scholarly journals Pause-triggered episode classification algorithms and high-priority review queue concept for cardiac monitoring devices

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
G Rajagopal ◽  
S.R Landman
Keyword(s):  
Review Process ◽  
Normal Sinus Rhythm ◽  
Priority Queue ◽  
Cardiac Monitoring ◽  
Validation Dataset ◽  
Priority Review ◽  
Funding Source ◽  
Private Company ◽  
Normal Sinus ◽  
Monitoring Center

Abstract Background Cardiac monitoring technologies often utilize monitoring centers or services in which technicians adjudicate arrhythmia episodes. Arrhythmias with a high prevalence of false detections (such as pauses) can slow down the review process while also delaying review of time-sensitive true episodes. Objective To develop algorithms that can stratify device-detected pause episodes into low- and high-priority queues to facilitate monitoring center review. Methods The high priority queue algorithm identifies episodes which are likely to be true pause episodes. The algorithm consists of four conditions, which identify features that were found to be predictive of true pauses, including the number of beats in the episode, the noise status of the last pre-pause beat, and the relative flatness of the ECG signal. Using a similar set of features, a set of criteria was determined that would identify pause-triggered episodes that were highly unlikely to be true pauses. 11,567 pause episodes were used for development with 19,520 separate episodes used for validation. All episodes were adjudicated by a cardiac monitoring center. The validation dataset consisted of 18,280 (93.6%) false pauses and 1240 (6.4%) true pauses. Results The high-priority queue algorithm identified true pause episodes with a sensitivity of 82.3% and specificity of 96.8% in the validation dataset (see table). The low-priority queue algorithm flagged 78.4% of all pause episodes as low-priority in the validation dataset, with only 4 true pause episodes (<0.1%) flagged as low priority. Conclusion The high-priority queue algorithm for device detected pause episodes could potentially identify ∼82% of all true pause notifiable episodes, expediting their review process. The low-priority queue successfully identifies a large percentage (∼80%) of false pause-triggered episodes, which would help to improve monitoring center efficiency as false pause-triggered episodes are a large driver of artifact / normal sinus rhythm episodes. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Medtronic

Fully automated contrast and non-contrast cardiac view detection in echocardiography a multi-centre, multi-vendor study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Gao ◽  
D Stojanovski ◽  
A Parker ◽  
P Marques ◽  
S Heitner ◽  
...  
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Classification Model ◽  
Validation Dataset ◽  
Funding Source ◽  
Private Company ◽  
Validation Data ◽  
Independent Test ◽  
Model Training ◽  
Confusion Matrices

Abstract Background Correctly identifying views acquired in a 2D echocardiographic examination is paramount to post-processing and quantification steps often performed as part of most clinical workflows. In many exams, particularly in stress echocardiography, microbubble contrast is used which greatly affects the appearance of the cardiac views. Here we present a bespoke, fully automated convolutional neural network (CNN) which identifies apical 2, 3, and 4 chamber, and short axis (SAX) views acquired with and without contrast. The CNN was tested in a completely independent, external dataset with the data acquired in a different country than that used to train the neural network. Methods Training data comprised of 2D echocardiograms was taken from 1014 subjects from a prospective multisite, multi-vendor, UK trial with the number of frames in each view greater than 17,500. Prior to view classification model training, images were processed using standard techniques to ensure homogenous and normalised image inputs to the training pipeline. A bespoke CNN was built using the minimum number of convolutional layers required with batch normalisation, and including dropout for reducing overfitting. Before processing, the data was split into 90% for model training (211,958 frames), and 10% used as a validation dataset (23,946 frames). Image frames from different subjects were separated out entirely amongst the training and validation datasets. Further, a separate trial dataset of 240 studies acquired in the USA was used as an independent test dataset (39,401 frames). Results Figure 1 shows the confusion matrices for both validation data (left) and independent test data (right), with an overall accuracy of 96% and 95% for the validation and test datasets respectively. The accuracy for the non-contrast cardiac views of >99% exceeds that seen in other works. The combined datasets included images acquired across ultrasound manufacturers and models from 12 clinical sites. Conclusion We have developed a CNN capable of automatically accurately identifying all relevant cardiac views used in “real world” echo exams, including views acquired with contrast. Use of the CNN in a routine clinical workflow could improve efficiency of quantification steps performed after image acquisition. This was tested on an independent dataset acquired in a different country to that used to train the model and was found to perform similarly thus indicating the generalisability of the model. Figure 1. Confusion matrices Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Ultromics Ltd.

