raw data for the development of an AI-based detection algorithm. The AI training was done using ECG data recorded from five different rats and reached 99.5% self-testing accuracy to detect all sub-types of atrial arrhythmias based on pre-categorized data. Using uncategorized data (n=25 from 5 rats) different from the training dataset, the AI algorithm could detect &amp;gt;80% of all types of atrial arrhythmias within 3s after initiation, while the rest could be detected within 10s. Though R-R interval based algorithm showed a high detection rate of 88% of AF within 10s, this method did not allow the detection of flutter or AF presenting regular R-R intervals within 1 minute after initiation. Conclusion Our atrial arrhythmia detection and analysis system provides a novel method for non-invasive investigation into atrial arrhythmias in rats and could easily be scaled for other species. It allows on-site fast and accurate detection and characterization of atrial arrhythmia subtypes without external equipment, thereby opening new possibilities for mechanistic studies and therapeutic testing, including closed-loop applications aiming for arrhythmia termination.

Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture.

Automatic detection of atrial fibrillation and flutter (AF/AFL) is a significant concern in preventing stroke and mitigating hemodynamic instability. Herein, we developed a Transformer-based deep learning model for AF/AFL segmentation in single-lead electrocardiograms (ECGs) by self-supervised learning with masked signal modeling (MSM). We retrieved data from 11 open-source databases on PhysioNet; 7 of these databases included labeled ECGs, while the other 4 were without labels. Each database contained ECG recordings with durations of ≥30s. A total of 24 intradialytic ECGs with paroxysmal AF/AFL during 4h of hemodialysis sessions at Seoul National University Hospital were used for external validation. The model was pretrained by predicting masked areas of ECG signals and fine-tuned by predicting AF/AFL areas. Cross-database validation was used for evaluation, and the intersection over union (IOU) was used as a main performance metric in external database validation. In the 7 labeled databases, the areas marked as AF/AFL constituted 41.1% of the total ECG signals, ranging from 0.19% to 51.31%. In the evaluation per ECG segment, the model achieved IOU values of 0.9254 and 0.9477 for AF/AFL segmentation and other segmentation tasks, respectively. When applied to intradialytic ECGs with paroxysmal AF/AFL, the IOUs for the segmentation of AF/AFL and non-AF/AFL were 0.9896 and 0.9650, respectively. Model performance by different training procedure indicated that pretraining with MSM and the application of an appropriate masking ratio both contributed to the model performance. It also showed higher IOUs of AF/AFL labels than in previous studies when training and test databases were matched. The present model with self-supervised learning by MSM performs robustly in segmenting AF/AFL.

Automatic Detection of Intradialytic Paroxysmal Atrial Fibrillation and Flutter in Single-Lead ECG

Automatic Detection of Intradialytic Paroxysmal Atrial Fibrillation and Flutter in Single-Lead ECG

Machine learning augmented multi-sensor data fusion to detect aero engine fan rotor blade flutter

Abstract Flutter-induced fatigue failure investigation of the fan blades of aero-engines necessitates extensive testing. During engine ground testing, strain gauges on rotor fan blades and casing vibration sensors were employed to investigate structural dynamic aspects. The correlation between strain sensor signals and fan casing vibration signals allowed the diagnosis of fluttering fan blades. For automated flutter detection during engine development testing, a machine learning-augmented information fusion methodology was developed. The method analyses casing vibration signals by extracting time-domain statistical features, intrinsic mode function characteristics through empirical mode decomposition, and recurrence quantification features. Feature vectors obtained from a relatively large set of engine tests were subjected to dimension reduction by applying machine learning techniques to rank them. Reduced feature vector space was labelled as “flutter” or “normal” based on the correlation of rotor strain gauge signals. In addition, the labelled feature vectors were employed to train classifier models using supervised learning-based algorithms such as Support Vector Machines, Linear Discriminant Analysis, K-means Clustering, and Artificial Neural Networks. Using only vibration signals from the casing, the trained and validated classifiers were able to detect flutter in fan baldes with a 99% probability during subsequent testing.

