Personalized pulmonary vein isolation with very high-power short-duration lesions guided by left atrial wall thickness: the QDOT-by-LAWT randomized trial.

AIMS: Pulmonary vein isolation (PVI) for paroxysmal atrial fibrillation (PAF) using very high-power short-duration (vHPSD) radiofrequency (RF) ablation proved to be safe and effective. However, vHPSD applications result in shallower lesions that might not be always transmural. Multidetector computed tomography-derived left atrial wall thickness (LAWT) maps could enable a thickness-guided switching from vHPSD to the standard-power ablation mode. The aim of this randomized trial was to compare the safety, the efficacy, and the efficiency of a LAWT-guided vHPSD PVI approach with those of the CLOSE protocol for PAF ablation (NCT04298177). METHODS AND RESULTS: Consecutive patients referred for first-time PAF ablation were randomized on a 1:1 basis. In the QDOT-by-LAWT arm, for LAWT ≤2.5 mm, vHPSD ablation was performed; for points with LAWT > 2.5 mm, standard-power RF ablation titrating ablation index (AI) according to the local LAWT was performed. In the CLOSE arm, LAWT information was not available to the operator; ablation was performed according to the CLOSE study settings: AI ≥400 at the posterior wall and ≥550 at the anterior wall. A total of 162 patients were included. In the QDOT-by-LAWT group, a significant reduction in procedure time (40 vs. 70 min; P < 0.001) and RF time (6.6 vs. 25.7 min; P < 0.001) was observed. No difference was observed between the groups regarding complication rate (P = 0.99) and first-pass isolation (P = 0.99). At 12-month follow-up, no significant differences occurred in atrial arrhythmia-free survival between groups (P = 0.88). CONCLUSION: LAWT-guided PVI combining vHPSD and standard-power ablation is not inferior to the CLOSE protocol in terms of 1-year atrial arrhythmia-free survival and demonstrated a reduction in procedural and RF times.

Personalized pulmonary vein antrum isolation guided by left atrial wall thickness for persistent atrial fibrillation.

AIMS: Pulmonary vein (PV) antrum isolation proved to be effective for treating persistent atrial fibrillation (PeAF). We sought to investigate the results of a personalized approach aimed at adapting the ablation index (AI) to the local left atrial wall thickness (LAWT) in a cohort of consecutive patients with PeAF. METHODS AND RESULTS: Consecutive patients referred for PeAF first ablation were prospectively enrolled. The LAWT three-dimensional maps were obtained from pre-procedure multidetector computed tomography and integrated into the navigation system. Ablation index was titrated according to the local LAWT, and the ablation line was personalized to avoid the thickest regions while encircling the PV antrum. A total of 121 patients (69.4% male, age 64.5 ± 9.5 years) were included. Procedure time was 57 min (IQR 50-67), fluoroscopy time was 43 s (IQR 20-71), and radiofrequency (RF) time was 16.5 min (IQR 14.3-18.4). The median AI tailored to the local LAWT was 387 (IQR 360-410) for the anterior wall and 335 (IQR 300-375) for the posterior wall. First-pass PV antrum isolation was obtained in 103 (85%) of the right PVs and 103 (85%) of the left PVs. Median LAWT values were higher for PVs without first-pass isolation as compared to the whole cohort (P = 0.02 for left PVs and P = 0.03 for right PVs). Recurrence-free survival was 79% at 12 month follow-up. CONCLUSION: In this prospective study, LAWT-guided PV antrum isolation for PeAF was effective and efficient, requiring low procedure, fluoroscopy, and RF time. A randomized trial comparing the LAWT-guided ablation with the standard of practice is in progress (ClinicalTrials.gov, NCT05396534).

Joint Analysis of Morphological Parameters and In Silico Haemodynamics of the Left Atrial Appendage for Thrombogenic Risk Assessment.