Can left atrial mechanics predict anticoagulation in cryptogenic stroke?

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Azul Freitas ◽  
J Milner ◽  
J Ferreira ◽  
C Ferreira ◽  
S Martinho ◽  
...  
Keyword(s):  
Longitudinal Strain ◽  
Normal Sinus Rhythm ◽  
Cryptogenic Stroke ◽  
Cardiac Monitoring ◽  
Western World ◽  
Normal Sinus ◽  
Cardiac Evaluation ◽  
Male Preponderance ◽  
Left And Right ◽  
Left Atrial Mechanics

Abstract Introduction Ischemic stroke is a leading cause of death and disability in the Western world, frequently due to cardioembolism and atherothromboembolism. Cryptogenic strokes occur without a well-defined aetiology after a standard vascular and cardiac evaluation, and secondary prevention may include antiplatelet therapy while awaiting results of long-term cardiac monitoring. In this study, we aimed to identify echocardiographic predictors of paroxysmal atrial fibrillation (AF) latter identified in follow-up of patients with cryptogenic stroke. Methods We retrospectively assessed all patients with cryptogenic stroke admitted in our hospital in the last 2 years. Only patients in normal sinus rhythm with a minimum of 24 hours of cardiac monitoring at admission and 24 hours Holter monitor within 6 months after discharge were included. Echocardiographic measures included left ventricle ejection fraction, left atrium (LA) volume, left and right atrium longitudinal strain, left and right ventricle longitudinal strain, E/A ratio, E/e' ratio, isovolumetric relaxation time (IVRT) and E wave deacceleration time. Echocardiographic data was assessed to determine its accuracy to identify AF. Results The study included 32 patients with a mean age of 72±10 years and a male preponderance (87.5%). AF was identified in 12 (37.5%) patients. This group of patients had a larger indexed LA volume (44.3 vs 29.1 mL/m2, p=0.043), a lower IVRT (87 vs 116 ms, p=0.028), and a lower LA longitudinal strain in contractile (6.7 vs 13.6%, p<0.001) and in reservoir phase (17.1 vs 23.6%, p=0.042). All other variables were not significantly different among groups, including LA longitudinal strain in conduit phase. LA longitudinal strain in contractile phase showed the best predictive power with an area under the ROC curve of 0.925 (95% CI 0.82–1 p=0.001). The cut-off value that best predicted AF was 8.17% with a sensitivity of 1 and specificity of 0.9. Conclusion LA longitudinal strain in contractile phase is a powerful method to identify AF in cryptogenic stroke. When reduced, anticoagulation may be considered in order to prevent recurrence. Further studies are warranted to reproduce these results in larger cohorts. Funding Acknowledgement Type of funding source: None

scholarly journals Prediction of atrial fibrillation from normal ECG using artificial intelligence in patients with unexplained stroke

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
Y.S Baek ◽  
S.C Lee ◽  
W.I Choi ◽  
D.H Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Statistical Significance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Normal Sinus ◽  
Group 2

Abstract Background Stroke related to embolic and of undetermined source constitute 20 to 30% of ischemic strokes. Many of these strokes are related to atrial fibrillation (AF), which might be underdetected due to its paroxysmal and silent nature. Purpose The aim of our study was to predict AF during normal sinus rhythm in a standard 12-lead ECG to train an artificial intelligence to train deep neural network in patients with unexplained stroke (embolic stroke of undetermined source; ESUS). Methods We analyzed digital raw data of 12-lead ECGs using artificial intelligence (AI) recurrent neural network (RNN) to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 12-lead ECGs. We included 2,585 cases aged 18 years or older with multiple ECGs at our university hospital between 2005 and 2017 validated by crossover analysis of two electrophysiologists. We defined the first recorded AF ECG as the index ECG and the first day of the window of interest as 14 days before the date of the index ECG. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated recall, F1 score, and the area under the curve (AUC) of the receiver operatoring characteristic curve (ROC) for the internal validation dataset to select a probability threshold. We applied this developed AI program to 169 ESUS patients who has been diagnosed and had standard 12-lead ECGs in our hospital. Results We acquired 1,266 NSR ECSs from real normal subjects and 1,319 NSR ECGs form paroxysmal AF patients. RNN AI-enabled ECG identified atrial fibrillation with an AUC of 0.79, recall of 82%, specificity of 78%, F1 score of 75% and overall accuracy of 72.8% (Figure). ESUS patients were divided into three groups according to calculated probabilities of AF using AI guided RNN program: group 1 (35 patients with probability of 0–25% of paroxysmal AF), group 2 (86 patients with probability of 25–75% of paroxysmal AF) and group 3 (48 patients with probability of 75–100% of paroxysmal AF). In Kaplan-Meier estimates, Group 2 and 3 (more than 25% of PAF probabilities) tended to have higher AF incidence although it did not reach statistical significance (log-rank p 0.678) (Figure). Conclusion AI may discriminate subtle changes between real and paroxysmal NSR and can also be helpful in patients with ESUS to identify if AF is the underlying cause of the stroke. Further studies are needed in order to evaluate their possible use in future prognostic models. Funding Acknowledgement Type of funding source: None

