Development of a Customizable Pacing Protocol to Induce Persistent Atrial Fibrillation in Swine
Bravo, F.; Ref, J.; Riemenschneider, J.; Lefkowitz, E.; Mostafizi, P.; Salciccioli, A.; DiPonio, S.; Grijalva, A.; Benson, D.; Tulino, A. S.; Pierce, M. K.; Goldman, S.; Moukabary, T.
Show abstract
IntroductionPersistent atrial fibrillation (AFib) research is dependent on large animal models to understand pathogenesis and test new treatments and therapeutic techniques. While various methods have been established to induce persistent AFib in large animals, including electrical, surgical, pharmacological, and genetic approaches, each has distinct limitations. The most recent being the device industry no longer providing off-target rapid atrial pacing programs for use in animals. This challenge requires the development of a new accessible model of inducing atrial fibrillation in large animals. Methods UsedWe developed a wireless pacing system using a Raspberry Pi Pico W microcontroller (Pico) programmed for variable pacing frequencies. The device is powered by a subcutaneously implanted 9V battery. The Picos built-in Wi-Fi capabilities enable remote connection and real-time adjustment of pacing frequency output. This protocol describes a prospective study in which swine will undergo chronic right atrial pacing for 3-4 weeks to induce persistent AFib. We tested our device in a domestic swine. Vascular access was established through the left jugular vein and an active fixation pacing lead (Medtronic 5076) was implanted under fluoroscopic guidance in the right atrium. Proper lead positioning and pacing function were confirmed through electrocardiographic monitoring of both atrial and ventricular capture. Preliminary ResultsOscilloscope testing demonstrated frequency and voltage output concordant with the programmed frequencies while real-time adjustments were made through the Wi-Fi interface. A cardiac pacing wire was placed in the right ventricle then relocated to the right atrium and successful pacing with capture was verified using an electrocardiogram. ConclusionsThis protocol will provide a system capable of capturing atrial tissue with confirmed wireless power transfer capabilities that has minimal tissue heating and is physiologically safe. Combined with our literature review findings that electrical atrial pacing methods are most effective for inducing persistent AFib, our device provides researchers with the potential for an improved tool for creating large animal models of persistent AFib.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- High-resolution Spatiotemporal Changes in Dominant Frequency and Structural Organization During Persistent Atrial Fibrillation 97%
- Mechanical effects of MitraClip on leaflet stress and myocardial strain in functional mitral regurgitation: A finite element modeling study. 94%
- Ventricular anatomical complexity and gender differences impact predictions from computational models 94%
Similar papers in this journal
- High Initial Heart Rate Score is an Independent Predictor of New Atrial High-Rate Episodes in Pacemaker Patients with Sinus Node Dysfunction 96%
- Fixation beats – a novel marker for reaching the left bundle branch area during deep septal lead implantation 95%
- Right bundle branch pacing: criteria, characteristics and outcomes 95%
Similar papers in this journal
- Comparison of electrophysiological left bundle branch pacing characteristics in different bilateral electrode pacing vector configurations 95%
- Electrocardiographic Abnormalities and Troponin Elevation in COVID-19 93%
- Autologous cardiac micrografts as support therapy to coronary artery bypass surgery 92%
Similar papers in this journal
- A Reproducible Protocol to Assess Arrhythmia Vulnerability in Silico: Pacing at the End of the Effective Refractory Period 94%
- Machine Learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data 94%
- Combination of personalized computational modeling and machine-learning for optimization of left ventricular pacing site in cardiac resynchronization therapy 94%
Similar papers in this journal
- BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis 93%
- Digitizing ECG image: new fully automated method and open-source software code 93%
- In Silico Modeling of Transcatheter Heart Valve Oversizing and Ellipticity, Part I: Establishing Credibility of an Advanced Model 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.