3D-electroanatomical mapping of the left atrium and catheter-based pulmonary vein isolation in pigs: A practical guide

AimTo propose a standardized workflow for 3D-electroanatomical mapping guided pulmonary vein isolation in pigs.Materials and methodsDanish female landrace pigs were anaesthetized. Ultrasound-guided puncture of both femoral veins was performed and arterial access for blood pressure measurement established. Fluoroscopy- and intracardiac ultrasound-guided passage of the patent foramen ovale or transseptal puncture was performed. Then, 3D-electroanatomical mapping of the left atrium was conducted using a high-density mapping catheter. After mapping all pulmonary veins, an irrigated radiofrequency ablation catheter was used to perform ostial ablation to achieve electrical pulmonary vein isolation. Entrance- and exit-block were confirmed and re-assessed after a 20-min waiting period. Lastly, animals were sacrificed to perform left atrial anatomical gross examination.ResultsWe present data from 11 consecutive pigs undergoing pulmonary vein isolation. Passage of the fossa ovalis or transseptal puncture was uneventful and successful in all animals. Within the inferior pulmonary trunk 2–4 individual veins as well as 1–2 additional left and right pulmonary veins could be cannulated. Electrical isolation by point-by-point ablation of all targeted veins was successful. However, pitfalls including phrenic nerve capture during ablation, ventricular arrhythmias during antral isolation close to the mitral valve annulus and difficulties in accessing right pulmonary veins were encountered.ConclusionFluoroscopy- and intracardiac ultrasound-guided transseptal puncture, high-density electroanatomical mapping of all pulmonary veins and complete electrical pulmonary vein isolation can be achieved reproducibly and safely in pigs when using current technologies and a step-by-step approach.</p

Simplifying particle swarm optimization

The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, wheras previous approaches to meta-optimization have buned behavioural parameters to work well on just a single optimization problem. It is then found that not only the PSO method and its simplified variant have comparable performance for optimization a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some case