scholarly journals Electrical injury - induced atrial fibrillation: A case report

2021 ◽  
pp. 85-85
Author(s):  
Milovan Stojanovic ◽  
Bojan Ilic ◽  
Marina Deljanin-Ilic ◽  
Stevan Ilic
Keyword(s):  
Atrial Fibrillation ◽  
Case Report ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Head Injuries ◽  
Cardiac Monitoring ◽  
Electrical Injury ◽  
Normal Sinus ◽  
Entry Wound ◽  
Wall Motion Abnormalities

Introduction: Electrical injury can cause various cardiac dysrhythmias such as asystole, ventricular fibrillation, sinus tachycardia, and heart blocks. However, it rarely causes atrial fibrillation. Case report: Patient S.M, born in Nis in 1973, was admitted to the emergency department after receiving an electric shock (<600 V). He subsequently lost consciousness, fell down, and sustained back and head injuries. During the examination heart rate was irregular but with no heart murmurs. There was an entry wound on the front of the left thigh and an exit wound on the front of the neck. An electrocardiogram showed newly appearing atrial fibrillation. The laboratory tests showed no pathological deviation and focus cardiac ultrasound showed that contractile force was preserved with no wall-motion abnormalities and normal left atrium dimensions. The patient was administered low-molecular-weight heparin subcutaneously and propafenone (600 mg) orally. At follow up after 24 hours, an electrocardiogram showed normal sinus rhythm. Conclusion: We report a rare case of an electrical injury-induced atrial fibrillation, which was converted to sinus rhythm by pocket therapy. Although most cases of an electrical injury-induced AF represent benign conditions which are self-limited, cardiac monitoring as a routine measure should be considered.

scholarly journals Cost comparison of conventional monitoring strategies to diagnose syncope patients with pause arrhythmias relative to insertable cardiac monitoring: a simulation approach

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J.D Rogers ◽  
S Rosemas ◽  
P.D Ziegler ◽  
Y.-J Cheng ◽  
L Higuera
Keyword(s):  
Cost Comparison ◽  
Cardiac Monitoring ◽  
Funding Source ◽  
Fixed Costs ◽  
Ecg Monitoring ◽  
Private Company ◽  
Patient Pathways ◽  
Number Of Patients ◽  
The Cost

Abstract Background The infrequent nature of syncope can make diagnosis of cardiac pauses challenging with conventional monitoring (CM) strategies of intermittent external short-term ECG monitoring. Insertable cardiac monitors (ICMs) continuously monitor for arrhythmias and are well-established to have a higher likelihood of diagnosis compared to CM. It is not well understood whether the higher up-front cost of ICM is offset by the cost of repeat evaluation in a CM strategy, per diagnosed patient. Purpose The objective of this analysis was to simulate the cost per patient diagnosed with pause arrhythmias, between various CM strategies and ICM monitoring. Methods ICM device data from syncope patients was utilized to simulate patient pathways with CM. We assumed that detected true pause episodes (≥5 seconds) were symptomatic and prompted further evaluation: simulated inpatient or outpatient emergency department (ED) assessment, followed by external ECG monitoring of varying durations (24 or 48 hours, 14 or 30 days, or two 30-day monitors) beginning at random within the week after discharge. Subsequent true pause episodes in yet undiagnosed patients triggered additional rounds of CM. ECG diagnosis was considered successful if a pause episode occurred on the same day as simulated CM. Costs of evaluation and monitoring were accrued at each encounter. Inpatient and outpatient (ED) syncope evaluation costs (mean £3,746 and £367, respectively) were based on national episode statistics data and national average tariffs, and simulated at random from log-normal distributions; costs of external ECG monitors and ICM (including ICM device, insertion, remote monitoring, and explant) were fixed. Costs stopped accruing once a patient was diagnosed. We computed costs per diagnosed patient by dividing the total costs accrued for all patients by the number of patients diagnosed across 1,000 simulations. Longer pause definitions of ≥6–7 seconds were also evaluated. Results A total of 105 true pause episodes from 44 patients (mean (SD) age 66 (17), 48% male) were detected by ICM during 505 (333) days of follow-up. Patients experienced an average of 2.4 (2.7) pause episodes ≥5 seconds during follow-up. Relative to ICM-diagnosed patients, CM diagnosed between 13.8% (24-hour holter) to 30.2% (two 30-day monitors) of these patients. Cost per ICM-diagnosed patient was £2,985, whereas in the CM strategies the average cost per diagnosed patient ranged from £18,519 (£7,603) with 24-hour holter to £5,656 (£890) for two 30-day monitors (Figure). Costs per diagnosed patient further increased for pause durations of ≥6–7s, as the percent of patients diagnosed via CM decreased. Conclusion Relative to syncope patients diagnosed with pause arrhythmias via ICM, CM strategies diagnose fewer patients and incur significantly greater costs per diagnosed patient. Real-world suboptimal patient compliance with external monitoring would further decrease the cost-effectiveness of CM. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Medtronic

