The Roles of Pre-P-wave Versus Peri-P-wave Fractionated Electrograms for Atrial Substrate Beyond Entrainment Response.

Atrial tachycardia (AT) with alternating cycle lengths is sparsely reported, and, hence, the ideal mapping strategy has not been firmly established. Beyond the entrainment during tachycardia, some fragmentation characteristics might also give important clues for its possible participation in the macro-re-entrant circuit. We discuss a patient with prior atrial septal defect surgical closures who presented with dual macro-re-entrant ATs related to a fragmented area on the right atrial free wall (240 ms) and the cavotricuspid isthmus (260 ms), respectively. After ablation of the fastest AT on the lateral right atrial free wall, the cycle of the first AT changed to the second AT that was interrupted on cavotricuspid isthmus, proving the dual tachycardia mechanism. This case report addresses the utilization of electroanatomic mapping information as well as fractionated electrogram timing with respect to the surface P-wave as guides for ablation location.

The Initial Part of Polymorphic Ventricular Tachycardia as a Clue for the Sustainability of Tachycardia and Ablation Success: A Varying Degree of Purkinje-Myocardial Complicity?

The cardiac Purkinje system is capable of very rapid burst activity suggestive of its potential role in being a driver of polymorphic ventricular tachycardia (VT) (PMVT) or ventricular fibrillation (VF). It plays a pivotal role, however, not only in the triggering of but also the perpetuation of ventricular arrhythmias. A varying degree of Purkinje-myocardial complicity has been blamed in determining not only the sustained or non-sustained nature of PMVT but also the pleomorphism of the non-sustained runs. The initial part of PMVT before cascading to the whole ventricle to establish disorganized VF can give important clues for ablation of PMVT and VF. We present a case of an electrical storm after acute myocardial infarction that was successfully ablated after identifying Purkinje potentials that triggered polymorphic, monomorphic, and pleiomorphic VTs and VF.

Paradoxical Para-Hisian Pacing Response.

Para-Hisian pacing (PHP) is among the most useful maneuvers in cardiac electrophysiology during sinus rhythm and identifies whether retrograde conduction is dependent on the atrioventricular (AV) node. In this maneuver, the retrograde activation time and pattern are compared during capture and loss of capture of the His bundle while pacing from a para-Hisian position. A common misconception about PHP is that it is useful only for septal accessory pathways (APs). However, even with left or right lateral pathways, as long as pacing from the para-Hisian region conducts to the atrium with the activation sequence being analyzed, it can be used to determine whether that activation is AV node-dependent or AP-dependent.

Convergent Double Coronary Sinus Potentials During Atrial Tachycardia.

The analysis of the patterns and timing of coronary sinus activation provides a rapid stratification of the most likely macro-re-entrant atrial tachycardias and points toward the likely origin of centrifugal ones by comparing the left atrial and coronary sinus activation sequence and morphology during sinus rhythm and atrial tachycardia. The analysis of both the near- and far-field electrogram morphology of atrial signals also gives important clues in determining the mechanism of the arrhythmia.

Atrial Tachycardia with Cycle Length Alternans.

Tachycardia-induced tachycardia, or so-called double tachycardia, appears to be a relatively rare condition. The underlying mechanism for stable beat-to-beat cycle length variability (alternans) in atrial tachycardia has been sparsely reported.

Effectiveness of the systemic immune inflammation index to predict atrial fibrillation recurrence after cryoablation.

Background: We aimed to investigate the predictive role of systemic immune inflammation index (SII) on atrial fibrillation (AF) recurrence following cryoballoon-based AF ablation. Materials & methods: A total of 370 consecutive patients with symptomatic AF who underwent cryoablation were enrolled. The patients were divided into two groups according to recurrence development. Results: During the follow-up period of 25.0 ± 6.7 months, recurrence was observed in 77 patients (20.8%). Receiver operating characteristic analysis demonstrated that using a cutoff level of 532, SII had a sensitivity of 71% and specificity of 68%. In the multivariate Cox model, high SII was a significant predictor of the recurrence. Conclusion: This study showed that higher SII level is an independent predictor of AF recurrence.

Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet.

The segmentation of the liver is a difficult process due to the changes in shape, border, and density that occur in each section in computed tomography (CT) images. In this study, the Adding Inception Module-Unet (AIM-Unet) model, which is a hybridization of convolutional neural networks-based Unet and Inception models, is proposed for computer-assisted automatic segmentation of the liver and liver tumors from CT scans of the abdomen. Experimental studies were carried out on four different liver CT image datasets, one of which was prepared for this study and three of which were open (CHAOS, LIST, and 3DIRCADb). The results obtained using the proposed method and the segmentation results marked by the specialist were compared with the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), and accuracy (ACC) measurement parameters. In this study, we obtained the best DSC, JSC, and ACC liver segmentation performance metrics on the CHAOS dataset as 97.86%, 96.10%, and 99.75%, respectively, of the AIM-Unet model we propose, which is trained separately on three datasets (LiST, CHAOS, and our dataset) containing liver images. Additionally, 75.6% and 65.5% of the DSC tumor segmentation metrics were calculated on the proposed model LiST and 3DIRCADb datasets, respectively. In addition, the segmentation success results on the datasets with the AIM-Unet model were compared with the previous studies. With these results, it has been seen that the method proposed in this study can be used as an auxiliary tool in the decision-making processes of physicians for liver segmentation and detection of liver tumors. This study is useful for medical images, and the developed model can be easily developed for applications in different organs and other medical fields.