Lorenz Plot Analysis in Dogs with Sinus Rhythm and Tachyarrhythmias

The Lorenz plot (LP), a graphical representation of heart rate variability, has been poorly studied in dogs to date. The present study aimed to describe the graphic features of LP in dogs with sinus rhythm (SR) and tachyarrhythmias, and to analyze the usefulness of its pattern recognition. One hundred and nineteen canine Holter recordings were retrospectively evaluated. Cardiac rhythms were classified as: SR; SR with frequent (>100) premature ectopies (atrial, SR-APCs; ventricular, SR-VPCs; atrial and ventricular, SR-APCs-VPCs); atrial fibrillation (AF); and AF with frequent VPCs (AF-VPCs). Lorenz plots were studied qualitatively and quantitatively, and classified by distinct LP patterns (LPPs). Repeatability and reproducibility of LPP classification and diagnostic value were determined. Recordings included: 48 SR, 9 SR-APCs, 35 SR-VPCs, 5 SR-APCs-VPCs, 4 AF, and 18 AF-VPCs. Ten LPPs were identified: comet (n = 12), torpedo (n = 3), Y-shaped (n = 6), diamond (n = 10), diamond with a central silent zone (n = 17), double side-lobe (DSL) (n = 47), triple side-lobe (n = 1), quadruple side-lobe (n = 2), fan (n = 18), and fan with DSL (n = 3). Repeatability and reproducibility of LPP classification were excellent. The DSL pattern was both highly sensitive (91.3%) and specific (94.5%) for SR with frequent premature ectopies, either APCs, or VPCs, or both. The remaining LPPs had lower diagnostic value (high specificity but low sensitivity). Distinct rhythms imprint distinct and reproducible LPPs in dogs. The majority of canine LPPs are specific but insensitive indicators of SR and tachyarrhythmias.

Using Three-Dimensional Lorenz Scatter Plots to Detect Patients with Atrioventricular Node Double Path Caused by Interpolated Ventricular Premature Systoles: A Case Study

A series of related electrophysiology phenomena can be caused by the occurrence of interpolated ventricular prematurecontraction. In our recent three-dimensional Lorenz R-R scatter plot research, we found that atrioventricular nodedouble path caused by interpolated ventricular premature contraction imprints a specific pattern on three-dimensionalLorenz plots generated from 24-hour Holter recordings. We found two independent subclusters separated from the interpolated premature beat precluster, the interpolated premature beat cluster, and the interpolated premature beat postcluster, respectively. Combined with use of the trajectory tracking function and the leap phenomenon, our results reveal the presence of the atrioventricular node double conduction path.

Short‐Term Variability of the QT Interval Can be Used for the Prediction of Imminent Ventricular Arrhythmias in Patients With Primary Prophylactic Implantable Cardioverter Defibrillators

Background Short‐term variability of the QT interval (STV QT ) has been proposed as a novel electrophysiological marker for the prediction of imminent ventricular arrhythmias in animal models. Our aim is to study whether STV QT can predict imminent ventricular arrhythmias in patients. Methods and Results In 2331 patients with primary prophylactic implantable cardioverter defibrillators, 24‐hour ECG Holter recordings were obtained as part of the EU‐CERT‐ICD (European Comparative Effectiveness Research to Assess the Use of Primary Prophylactic Implantable Cardioverter Defibrillators) study. ECG Holter recordings showing ventricular arrhythmias of >4 consecutive complexes were selected for the arrhythmic groups (n=170), whereas a control group was randomly selected from the remaining Holter recordings (n=37). STV QT was determined from 31 beats with fiducial segment averaging and calculated as , where D n represents the QT interval. STV QT was determined before the ventricular arrhythmia or 8:00 am in the control group and between 1:30 and 4:30 am as baseline. STV QT at baseline was 0.84±0.47 ms and increased to 1.18±0.74 ms ( P <0.05) before the ventricular arrhythmia, whereas the STV QT in the control group remained unchanged. The arrhythmic patients were divided into three groups based on the severity of the arrhythmia: (1) nonsustained ventricular arrhythmia (n=32), (2) nonsustained ventricular tachycardia (n=134), (3) sustained ventricular tachycardia (n=4). STV QT increased before nonsustained ventricular arrhythmia, nonsustained ventricular tachycardia, and sustained ventricular tachycardia from 0.80±0.43 ms to 1.18±0.78 ms ( P <0.05), from 0.90±0.49 ms to 1.14±0.70 ms ( P <0.05), and from 1.05±0.22 ms to 2.33±1.25 ms ( P <0.05). This rise in STV QT was significantly higher in sustained ventricular tachycardia compared with nonsustained ventricular arrhythmia (+1.28±1.05 ms versus +0.24±0.57 ms [ P <0.05]) and compared with nonsustained ventricular arrhythmia (+0.34±0.87 ms [ P <0.05]). Conclusions STV QT increases before imminent ventricular arrhythmias in patients, and the extent of the increase is associated with the severity of the ventricular arrhythmia.