Arrhythmias assessed with long-term continuous cardiac monitoring in pulmonary hypertension

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M.O.E.A Oerbaek Andersen ◽  
S.Z.D Diederichsen ◽  
J.H.S Hastrup Svendsen ◽  
J.C Carlsen
Keyword(s):  
Pulmonary Hypertension ◽  
Retrospective Studies ◽  
Chronic Thromboembolic Pulmonary Hypertension ◽  
Heart Rhythm ◽  
Cardiac Monitoring ◽  
Funding Source ◽  
Private Company ◽  
Clinical Role ◽  
Period Prevalence

Abstract Background Arrhythmias are considered a prominent phenomenon in pulmonary hypertension (PH) as the disease progresses. In short-term retrospective studies with up to 24 hours of monitoring, supraventricular tachycardia (SVT) has been found in 8–35% of patients, with significant impact on survival. The incidence of arrhythmias and their clinical role is based on a small number of retrospective studies. Purpose Assessment of arrhythmias in PH by continuous long-term cardiac monitoring Methods Patients diagnosed with PH were included in this prospective single-centre study where the diagnosis of PAH was established according to international guidelines. Patients received a Reveal LINQ (Medtronic) insertable cardiac monitor (ICM). ICM's continuously monitor the heart rhythm for arrhythmias according to algorithms; and an ECG of the episode will automatically be transmitted whenever an arrhythmia is detected. Results Twenty-eight patients were included, of whom 24 patients had pulmonary arterial hypertension (PAH) and four had chronic thromboembolic pulmonary hypertension (table 1) treated according to Table 1. During 168 patient-months of continuous monitoring (median: 206 days per patient) no long-lasting arrhythmias were observed, but only transient SVTs in one patient lasting a total of 2.5 days. Two had SVTs lasting 4.5 hours, and two hours. Five patients had isolated episodes of SVTs lasting 0–10 minutes and two patients had an episode of bradycardia for 10 seconds. The total arrhythmic burden was a period prevalence of less than 0.001%, despite a RVEF of 20.3% and 35.7% in WHO FC IV and III, respectively. Conclusion This is the first study with long-term continuous rhythm monitoring in patients with PH. In contrast to earlier observations no long-lasting arrhythmias occurred during 168 patient-months with a period prevalence of arrhythmias as low as less than 0.001% in this cohort of PH patients with WHO FC I-IV and the majority on modern specific PAH therapy. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Actelion

scholarly journals Premature ventricular contraction detection system from the 12 leads ECG by artificial intelligence

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
Y Kim ◽  
J.H Kim ◽  
M.Y Lee ◽  
Y.K Kim
Keyword(s):  
Detection System ◽  
Normal Sinus Rhythm ◽  
Premature Ventricular Contraction ◽  
Heart Rhythm ◽  
Digital Signal ◽  
Ecg Signal ◽  
Funding Source ◽  
Ventricular Contraction ◽  
Normal Sinus ◽  
Baseline Wandering

