Heart failure (HF) is a major cause of hospitalization, often leading to acute kidney injury (AKI) due to venous congestion. The Venous Excess Ultrasound (VExUS) score, introduced by Beaubin-Souligny, is a bedside tool for assessing congestion severity and guiding decongestive therapy. VExUS has demonstrated prognostic value in predicting AKI, HF readmission, and mortality. Indeed, guiding decongestive therapy through the VExUS score has been shown to significantly improve the likelihood of achieving faster decongestion. Objectives: This review aims to discuss the potential role of VExUS and analyze the recent findings about its relevance in guiding decongestive therapy in patients with acute decompensated HF. Methods: A comprehensive literature review was conducted, which identified journal articles focused on VExUS and manual reviews of relevant peer-reviewed journals. Conclusions: VExUS is a promising tool for evaluating venous congestion in cardiorenal patients, thereby improving fluid and diuretic management. It provides real-time, non-invasive monitoring that enhances clinical decision-making. However, its accuracy depends on operator expertise, and further research is needed to validate its application across different patient populations.

Aims: A pre-operative non-invasive identification of the site of origin (SOO) of outflow tract ventricular arrhythmias (OTVAs) is important to properly plan radiofrequency ablation procedures. Although some algorithms based on electrocardiograms (ECGs) have been developed to predict left vs. right ventricular origins, their accuracy is still limited, especially in complex anatomies. The aim of this work is to use patient-specific electrophysiological simulations of the heart to predict the SOO in OTVA patients. Methods and results: An in silico pace-mapping procedure was designed and used on 11 heart geometries, generating for each case simulated ECGs from 12 clinically plausible SOO. Subsequently, the simulated ECGs were compared with patient ECG data obtained during the clinical tachycardia using the 12-lead correlation coefficient (12-lead ρ). Left ventricle (LV) vs. right ventricle (RV) SOO was estimated by computing the LV/RV ratio for each patient, obtained by dividing the average 12-lead ρ value of the LV- and RV-SOO simulated ECGs, respectively. Simulated ECGs that had virtual sites close to the ablation points that stopped the arrhythmia presented higher correlation coefficients. The LV/RV ratio correctly predicted LV vs. RV SOO in 10/11 cases; 1.07 vs. 0.93 P < 0.05 for 12-lead ρ. Conclusion: The obtained results demonstrate the potential of the developed in silico pace-mapping technique to complement standard ECG for the pre-operative planning of complex ventricular arrhythmias.

Electroanatomical mapping (EAM) systems are commonly used in clinical practice for guiding catheter ablation treatments of common arrhythmias. In focal tachycardias, the ablation target is defined by locating the earliest activation area determined by the joint analysis of electrogram (EGM) signals at different sites. However, this is currently a manual time-consuming and experience-dependent task performed during the intervention and thus prone to stress-related errors. In this paper, we present an automatic delineation strategy that combines electrocardiogram (ECG) information with the wavelet decomposition of the EGM signal envelope to identify the onset of each EGM signal for activation mapping. Fourteen electroanatomical maps corresponding to ten patients suffering from non-tolerated premature ventricular contraction (PVC) beats and admitted for ablation procedure were used for evaluation. We compared the results obtained automatically with two types of manual annotations: one during the intervention by an expert technician (on-procedure) and other after the intervention (off-procedure), free from time and procedural constraints, by two other technicians. The automatic annotations show a significant correlation (0.95, p <; 0.01) with the evaluation reference (off-procedure annotation sets combination) and has an error of 2.1 ± 10.9 ms, around the order of magnitude of the on-procedure annotations error (-2.6 ± 6.8 ms). The results suggest that the proposed methodology could be incorporated into EAM systems to considerably reduce processing time during ablation interventions.

Objective: This study introduces a predictability framework based on the concept of Granger causality (GC), in order to analyze the activity and interactions between different intracardiac sites during atrial fibrillation (AF). Methods: GC-based interactions were studied using a three-electrode analysis scheme with multi-variate autoregressive models of the involved preprocessed intracardiac signals. The method was evaluated in different scenarios covering simulations of complex atrial activity as well as endocardial signals acquired from patients. Results: The results illustrate the ability of the method to determine atrial rhythm complexity and to track and map propagation during AF. Conclusion: The proposed framework provides information on the underlying activation and regularity, does not require activation detection or postprocessing algorithms and is applicable for the analysis of any multielectrode catheter. Significance: The proposed framework can potentially help to guide catheter ablation interventions of AF.