Short coupling with high burden of atrial ectopy in twenty-four hour holter recording predicts recurrence of atrial arrhythmia after atrial fibrillation ablation

Background Atrial arrhythmia recurrence is experienced in up to 20% of patients after initially receiving a catheter ablation for atrial fibrillation (AF). Therefore, it is important to define predictors of atrial arrhythmia recurrence. Atrial ectopy (AE) with short coupling interval (S-AE) has been reported to be a trigger of AF. On the other hand, high burden of AE has been reported to be a useful predictor of atrial arrhythmia recurrences after AF ablation. Thus, the combination of the incidence of S-AE and AE burden during a 24-hour Holter recording could be a useful predictor of atrial arrhythmia recurrence after AF ablation. Purpose To investigate this hypothesis, we performed a retrospective case-controlled study. Methods We enrolled 180 patients who underwent their first catheter ablation procedure for AF and performed a 24-hour Holter recording between 90 to 365 days after their ablation procedure. Patients who performed an additional ablation procedure before the Holter recording were excluded. Finally, we analyzed 173 patients (age: 65±10 years, female: 28.3%, non-paroxysmal: 27.7%). The Holter recordings were analyzed by the same experienced technicians. We defined AE as a narrow QRS complex occurring &gt;25% than prior R-R interval, and S-AE as AE occurring &gt;55% earlier than expected. The relationship between the characteristics of AE during the Holter recording and atrial arrhythmia recurrences was investigated. Results The Holter recordings were performed at a median of 103 (IQR: 98–138) days after ablation. The median number of AE were 144 (IQR: 54–699) beats per day, and S-AE was recorded in 49 patients (28.3%). Forty-two patients (24.3%) had a recurrence of atrial arrhythmia during a median 488-day follow up period. Patients with S-AE had a recurrence of atrial arrhythmia more frequently than those without S-AE (44.9% vs 16.1%, p&lt;0.001). We found the cut-off point of AE burden as 241 beats per day by the receiver operating characteristic curve with 74% sensitivity and 70% specificity to predict atrial arrhythmia recurrence. We divided the patients into four groups according to the presence or absence of S-AE and high AE burden. In the Kaplan-Meier analysis, patients with S-AE and high AE burden had the highest atrial arrhythmia recurrence rate (Log-rank test: p&lt;0.001). In the Cox multivariate analysis, S-AE with high AE burden was an independent predictor of atrial arrhythmia recurrence (HR: 4.27, 95% CI: 2.32–7.85, p&lt;0.001). Conclusion For AF patients who underwent their first catheter ablation, S-AE (&gt;55% earlier than expected) with high AE burden (&gt;241 beats per day) during the 24-hour Holter recording predicted recurrences of atrial arrhythmia. These results can help to develop follow-up strategies after AF ablation. Funding Acknowledgement Type of funding source: None

Nurse Led Smartphone Electrographic Monitoring for Atrial Fibrillation after Ischemic Stroke: SPOT-AF