Abstract Background Electrocardiographs (ECG) are obtained by a digital signal. However, they are still printed out of paper to read by physicians. Digitizing the analog ECG from the paper to digital signal make us much easier to access of bid data pool form the daily clinical practice and previous resources. Objective The goal of this study is to digitize paper ECG to detect premature ventricular contraction (PVC). Methods This system consists of 2 steps; digitization and PVC detection. Results First, for digitization, ECG are filtered by the specific cut-off value of red, green and blue, then the filtered ECG image is changed into gray scale. In order to extract ECG signal, the algorithm fine the only one of the biggest white body throughout the X-axis. The X and Y axis is matched with distance and amplitude, depending on dots per inch (DPI). Second, to detect PVC, ECG signal is filtered to eliminate baseline wandering. The characteristics of PVC is higher amplitude and longer duration than normal sinus rhythm, we set two criteria to detect the PVC: 1.5 times the duration, 1.2 points out of the amplitude. For the synchronization of timing, lead II rhythm strip was used by PVC detection and then the rest of 12-lead ECG is matched based on lead II synchronization (Figure 1). We applied this algorithm to the 300 real patient's ECG. 290 of 300 (96.7%) ECG are successfully digitized signal and PVC detection. Conclusion We successfully developed the algorithm analog ECG signal into digital signal to detect PVC. In the future, this method helps to gather big data from ECG papers to develop a new algorithm to localization of PVC. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Korean Heart Rhythm Society 2019 Research Fund

Projecting the long-term benefits of single pill combination therapy for patients with hypertension in five countries

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C Borghi ◽  
J.G Wang ◽  
A.V Rodionov ◽  
M Rosas ◽  
I.S Sohn ◽  
...  
Keyword(s):  
Combination Therapy ◽  
South Korea ◽  
Health Outcomes ◽  
Clinical Outcomes ◽  
Funding Source ◽  
Combination Therapies ◽  
Private Company ◽  
Treatment Pathways ◽  
Single Pill ◽  
The Impact

Abstract Background It is well established that single pill combination (SPC) therapies have the potential to improve patient adherence versus multi-pill regimens, thereby improving blood pressure control and clinical outcomes in populations with hypertension. Purpose To develop a microsimulation model, capturing different treatment pathways, to project the impact on clinical outcomes of using single pill combination therapies for the management of hypertension in five countries (Italy, Russia, China, South Korea and Mexico). Methods The model was designed to project health outcomes between 2020 and 2030 for populations with hypertension managed according to four different treatment pathways: current treatment practices [CTP], single drug with dosage titration first then sequential addition of other agents [start low and go slow, SLGS], free choice combination with multiple pills [FCC] and combination therapy in the form of a single pill [SPC]. Model inputs were derived from Global Burden of Disease 2017 dataset, including demographics, health status/risk factors, transition probabilities and treatment attributes/healthcare utilization, and the model incorporated real-world challenges to healthcare delivery such as access to care, SBP measurement error, adherence and therapeutic inertia. Simulated outcomes of mortality, incidence of chronic kidney disease (CKD), stroke and ischemic heart disease (IHD), and disability-adjusted life years (DALYs) due to these conditions were estimated for population of 1,000,000 simulated patients for each treatment pathway and country. Results SPC therapy was projected to improve health outcomes over SLGS, FCC and CTP over 10 years in all five countries. SPC was forecast to reduce mortality by 5.4% (Italy), 4.9% (Russia), 4.5% (China), 2.3% (South Korea) and 3.6% (Mexico) versus CTP and showed greater projected reductions in mortality than SLGS and FCC. DALYs were projected to be reduced with SPC therapy by between 5.7% (Italy) and 2.2% (South Korea) compared with CTP and reductions in the incidence of clinical events were also projected with SPC therapy, with decreases in the range of 11.5% (Italy) to 4.9% (South Korea) versus CTP. Conclusions Ten-year projections of clinical outcomes associated with different anti-hypertensive treatment pathways in five countries indicated that both combination therapies (FCC and SPC) are likely to reduce the disease burden of hypertension compared with conventional management approaches, with SPC showing the greatest overall benefits due to improved adherence. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Sanofi, Gentilly, France

Edoxaban treatment of elderly patients with atrial fibrillation in routine clinical practice: 1-year results of the non-interventional Global ETNA-AF program

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Yamashita ◽  
C.C Wang ◽  
Y.-H Kim ◽  
R De Caterina ◽  
P Kirchhof ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Clinical Practice ◽  
Major Bleeding ◽  
Intracranial Haemorrhage ◽  
Clinical Care ◽  
Oral Anticoagulants ◽  
Clinical Event ◽  
Funding Source ◽  
Private Company ◽  
All Cause Mortality