Atrioventricular Synchronization for Detection of Atrial Fibrillation and Flutter in One to Twelve ECG Leads Using a Dense Neural Network Classifier.

This study investigates the use of atrioventricular (AV) synchronization as an important diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart rate, lead-specific AV conduction time, and P-/f-wave amplitude were evaluated by three representative ECG metrics (mean value, standard deviation), namely RR-interval (RRi-mean, RRi-std), PQ-interval (PQi-mean, PQI-std), and PQ-amplitude (PQa-mean, PQa-std), in 71,545 standard 12-lead ECG records from the six largest PhysioNet CinC Challenge 2021 databases. Two rhythm classes were considered (AF, non-AF), randomly assigning records into training (70%), validation (20%), and test (10%) datasets. In a grid search of 19, 55, and 83 dense neural network (DenseNet) architectures and five independent training runs, we optimized models for one-lead, six-lead (chest or limb), and twelve-lead input features. Lead-set performance and SHapley Additive exPlanations (SHAP) input feature importance were evaluated on the test set. Optimal DenseNet architectures with the number of neurons in sequential [1st, 2nd, 3rd] hidden layers were assessed for sensitivity and specificity: DenseNet [16,16,0] with primary leads (I or II) had 87.9–88.3 and 90.5–91.5%; DenseNet [32,32,32] with six limb leads had 90.7 and 94.2%; DenseNet [32,32,4] with six chest leads had 92.1 and 93.2%; and DenseNet [128,8,8] with all 12 leads had 91.8 and 95.8%, indicating sensitivity and specificity values, respectively. Mean SHAP values on the entire test set highlighted the importance of RRi-mean (100%), RR-std (84%), and atrial synchronization (40–60%) for the PQa-mean (aVR, I), PQi-std (V2, aVF, II), and PQi-mean (aVL, aVR). Our focus on finding the strongest AV synchronization predictors of AF in 12-lead ECGs would lead to a comprehensive understanding of the decision-making process in advanced neural network classifiers. DenseNet self-learned to rely on a few ECG behavioral characteristics: first, characteristics usually associated with AF conduction such as rapid heart rate, enhanced heart rate variability, and large PQ-interval deviation in V2 and inferior leads (aVF, II); second, characteristics related to a typical P-wave pattern in sinus rhythm, which is best distinguished from AF by the earliest negative P-peak deflection of the right atrium in the lead (aVR) and late positive left atrial deflection in lateral leads (I, aVL). Our results on lead-selection and feature-selection practices for AF detection should be considered for one- to twelve-lead ECG signal processing settings, particularly those measuring heart rate, AV conduction times, and P-/f-wave amplitudes. Performances are limited to the AF diagnostic potential of these three metrics. SHAP value importance can be used in combination with a human expert’s ECG interpretation to change the focus from a broad observation of 12-lead ECG morphology to focusing on the few AV synchronization findings strongly predictive of AF or non-AF arrhythmias. Our results are representative of AV synchronization findings across a broad taxonomy of cardiac arrhythmias in large 12-lead ECG databases.