Background and Purpose Paroxysmal atrial fibrillation (PAF) underlying acute stroke frequently evades detection by standard practice, considered to be a combination of routine electrocardiogram (ECG) monitoring, and 24-hour Holter recordings. We hypothesized that nurse-led in-hospital intermittent monitoring approach would increase PAF detection rate.Methods We recruited patients hospitalised for stroke/transient ischemic attack, without history of atrial fibrillation (AF), in a prospective multi-centre observational study. Patients were monitored using a smartphone-enabled handheld ECG (iECG) during routine nursing observations, and underwent 24-hour Holter monitoring according to local practice. The primary outcome was comparison of AF detection by nurse-led iECG versus Holter monitoring in patients who received both tests: secondary outcome was oral anticoagulant commencement at 3-month following PAF detection.Results One thousand and seventy-nine patients underwent iECG monitoring: 294 had iECG and Holter monitoring. AF was detected in 25/294 (8.5%) by iECG, and 8/294 (2.8%) by 24-hour Holter recordings (P<0.001). Median duration from stroke onset to AF detection for iECG was 3 days (interquartile range [IQR], 2 to 6) compared with 7 days (IQR, 6 to 10) for Holter recordings (P=0.02). Of 25 patients with AF detected by iECG, 11 were commenced on oral anticoagulant, compared to 5/8 for Holter. AF was detected in 8.8% (69/785 patients) who underwent iECG recordings only (P=0.8 vs. those who had both iECG and 24-hour Holter).Conclusions Nurse-led in-hospital iECG surveillance after stroke is feasible and effective and detects more PAF earlier and more frequently than routine 24-hour Holter recordings. Screening with iECG could be incorporated into routine post-stroke nursing observations to increase diagnosis of PAF, and facilitate institution of guideline-recommended anticoagulation.

222AI-based strategy enables faster Holter ECG analysis with equivalent clinical accuracy compared to a classical strategy

BACKGROUND Analysis of Holter recordings can be challenging and time-consuming, therefore requiring significant clinical resources in order to achieve a high-quality diagnosis. Such resources depend largely on the qualifications of the person conducting the analysis and the duration of the recordings. A novel Holter analysis platform has been developed, based on deep neural networks trained with a dataset of one million ECGs, to allow fast and reliable Holter recording analysis. PURPOSE This study sought to compare the performance of an artificial intelligence (AI)-based Holter analysis platform using deep learning tools with a classical one used on a daily basis in hospitals (the reference). The main endpoints evaluated were duration to complete the analysis by the physician operating it as well as diagnostic accuracy of each strategy, when platforms are used by electrophysiologists (EPs). METHODS For this prospective evaluation, a total of 159 Holter recordings (24-hour) were selected from a large Holter dataset from 1 hospital, with a relatively high prevalence of electrical rhythm and conduction disorders. Recordings were analysed by four EPs using independently both the AI-based and classical analysis platforms. All four EPs had no previous experience with the AI-based platform, except for an introductory 6-hour training session. Three EPs had multiple years of experience with the traditional platform, while one EP had limited experience. For each recording, in addition to the analysis duration, diagnostic accuracy was evaluated through the analysis of the presence or absence of predefined cardiac arrhythmias and conduction disorders (prevalence): pauses (25.2%), ventricular tachycardia (VT, 30.2%), atrial fibrillation (AF, 26.4%), high grade atrioventricular block (AVB, 10.1%) and burden of premature ventricular complex larger than 10% (PVC, 23.9%). Definite diagnostics were established by an expert EP after a careful examination of all available analysis reports. RESULTS Time required for the AI-based analysis was on average 42% shorter compared to the traditional platform (6.65 min vs 11.5 min, p &lt; 0.0001). Regarding accuracy to detect electrical disorders, there was no statistically significant differences between AI-based and classical platforms (AF: 98.7% vs 96.9%, Pause: 99.4% vs 100%, PVC: 98.7% vs 98.7%, VT: 92.5% vs 96.2%, AVB: 98.7% vs 94.3%). CONCLUSION: These preliminary findings suggest that an AI-based strategy to analyse Holter recordings may be highly accurate in detecting cardiac electrical abnormalities, with significant time savings compared to a classical strategy, even for users with no previous experience with the novel AI-based platform. An AI-based Holter analysis platform may contribute to a broader and more resource-efficient adoption of Holter monitoring. Abstract Figure. analysis duration using each strategy