Abstract Background The prevalence of atrial fibrillation (AF) and the need for appropriate anticoagulation increase with age. The benefit/risk profile of direct oral anticoagulants such as edoxaban in elderly population with AF in regular clinical practice is therefore of particular interest. Purpose Analyses of Global ETNA-AF data were performed to report patient characteristics, edoxaban treatment, and 1-year clinical events by age subgroups. Methods Global ETNA-AF is a multicentre, prospective, noninterventional program conducted in Europe, Japan, Korea, Taiwan, and other Asian countries. Demographics, baseline characteristics, and 1-year clinical event data were analysed in four age subgroups. Results Of 26,823 patients included in this analysis, 50.4% were ≥75 years old and 11.6% were ≥85 years. Increase in age was generally associated with lower body weight, lower creatinine clearance, higher CHA2DS2-VASc and HAS-BLED scores, and a higher percentage of patients receiving the reduced dose of 30 mg daily edoxaban. At 1-year, rates of ISTH major bleeding and ischaemic stroke were generally low across all age subgroups. The proportion of intracranial haemorrhage within major bleeding events was similar across age groups. All-cause mortality increased with age more than cardiovascular mortality. Conclusion Data from Global ETNA-AF support the safety and effectiveness of edoxaban in elderly AF patients (including ≥85 years) in routine clinical care with only a small increase in intracranial haemorrhage. The higher all-cause mortality with increasing age is not driven by cardiovascular causes. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Daiichi Sankyo

scholarly journals Atrial dilation as a substrate for atrial fibrillation in the canine complete chronic atrioventricular block model

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Smoczynska ◽  
H.D.M Beekman ◽  
R.W Chui ◽  
S Rajamani ◽  
M.A Vos
Keyword(s):  
Atrial Fibrillation ◽  
Atrioventricular Block ◽  
Volume Overload ◽  
Right Atrial ◽  
Funding Source ◽  
Private Company ◽  
Structural Remodeling ◽  
2D Echocardiography ◽  
Atrial Enlargement ◽  
The Right

Abstract Background Atrial fibrillation (AF) is the most common cardiac arrhythmia treated in clinical practice. Structural remodeling is characterized by atrial enlargement and contributes to the therapeutic resistance in patients with long-standing AF. Purpose To study the atrial arrhythmogenic and echocardiographic consequences induced by volume overload in the complete chronic atrioventricular block (CAVB) dog. Methods Echocardiographic and electrophysiological data was obtained in 14 anaesthetized Mongrel dogs, in acute AV-block (AAVB), after 6 weeks of CAVB (CAVB6) and CAVB10. Left atrial (LA) volume was determined with 2D echocardiography by using the biplane method. An electrocardiogram and monophasic action potentials (MAP) at the right atrial (RA) free wall were recorded. Atrial effective refractory period (AERP) was determined by continuous programmed electrical stimulation (PES) of 20 beats with a cycle length of 400 ms and an extrastimulus with decremental design until refractoriness was reached. A continuous PES protocol of 20 beats with an extrastimulus 5 ms longer than the AERP was applied for 150 seconds to trigger AF. After 5 min without arrhythmias, autonomic neuromodulation was performed by intravenous infusion (IV) of acetylcholine (1,5μg/kg/min to 6,0μg/kg/min) for 20 min followed by prompt IV infusion of isoprenaline (3μg/min) until the atrial heart rate increased by 20 bpm. PES with an extrastimulus was repeated for 150 seconds to induce AF. Results LA volume increased from 13.7±3.2 ml at AAVB to 20.5±5.9 ml* at CAVB6, and 22.7±6.0 ml* at CAVB10 (Fig. 1A). AERP was similar at AAVB, CAVB6, and CAVB10 (115.8±11.9, 117.3±11.7, and 106.8±12.1 ms respectively). Repetitive AF paroxysms of &gt;10 seconds were induced in 1/14 (7%) dogs at AAVB, 1/11 (9%) at CAVB6, and 5/10 (50%)* at CAVB10 (*p&lt;0.05) upon PES (Fig. 1B). Combined neuromodulation and PES did not increase the AF inducibility rate, but prolonged the longest episode of AF in the inducible dogs from 55±49 seconds to 236±202 seconds* at CAVB10 (Fig. 1C). LA volume was higher in inducible dogs 25.0±4.9 ml compared to 18.4±4.2 ml in non-inducible dogs at CAVB10. Conclusion Sustained atrial dilation forms a substrate for repetitive paroxysms of AF. Neuro-modulation prolongs AF episode duration in susceptible dogs. This animal model can be used to study structural remodeling of the atria and possible therapeutic advances in the management of AF. Figure 1 Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Amgen Research

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