Flutter prediction of its occurrence, amplitude and nonlinear behaviour

Flutter prediction of its occurrence, limit cycle amplitude and chaotic behaviour is investigated using a general binary (two degree of freedom) coupled modal model to provide new closed form analytical predictions and insight into the aeroelastic phenomenon. The generalised unsteady flutter model, based on flutter derivatives (for any cross-section) and an approximate continuous bilinear lift curve, is used in a mass decoupled form to formulate and develop analytical criteria for the onset and amplitude of flutter under damping and offset angle of attack. This is based on modal coupling through stiffness and damping terms, to clearly identify and clarify two forms of flutter based on stiffness and viscous mode coupling. Closed form analytical criteria for flutter onset are developed including a new necessary dynamic divergence criterion (analogous to spragging in brake squeal) based on flutter derivatives for any structure. The model is first validated with the well-known classical numerical and experimental airfoil results of Theodorsen and analytical results of Pines, which are also shown to correspond to stiffness mode coupling. In addition, the new modelling enables the detection and corresponding conditions of viscous mode coupling flutter which does not require closely spaced modes. The occurrence and conditions of both stiffness and viscous mode coupling flutter is verified numerically with results highlighting important structural parameters i.e. damping factors which could cause flutter outside classical conditions. In particular, the conditions for viscous mode coupling are newly identified and found to occur at flow velocities substantially lower (up to approximately 10%) than classical stiffness mode coupling values. Dynamic divergence is shown to be the lower bound for the critical flutter speed under a range of conditons. The viscous mode coupling is shown to occur under high levels of torsional damping (20 times the nominal 1% of critical), for the airfoil case, compared to vertical damping, but does not require the modal frequencies to be close. The efficient analytical prediction for flutter amplitude is then numerically verified for Theodorsen's airfoil. The results show that flutter amplitude is primarily dependent upon flow velocity and stall conditions without structural nonlinearity. Finally, a further nonlinear investigation of flutter identifies useful analytical criteria for the occurrence of chaotic flutter which is confirmed numerically. These results, based on a bilinear lift coefficient model, highlight that the occurrence of chaotic flutter can be due to simplified aerodynamics alone i.e. without nonlinear structural stiffness. The closed form analyses provide critical insight into; the occurrence and underlying mechanisms of flutter based on stiffness and viscous mode coupling, dynamic divergence as well as the nonlinear amplitudes and behaviour under an offset angle of attack and stall conditions.

Comparison between cryoballoon and radiofrequency for atrial fibrillation ablation in patients with heart failure

Abstract Funding Acknowledgements Type of funding sources: None. Background Radiofrequency (RF) and cryoballoon (CB) ablation are the most commonly performed methods for catheter ablation in patients with atrial fibrillation (AF). However, data regarding AF ablation in patients with reduced left ventricular ejection fraction (LVEF) is scarce. Purpose We aimed to compare the safety and efficacy of RF and CB AF ablation in heart failure (HF) patients with LVEF &amp;lt;50%. Methods HF patients whose LVEF &amp;lt; 50% were included in this retrospective study. Transthoracic echocardiography (TTE) was performed to evaluate LVEF by using Modified Simpson method before and after the procedure. Atrial tachyarrhythmia (ATa) recurrence was defined as detection of AF, atrial flutter, or atrial tachycardia (≥ 30 s) after a 3-month blanking period. Results Eighty-nine (n = 42 CB and n = 47 RF) HF patients were enrolled and baseline characteristics were listed in Table-1. There was no significant difference in AF recurrence rate between the groups [16 (38.1 %) in CB and 12 (25.5 %) in RF, respectively, P = 0.296] during the follow up of median 16.5 (10.25-24) and 11 (9-17) months (Figure-1). There was significant increase in LVEF (39.52 ± 7.77 vs. 46.30 ± 10.95, P &amp;lt; 0.001) and decrease in brain natriuretic peptide (BNP) [230 (10-1653.10) vs. 163 (10-1653.10), P = 0.003] levels after ablation. The changes in LVEF (P = 0.370) and BNP (P = 0.772) were similar between the CB and RF ablation groups. Conclusion Our findings demonstrated that AF ablation using either CB or RF was associated with an improvement in LVEF and neurohormonal profile in AF patients with reduced LVEF.

Exploration of an unconventional validation tool to investigate aero engine transonic fan flutter signature

Abstract Flutter, an aeroelastic blade vibration phenomena, experienced by the fan of an developmental aero gas turbine engine, result in blade failure. Hence, suitable flutter detection instrumentation is required during engine testing. Flutter signature capture from revolving blades is a challenging task that necessitates either a complicated strain gauge-based rotating instrumentation or a noncontact tip timing system. Authors investigated a unique way for identifying, measuring, and validating flutter signature by assessing wall static pressure pulsations produced during blade tip transit across a casing mounted high bandwidth sensor during this research. The authors devised a mathematical model to explain signal spectrum components that feature both amplitude and angle modulation properties at the same time. The theory was tested using first-stage fan rotor blades that were fluttering in the first flexural mode (1F) and forming the second nodal diameter (2ND). The approach’s estimated blade deflection was compared to measurements taken using a traditional tip timing method up to 7 mm and determined to be within 1% inaccuracy. This research provides a low-cost, easy alternative technique for measuring flutter during engine development testing.