Background: Atrial fibrillation (AF) is considered the most common human arrhythmia. In nonvalvular AF, around 99% of thrombi are formed in the left atrial appendage (LAA). Nevertheless, there is not a consensus in the community about the relevant factors to stratify the AF population according to thrombogenic risk. Objective: To demonstrate the need for combining left atrial morphological and haemodynamics indices to improve the thrombogenic risk assessment in nonvalvular AF patients. Methods: A cohort of 71 nonvalvular AF patients was analysed. Statistical analysis, regression models, and random forests were used to analyse the differences between morphological and haemodynamics parameters, extracted from computational simulations built on 3D rotational angiography images, between patients with and without transient ischemic attack (TIA) or cerebrovascular accident (CVA). Results: The analysis showed that models composed of both morphological and haemodynamic factors were better predictors of TIA/CVA compared with models based on either morphological or haemodynamic factors separately. Maximum ostium diameter, length of the centreline, blood flow velocity within the LAA, oscillatory shear index, and time average wall shear stress parameters were found to be key risk factors for TIA/CVA prediction. In addition, TIA/CVA patients presented more flow stagnation within the LAA. Conclusion: Thrombus formation in the LAA is the result of multiple factors. Analyses based only on morphological or haemodynamic parameters are not precise enough to predict such a phenomenon, as demonstrated in our results; a better patient stratification can be obtained by jointly analysing morphological and haemodynamic features.

Machine learning for the automatic assessment of aortic rotational flow and wall shear stress from 4D flow cardiac magnetic resonance imaging.

OBJECTIVE: Three-dimensional (3D) time-resolved phase-contrast cardiac magnetic resonance (4D flow CMR) allows for unparalleled quantification of blood velocity. Despite established potential in aortic diseases, the analysis is time-consuming and requires expert knowledge, hindering clinical application. The present research aimed to develop and test a fully automatic machine learning-based pipeline for aortic 4D flow CMR analysis. METHODS: Four hundred and four subjects were prospectively included. Ground-truth to train the algorithms was generated by experts. The cohort was divided into training (323 patients) and testing (81) sets and used to train and test a 3D nnU-Net for segmentation and a Deep Q-Network algorithm for landmark detection. In-plane (IRF) and through-plane (SFRR) rotational flow descriptors and axial and circumferential wall shear stress (WSS) were computed at ten planes covering the ascending aorta and arch. RESULTS: Automatic aortic segmentation resulted in a median Dice score (DS) of 0.949 and average symmetric surface distance of 0.839 (0.632-1.071) mm, comparable with the state of the art. Aortic landmarks were located with a precision comparable with experts in the sinotubular junction and first and third supra-aortic vessels (p = 0.513, 0.592 and 0.905, respectively) but with lower precision in the pulmonary bifurcation (p = 0.028), resulting in precise localisation of analysis planes. Automatic flow assessment showed excellent (ICC > 0.9) agreement with manual quantification of SFRR and good-to-excellent agreement (ICC > 0.75) in the measurement of IRF and axial and circumferential WSS. CONCLUSION: Fully automatic analysis of complex aortic flow dynamics from 4D flow CMR is feasible. Its implementation could foster the clinical use of 4D flow CMR. KEY POINTS: • 4D flow CMR allows for unparalleled aortic blood flow analysis but requires aortic segmentation and anatomical landmark identification, which are time-consuming, limiting 4D flow CMR widespread use. • A fully automatic machine learning pipeline for aortic 4D flow CMR analysis was trained with data of 323 patients and tested in 81 patients, ensuring a balanced distribution of aneurysm aetiologies. • Automatic assessment of complex flow characteristics such as rotational flow and wall shear stress showed good-to-excellent agreement with manual quantification.

High-power short-duration vs. standard radiofrequency cardiac ablation: comparative study based on an in-silico model.

PURPOSE: While the standard setting during radiofrequency catheter ablation (RFCA) consists of applying low power for long times, a new setting based on high power and short duration (HPSD) has recently been suggested as safer and more effective. Our aim was to compare the electrical and thermal performance of standard vs. HPSD settings, especially to assess the effect of the catheter orientation. METHODS: A 3D computational model was built based on a coupled electric-thermal-flow problem. Standard (20 W-45 s and 30 W-30 s) and HPSD settings (70 W-7 s and 90 W-4 s) were compared. Since the model only included a cardiac tissue fragment, the power values were adjusted to 80% of the clinical values (15, 23, 53 and 69 W). Three catheter-tissue orientations were considered (90°, 45° and 0°). Thermal lesions were assessed by the Arrhenius equation. Safety was assessed by checking the occurrence of steam pops (100 °C in tissue) and thrombus formation (80 °C in blood). RESULTS: The computed thermal lesions were in close agreement with the experimental data in the literature, in particular with in vivo studies. HPSD created shallower and wider lesions than standard settings, especially with the catheter at 45°. Steam pops occurred earlier with HPSD, regardless of catheter orientation. CONCLUSION: HPSD seems to be more effective in cases that need shallow and extensive lesions, especially when the catheter is at 0° or at 45°, as used in pulmonary vein isolation.