A comparison of over-the-counter available smartwatches and devices for electrocardiogram based detection of atrial fibrillation

Abstract Background There is an increasing number of smartwatches and devices commercially available that can generate and automatically interpret an electrocardiogram (ECG). Such devices have an enormous potential to improve population screening and telemonitoring of atrial fibrillation (AF). Purpose There is limited data on the sensitivity, specificity and interpretability of these devices and comparative studies are lacking. Our purpose was to compare three frequently used devices for AF detection. Methods We performed a single-center, prospective study in consecutive patients with AF presenting for electrical cardioversion (ECV). We collected a standard 12-lead ECG recording immediately followed by four times 30 seconds of ECG recordings from different devices for every patient prior to the ECV. These paired measurements were considered simultaneous. If the ECV was performed, the same measurements were repeated afterwards. The standard 12L-ECGs were interpreted by a cardiologist and used as golden standard for heart rhythm. The different devices used for the 30 second ECGs were: Withings Move ECG (lead I), Apple Watch series 5 (lead I), Kardia Mobile 6L (six leads) and Withings/Apple (1:1 ratio) on left knee (lead II). Sensitivity and specificity were determined for each AF detection algorithm excluding patients with atrial flutter (AFL) or uninterpretable ECGs. In addition, proportions of uninterpretable ECGs were determined including all patients and including only patients with sinus rhythm (SR) and compared between devices using McNemar's test. Results A total of 220 patients were included (age 70±10 years, female 35%, first ECV 44%) and in total 415 12-lead ECGs were performed (45% SR, 45% AF, 10% AFL). The sensitivity/specificity were overall similar for all devices (Withings 98%/95%, Apple 94%/98%, Kardia 99%/91%. P&amp;gt;0.05 for all). In detail, Kardia was the most sensitive test with highest proportion of suspected AF (57%) whereas Apple was the most specific, as shown by the highest proportion of normal heart rate results by the device (55%, P=0.003 compared to Kardia (43%)). Overall, Withings, Apple and Kardia had a comparable proportion of uninterpretable ECGs (20%, 20%, 24%, respectively. P&amp;gt;0.05 for all). Lead II had higher proportion of uninterpretable ECGs (32%, p&amp;lt;0.01 compared to all). More specifically, Kardia had a higher rate of uninterpretable ECGs in those with SR (P&amp;lt;0.05 compared to Withings (lead I) and Apple (lead I)). Conclusion In all devices, we found sensitivity/specificity for AF detection between 91%-99%, better than previous studies reported, and 20–24% of uninterpretable ECGs. Kardia was the most sensitive device, but less useful to rule out atrial fibrillation whereas Apple had numerically highest specificity. We aim to further evaluate both cardiologist interpretation and accuracy of atrial flutter detection using different leads to inform clinical use. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Tergooi Cardiology department, J.P. Bokma was supported with a research grant by Amsterdam Cardiovascular Sciences Overview and comparison

Comparison of atrial fibrillation ablation using cryoballoon versus radiofrequency in patients with left common pulmonary veins: mid-term follow-up results.

Cryoballoon (CB) and radiofrequency (RF) ablation techniques have similar outcomes for pulmonary vein isolation (PVI) in patients with atrial fibrillation (AF). However, there is limited data about the impact of different ablation strategies in patients with left common pulmonary vein (LCPV). Our aim was to compare the safety and efficacy of RF and CB ablation in AF patients with LCPV. One hundred and twenty-seven (n = 80 CB and n = 47 RF) AF patients with LCPV detected by preprocedural computerized tomography (CT) were included in the study. Ostial dimensions and trunk distance were measured in all patients. Atrial tachyarrhythmia (ATa) recurrence was defined as detection of AF, atrial flutter, or atrial tachycardia (≥ 30s) after a 3-month blanking period. There was no significant difference in acute procedural success rates for PVI (97.5% in CB and 97.9% in RF, respectively, P = 0.953) and complication rates were similar between the groups (6 (7.5%) in CB and 4 (8.5%) in RF, respectively, P = 1.000). During a median follow-up of 20.7 (4.8-50.2) months for CB and 20.5 (6.2-36.0) months for RF, ATa recurrence was 35.0% and 38.2%, respectively (P = 0.777). Multivariate analysis did not reveal any of the morphologic parameters of LCPV as a significant predictor of ATa recurrence. Our findings demonstrated that both CB and RF ablation techniques have similar efficacy and safety in AF patients with LCPV during the mid-term follow-up.