Automatic Detection of Slow Conducting Channels during Substrate Ablation of Scar-Related Ventricular Arrhythmias.

BACKGROUND: Voltage mapping allows identifying the arrhythmogenic substrate during scar-related ventricular arrhythmia (VA) ablation procedures. Slow conducting channels (SCCs), defined by the presence of electrogram (EGM) signals with delayed components (EGM-DC), are responsible for sustaining VAs and constitute potential ablation targets. However, voltage mapping, as it is currently performed, is time-consuming, requiring a manual analysis of all EGMs to detect SCCs, and its accuracy is limited by electric far-field. We sought to evaluate an algorithm that automatically identifies EGM-DC, classifies mapping points, and creates new voltage maps, named "Slow Conducting Channel Maps" (SCC-Maps). METHODS: Retrospective analysis of electroanatomic maps (EAM) from 20 patients (10 ischemic, 10 with arrhythmogenic right ventricular dysplasia/cardiomyopathy) was performed. EAM voltage maps were acquired during sinus rhythm and used for ablation. Preprocedural contrast-enhanced cardiac magnetic resonance (Ce-CMR) imaging was available for the ischemic population. Three mapping modalities were analysed: (i) EAM voltage maps using standard (EAM standard) or manual (EAM screening) thresholds for defining core and border zones; (ii) SCC-Maps derived from the use of the novel SCC-Mapping algorithm that automatically identify EGM-DCs measuring the voltage of the local component; and (iii) Ce-CMR maps (when available). The ability of each mapping modality in identifying SCCs and their agreement was evaluated. RESULTS: SCC-Maps and EAM screening identified a greater number of SCC entrances than EAM standard (3.45 ± 1.61 and 2.95 ± 2.31, resp., vs. 1.05 ± 1.10; p < 0.01). SCC-Maps and EAM screening highly correlate with Ce-CMR maps in the ischemic population when compared to EAM standard (Lin's correlation = 0.628 and 0.679, resp., vs. 0.212, p < 0.01). CONCLUSION: The SCC-Mapping algorithm allows an operator-independent analysis of EGM signals showing better identification of the arrhythmogenic substrate characteristics when compared to standard voltage EAM.

In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations.

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.

Reproducibility and accuracy of late gadolinium enhancement cardiac magnetic resonance measurements for the detection of left atrial fibrosis in patients undergoing atrial fibrillation ablation procedures.

AIMS: Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) may define left atrial (LA) anatomy and structural remodelling, and facilitate atrial fibrillation (AF) ablation. We aimed to assess the intra- and inter-observer reproducibility and agreement of LGE-CMR parameters with direct application to AF ablation techniques. METHODS AND RESULTS: One experienced and one non-experienced observer performed complete LGE-CMR data analysis twice, on different days, in 40 randomly selected LGE-CMR examinations [20 performed before ablation (pre-ablation) and 20 performed 3 months after ablation (post-ablation)]. Four additional observers (two experienced and two non-experienced) performed complete LGE-CMR data analysis in a subgroup of 30 patients (15 pre-ablation and 15 post-ablation). All LGE-CMR were performed in sinus rhythm. Intra- and inter-observer reproducibility of LA volume, LA area, and sphericity index (SI) was high: coefficient of variation <10% and intraclass correlation coefficient >0.71. Geometric congruency of repeated reconstruction of LA shape was high: maximal error <5 mm for intra-observer and <8 mm for inter-observer. The precision of scar location increased with extent of scar, and was high (Dice coefficient >0.75) when the scar area was >5 cm2 for a single observer and >15 cm2 for multiple observers. Non-experienced observers performed equally well to experienced observers. CONCLUSION: Late gadolinium enhancement cardiac magnetic resonance measurements of LA area, volume, and SI were reproducible, and geometric congruency of LA shape was high. Location of scar was precise for scar areas >5 cm2 for single observers and >15 cm2 for multiple observers, regardless of the observers' experience. These results may serve as a reference for future studies on the role for substrate-based AF ablation procedures.

Thermal impact of balloon occlusion of the coronary sinus during mitral isthmus radiofrequency ablation: an in-silico study.

Purpose: Although experimental data have suggested that temporary occlusion of the coronary sinus (CS) can facilitate the creation of transmural lesions across the atrial wall (AW) during mitral isthmus radiofrequency (RF) ablation, no computer modeling study has yet been made on the effect of the blood flow inside the epicardial vessels and its stoppage by an occlusion balloon.Methods: Computer simulations using constant power were conducted to study these phenomena by two methods: (1) by setting blood velocity in the CS to zero, which mimics a distal occlusion; and (2) by including a balloon filled with air in the model just below the ablation site, which mimics a proximal occlusion.Results: For short ablations (15 s) and perpendicular electrode/tissue orientation, lesion size was smaller with proximal occlusion compared to distal or no occlusion, regardless of the AW-CS distance (from 0.5 mm to 3.4 mm). For other angulations (0 and 45°) lesion size was almost the same in all cases. For longer ablations (60 s), the internal CS blood flow (no occlusion) considerably reduced lesion size, while stoppage combined with the proximal presence of a balloon produced the largest lesions. This performance was similar for different catheter angulations (0, 45 and 90°). Balloon length (from 10 to 40 mm) was found to be an irrelevant parameter when proximal occlusion was modeled.Conclusions: Using an air-filled balloon to occlude CS facilitates mitral isthmus ablation in long ablations, while proximal occlusion could impede transmural lesions in the case of short ablations (15 s).

The reproducibility of late gadolinium enhancement cardiovascular magnetic resonance imaging of post-ablation atrial scar: a cross-over study.

BACKGROUND: Cardiovascular magnetic resonance (CMR) imaging has been used to visualise post-ablation atrial scar (PAAS), generally employing a three-dimensional (3D) late gadolinium enhancement (LGE) technique. However the reproducibility of PAAS imaging has not been determined. This cross-over study is the first to investigate the reproducibility of the technique, crucial for both future research design and clinical implementation. METHODS: Forty subjects undergoing first time ablation for atrial fibrillation (AF) had detailed CMR assessment of PAAS. Following baseline pre-ablation scan, two scans (separated by 48 h) were performed at three months post-ablation. Each scan session included 3D LGE acquisition at 10, 20 and 30 min post administration of gadolinium-based contrast agent (GBCA). Subjects were allocated at second scan post-ablation to identical imaging parameters ('Repro', n = 10), 3 T scanner ('3 T', n = 10), half-slice thickness ('Half-slice', n = 10) or half GBCA dose ('Half-gad', n = 10). PAAS was compared to baseline scar and then reproducibility was assessed for two measures of thresholded scar (% left atrial (LA) occupied by PAAS (%LA PAAS) and Pulmonary Vein Encirclement (PVE)), and then four measures of non-thresholded scar (point-by-point assessment of PAAS, four normalisation methods). Thresholded measures of PAAS were evaluated against procedural outcome (AF recurrence). RESULTS: A total of 271 3D acquisitions (out of maximum 280, 96.7%) were acquired. At 20 and 30 min, inter-scan reproducibility was good to excellent (coefficient of variation at 20 min and 30 min: %LA PAAS 0.41 and 0.20; PVE 0.13 and 0.04 respectively for 'Repro' group). Changes in imaging parameters, especially reduced GBCA dose, reduced inter-scan reproducibility, but for most measures remained good to excellent (ICC for %LA PAAS 0.454-0.825, PVE 0.618-0.809 at 30 min). For non-thresholded scar, highest reproducibility was observed using blood pool z-score normalisation technique: inter-scan ICC 0.759 (absolute agreement, 'Repro' group). There was no significant relationship between indices of PAAS and AF recurrence. CONCLUSION: PAAS imaging is a reproducible finding. Imaging should be performed at least 20 min post-GBCA injection, and a blood pool z-score should be considered for normalisation of signal intensities. The clinical implications of these findings remain to be established in the absence of a simple correlation with arrhythmia outcome. TRIAL REGISTRATION: United Kingdom National Research Ethics Service 08/H0802/68 - 30th September 2008.

Preferential regional distribution of atrial fibrosis in posterior wall around left inferior pulmonary vein as identified by late gadolinium enhancement cardiac magnetic resonance in patients with atrial fibrillation.