A comparison of over-the-counter available smartwatches and devices for electrocardiogram based detection of atrial fibrillation

Abstract Background There is an increasing number of smartwatches and devices commercially available that can generate and automatically interpret an electrocardiogram (ECG). Such devices have an enormous potential to improve population screening and telemonitoring of atrial fibrillation (AF). Purpose There is limited data on the sensitivity, specificity and interpretability of these devices and comparative studies are lacking. Our purpose was to compare three frequently used devices for AF detection. Methods We performed a single-center, prospective study in consecutive patients with AF presenting for electrical cardioversion (ECV). We collected a standard 12-lead ECG recording immediately followed by four times 30 seconds of ECG recordings from different devices for every patient prior to the ECV. These paired measurements were considered simultaneous. If the ECV was performed, the same measurements were repeated afterwards. The standard 12L-ECGs were interpreted by a cardiologist and used as golden standard for heart rhythm. The different devices used for the 30 second ECGs were: Withings Move ECG (lead I), Apple Watch series 5 (lead I), Kardia Mobile 6L (six leads) and Withings/Apple (1:1 ratio) on left knee (lead II). Sensitivity and specificity were determined for each AF detection algorithm excluding patients with atrial flutter (AFL) or uninterpretable ECGs. In addition, proportions of uninterpretable ECGs were determined including all patients and including only patients with sinus rhythm (SR) and compared between devices using McNemar's test. Results A total of 220 patients were included (age 70±10 years, female 35%, first ECV 44%) and in total 415 12-lead ECGs were performed (45% SR, 45% AF, 10% AFL). The sensitivity/specificity were overall similar for all devices (Withings 98%/95%, Apple 94%/98%, Kardia 99%/91%. P&amp;gt;0.05 for all). In detail, Kardia was the most sensitive test with highest proportion of suspected AF (57%) whereas Apple was the most specific, as shown by the highest proportion of normal heart rate results by the device (55%, P=0.003 compared to Kardia (43%)). Overall, Withings, Apple and Kardia had a comparable proportion of uninterpretable ECGs (20%, 20%, 24%, respectively. P&amp;gt;0.05 for all). Lead II had higher proportion of uninterpretable ECGs (32%, p&amp;lt;0.01 compared to all). More specifically, Kardia had a higher rate of uninterpretable ECGs in those with SR (P&amp;lt;0.05 compared to Withings (lead I) and Apple (lead I)). Conclusion In all devices, we found sensitivity/specificity for AF detection between 91%-99%, better than previous studies reported, and 20–24% of uninterpretable ECGs. Kardia was the most sensitive device, but less useful to rule out atrial fibrillation whereas Apple had numerically highest specificity. We aim to further evaluate both cardiologist interpretation and accuracy of atrial flutter detection using different leads to inform clinical use. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Tergooi Cardiology department, J.P. Bokma was supported with a research grant by Amsterdam Cardiovascular Sciences Overview and comparison

Kardia Mobile applicability in clinical practice: A comparison of Kardia Mobile and standard 12-lead electrocardiogram records in 100 consecutive patients of a tertiary cardiovascular care center.