Aims: Left atrial (LA) fibrosis can be identified by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) in patients with atrial fibrillation (AF). However, there is limited information about anatomical fibrosis distribution in the left atrium. The aim is to determine whether there is a preferential spatial distribution of fibrosis in the left atrium in patients with AF. Methods and results: A 3-Tesla LGE-CMR was performed in 113 consecutive patients referred for AF ablation. Images were post-processed and analysed using ADAS-AF software (Galgo Medical), which allows fibrosis identification in 3D colour-coded shells. A regional semiautomatic LA parcellation software was used to divide the atrial wall into 12 segments: 1-4, posterior wall; 5-6, floor; 7, septal wall; 8-11, anterior wall; 12, lateral wall. The presence and amount of fibrosis in each segment was obtained for analysis. After exclusions for artefacts and insufficient image quality, 76 LGE-MRI images (68%) were suitable for fibrosis analysis. Segments 3 and 5, closest to the left inferior pulmonary vein, had significantly higher fibrosis (40.42% ± 23.96 and 25.82% ± 21.24, respectively; P < 0.001), compared with other segments. Segments 8 and 10 in the anterior wall contained the lowest fibrosis (2.54% ± 5.78 and 3.82% ± 11.59, respectively; P < 0.001). Age >60 years was significantly associated with increased LA fibrosis [95% confidence interval (CI) 0.19-8.39, P = 0.04] and persistent AF approached significance (95% CI -0.19% to 7.83%, P = 0.08). Conclusion: In patients with AF, the fibrotic area is preferentially located at the posterior wall and floor around the antrum of the left inferior pulmonary vein. Age >60 years was associated with increased fibrosis.

Automatic activation mapping and origin identification of idiopathic outflow tract ventricular arrhythmias.

PURPOSE: Activation mapping is used to guide ablation of idiopathic outflow tract ventricular arrhythmias (OTVAs). Isochronal activation maps help to predict the site of origin (SOO): left vs right outflow tract (OT). We evaluate an algorithm for automatic activation mapping based on the onset of the bipolar electrogram (EGM) signal for predicting the SOO and the effective ablation site in OTVAs. METHODS: Eighteen patients undergoing ablation due to idiopathic OTVAs were studied (12 with left ventricle OT origin). Right ventricle activation maps were obtained offline with an automatic algorithm and compared with manual annotation maps obtained during the intervention. Local activation time (LAT) accuracy was assessed, as well as the performance of the 10ms earliest activation site (EAS) isochronal area in predicting the SOO. RESULTS: High correlation was observed between manual and automatic LATs (Spearman's: 0.86 and Lin's: 0.85, both p<0.01). The EAS isochronal area were closely located in both map modalities (5.55 ± 3.56mm) and at a similar distance from the effective ablation site (0.15±2.08mm difference, p=0.859). The 10ms isochronal area longitudinal/perpendicular diameter ratio measured from automatic maps showed slightly superior SOO identification (67% sensitivity, 100% specificity) compared with manual maps (67% sensitivity, 83% specificity). CONCLUSIONS: Automatic activation mapping based on the bipolar EGM onset allows fast, accurate and observer-independent identification of the SOO and characterization of the spreading of the activation wavefront in OTVAs.

Toward the automatic quantification of in utero brain development in 3D structural MRI: A review.

Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high-quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1- and T2-weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio-temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772-2787, 2017. © 2017 Wiley Periodicals, Inc.

A computational model of the fetal circulation to quantify blood redistribution in intrauterine growth restriction.

Intrauterine growth restriction (IUGR) due to placental insufficiency is associated with blood flow redistribution in order to maintain delivery of oxygenated blood to the brain. Given that, in the fetus the aortic isthmus (AoI) is a key arterial connection between the cerebral and placental circulations, quantifying AoI blood flow has been proposed to assess this brain sparing effect in clinical practice. While numerous clinical studies have studied this parameter, fundamental understanding of its determinant factors and its quantitative relation with other aspects of haemodynamic remodeling has been limited. Computational models of the cardiovascular circulation have been proposed for exactly this purpose since they allow both for studying the contributions from isolated parameters as well as estimating properties that cannot be directly assessed from clinical measurements. Therefore, a computational model of the fetal circulation was developed, including the key elements related to fetal blood redistribution and using measured cardiac outflow profiles to allow personalization. The model was first calibrated using patient-specific Doppler data from a healthy fetus. Next, in order to understand the contributions of the main parameters determining blood redistribution, AoI and middle cerebral artery (MCA) flow changes were studied by variation of cerebral and peripheral-placental resistances. Finally, to study how this affects an individual fetus, the model was fitted to three IUGR cases with different degrees of severity. In conclusion, the proposed computational model provides a good approximation to assess blood flow changes in the fetal circulation. The results support that while MCA flow is mainly determined by a fall in brain resistance, the AoI is influenced by a balance between increased peripheral-placental and decreased cerebral resistances. Personalizing the model allows for quantifying the balance between cerebral and peripheral-placental remodeling, thus providing potentially novel information to aid clinical follow up.

Impact of left atrial wall motion assumptions in fluid simulations on proposed predictors of thrombus formation.