Mobile devices are gaining a rising number of users in all countries around the globe. Novel solutions to diagnose patients with out-of-hospital onset of arrhythmic symptoms can be easily used to record such events, but the effectiveness of these devices remain unknown. In a group of 100 consecutive patients of an academic cardiology care center (mean age 68 ± 14.2 years, males: 66%) a standard 12-lead electrocardiogram (ECG) and a Kardia Mobile (KM) record were registered. Both versions were assessed by three independant groups of physicians. The analysis of comparisons for standard ECG and KM records showed that the latter is of lower quality (p < 0.001). It was non-inferior for detection of atrial fibrillation and atrial flutter, showed weaker rhythm detection in pacemaker stimulation (p = 0.008), and was superior in sinus rhythm detection (p = 0.02), though. The sensitivity of KM to detect pathological Q-wave was low compared to specificity (20.6% vs. 93.7%, respectively, p < 0.001). Basic intervals measured by the KM device, namely PQ, RR, and QT were significantly different (shorter) than those observed in the standard ECG method (160 ms vs. 180 ms [p < 0.001], 853 ms vs. 880 ms [p = 0.03] and 393 ms vs. 400 ms [p < 0.001], respectively). Initial and indicative value of atrial fibrillation and atrial flutter detection in KM is comparable to results achieved in standard ECG. KM was superior in detection of sinus rhythm than eye-ball evaluation of 12-lead ECG. Though, the PQ and QT intervals were shorter in KM as compared to 12-lead ECG. Clinical value needs to be verified in large studies, though.

Effect of Implantable vs Prolonged External Electrocardiographic Monitoring on Atrial Fibrillation Detection in Patients With Ischemic Stroke

The relative rates of detection of atrial fibrillation (AF) or atrial flutter from evaluating patients with prolonged electrocardiographic monitoring with an external loop recorder or implantable loop recorder after an ischemic stroke are unknown. To determine, in patients with a recent ischemic stroke, whether 12 months of implantable loop recorder monitoring detects more occurrences of AF compared with conventional external loop recorder monitoring for 30 days. Investigator-initiated, open-label, randomized clinical trial conducted at 2 university hospitals and 1 community hospital in Alberta, Canada, including 300 patients within 6 months of ischemic stroke and without known AF from May 2015 through November 2017; final follow-up was in December 2018. Participants were randomly assigned 1:1 to prolonged electrocardiographic monitoring with either an implantable loop recorder (n = 150) or an external loop recorder (n = 150) with follow-up visits at 30 days, 6 months, and 12 months. The primary outcome was the development of definite AF or highly probable AF (adjudicated new AF lasting ≥2 minutes within 12 months of randomization). There were 8 prespecified secondary outcomes including time to event analysis of new AF, recurrent ischemic stroke, intracerebral hemorrhage, death, and device-related serious adverse events within 12 months. Among the 300 patients who were randomized (median age, 64.1 years [interquartile range, 56.1 to 73.7 years]; 121 were women [40.3%]; and 66.3% had a stroke of undetermined etiology with a median CHA2DS2-VASc [congestive heart failure, hypertension, age ≥75 years, diabetes, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category] score of 4 [interquartile range, 3 to 5]), 273 (91.0%) completed cardiac monitoring lasting 24 hours or longer and 259 (86.3%) completed both the assigned monitoring and 12-month follow-up visit. The primary outcome was observed in 15.3% (23/150) of patients in the implantable loop recorder group and 4.7% (7/150) of patients in the external loop recorder group (between-group difference, 10.7% [95% CI, 4.0% to 17.3%]; risk ratio, 3.29 [95% CI, 1.45 to 7.42]; P = .003). Of the 8 specified secondary outcomes, 6 were not significantly different. There were 5 patients (3.3%) in the implantable loop recorder group who had recurrent ischemic stroke vs 8 patients (5.3%) in the external loop recorder group (between-group difference, -2.0% [95% CI, -6.6% to 2.6%]), 1 (0.7%) vs 1 (0.7%), respectively, who had intracerebral hemorrhage (between-group difference, 0% [95% CI, -1.8% to 1.8%]), 3 (2.0%) vs 3 (2.0%) who died (between-group difference, 0% [95% CI, -3.2% to 3.2%]), and 1 (0.7%) vs 0 (0%) who had device-related serious adverse events. Among patients with ischemic stroke and no prior evidence of AF, implantable electrocardiographic monitoring for 12 months, compared with prolonged external monitoring for 30 days, resulted in a significantly greater proportion of patients with AF detected over 12 months. Further research is needed to compare clinical outcomes associated with these monitoring strategies and relative cost-effectiveness. ClinicalTrials.gov Identifier: NCT02428140.

Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: The Fitbit heart study

Early detection of atrial fibrillation or flutter (AF) may enable prevention of downstream morbidity. Consumer wrist-worn wearable technology is capable of detecting AF by identifying irregular pulse waveforms using photoplethysmography (PPG). The validity of PPG-based software algorithms for AF detection requires prospective assessment. The Fitbit Heart Study (NCT04380415) is a single-arm remote clinical trial examining the validity of a novel PPG-based software algorithm for detecting AF. The proprietary Fitbit algorithm examines pulse waveform intervals during analyzable periods in which participants are sufficiently stationary. Fitbit consumers with compatible wrist-worn trackers or smartwatches were invited to participate. Enrollment began May 6, 2020 and as of October 1, 2020, 455,699 participants enrolled. Participants in whom an irregular heart rhythm was detected were invited to attend a telehealth visit and eligible participants were then mailed a one-week single lead electrocardiographic (ECG) patch monitor. The primary study objective is to assess the positive predictive value of an irregular heart rhythm detection for AF during the ECG patch monitor period. Additional objectives will examine the validity of irregular pulse tachograms during subsequent heart rhythm detections, self-reported AF diagnoses and treatments, and relations between irregular heart rhythm detections and AF episode duration and time spent in AF. The Fitbit Heart Study is a large-scale remote clinical trial comprising a unique software algorithm for detection of AF. The study results will provide critical insights into the use of consumer wearable technology for AF detection, and for characterizing the nature of AF episodes detected using consumer-based PPG technology.

Detection of occult atrial fibrillation with 24-hour ECG after cryptogenic acute stroke or transient ischaemic attack: A retrospective cross-sectional study in a primary care database in Israel

Background Ischaemic stroke or cerebrovascular accident (CVA) due to occult atrial fibrillation (AF) may cause severe morbidity and mortality. Diagnosing occult AF can be challenging and there is no consensus regarding the optimal duration of screening. A 24-hour Holter electrocardiogram (ECG) is frequently employed to detect occult AF following ischaemic CVA. Objectives Demonstration of occult AF detection rate using a 24-hour Holter ECG in a primary care setting with descriptive analyses of independent variables to compare AF detected and non-detected patients. Methods This retrospective cross-sectional study utilised primary care data and included patients 50 years and older with a new CVA or transient ischaemic attack (TIA) diagnosis followed by a 24-hour Holter examination within 6 months, between 01 January 2013 and 01 June 2019. The analyses included descriptive statistics comparing demographics and clinical characteristics in patients who had AF or Atrial Flutter (AFL) detection to those who did not. Results Out of 5015 eligible patients, 66 (1.3%) were diagnosed with AF/AFL, with a number needed to screen of 88.5. Compared with those without AF/AFL detection, those diagnosed were older (75.42 ± 7.89 vs. 69.89 ± 9.88, p = 0.050), had a higher prevalence of hypertension (80.3% vs. 66.8%, p = 0.021) and chronic kidney disease (CKD) (71.2% vs. 44.2%, p < 0.001). Conclusion 24-hour Holter has a low AF/AFL detection rate. Older persons and those with hypertension or CKD are more likely to be detected with AF/AFL using this method.

Early Detection of Cascade Flutter in a Model Aircraft Turbine Using a Methodology Combining Complex Networks and Synchronization

We report an experimental study on the early detection of cascade flutter in a model aircraft turbine using a methodology that combines complex networks and synchronization. The network topology constructed from the determinism in the cross-recurrence plot allows us to extract the primary hubs during a transition to cascade flutter. The node strength in the network topology and the synchronization parameter are valid for capturing a precursor of cascade flutter.