Atrial fibrillation (AF) poses a significant risk of stroke due to thrombus formation, which primarily occurs in the left atrial appendage (LAA). Medical image-based computational fluid dynamics (CFD) simulations can provide valuable insight into patient-specific hemodynamics and could potentially enhance personalized assessment of thrombus risk. However, the importance of accurately representing the left atrial (LA) wall dynamics has not been fully resolved. In this study, we compared four modeling scenarios; rigid walls, a generic wall motion based on a reference motion, a semi-generic wall motion based on patient-specific motion, and patient-specific wall motion based on medical images. We considered a LA geometry acquired from 4D computed tomography during AF, systematically performed convergence tests to assess the numerical accuracy of our solution strategy, and quantified the differences between the four approaches. The results revealed that wall motion had no discernible impact on LA cavity hemodynamics, nor on the markers that indicate thrombus formation. However, the flow patterns within the LAA deviated significantly in the rigid model, indicating that the assumption of rigid walls may lead to errors in the estimated risk factors. In contrast, the generic, semi-generic, and patient-specific cases were qualitatively similar. The results highlight the crucial role of wall motion on hemodynamics and predictors of thrombus formation, and also demonstrate the potential of using a generic motion model as a surrogate for the more complex patient-specific motion. While the present study considered a single case, the employed CFD framework is entirely open-source and designed for adaptability, allowing for integration of additional models and generic motions.

On the importance of fundamental computational fluid dynamics toward a robust and reliable model of left atrial flows.

Computational fluid dynamics (CFD) studies of left atrial flows have reached a sophisticated level, for example, revealing plausible relationships between hemodynamics and stresses with atrial fibrillation. However, little focus has been on fundamental fluid modeling of LA flows. The purpose of this study was to investigate the spatiotemporal convergence, along with the differences between high- (HR) versus normal-resolution/accuracy (NR) solution strategies, respectively. Rigid wall CFD simulations were conducted on 12 patient-specific left atrial geometries obtained from computed tomography scans, utilizing a second-order accurate and space/time-centered solver. The convergence studies showed an average variability of around 30% and 55% for time averaged wall shear stress (WSS), oscillatory shear index (OSI), relative residence time (RRT), and endothelial cell activation potential (ECAP), even between intermediate spatial and temporal resolutions, in the left atrium (LA) and left atrial appendage (LAA), respectively. The comparison between HR and NR simulations showed good correlation in the LA for WSS, RRT, and ECAP ( R 2 > .9 ), but not for OSI ( R 2 = .63 ). However, there were poor correlations in the LAA especially for OSI, RRT, and ECAP ( R 2 = .55, .63, and .61, respectively), except for WSS ( R 2 = .81 ). The errors are comparable to differences previously reported with disease correlations. To robustly predict atrial hemodynamics and stresses, numerical resolutions of 10 M elements (i.e., Δ x = ∼ .5 mm) and 10 k time-steps per cycle seem necessary (i.e., one order of magnitude higher than normally used in both space and time). In conclusion, attention to fundamental numerical aspects is essential toward establishing a plausible, robust, and reliable model of LA flows.

The role of the pulmonary veins on left atrial flow patterns and thrombus formation.

Atrial fibrillation (AF) is the most common human arrhythmia, forming thrombi mostly in the left atrial appendage (LAA). However, the relation between LAA morphology, blood patterns and clot formation is not yet fully understood. Furthermore, the impact of anatomical structures like the pulmonary veins (PVs) have not been thoroughly studied due to data acquisition difficulties. In-silico studies with flow simulations provide a detailed analysis of blood flow patterns under different boundary conditions, but a limited number of cases have been reported in the literature. To address these gaps, we investigated the influence of PVs on LA blood flow patterns and thrombus formation risk through computational fluid dynamics simulations conducted on a sizeable cohort of 130 patients, establishing the largest cohort of patient-specific LA fluid simulations reported to date. The investigation encompassed an in-depth analysis of several parameters, including pulmonary vein orientation (e.g., angles) and configuration (e.g., number), LAA and LA volumes as well as their ratio, flow, and mass-less particles. Our findings highlight the total number of particles within the LAA as a key parameter for distinguishing between the thrombus and non-thrombus groups. Moreover, the angles between the different PVs play an important role to determine the flow going inside the LAA and consequently the risk of thrombus formation. The alignment between the LAA and the main direction of the left superior pulmonary vein, or the position of the right pulmonary vein when it exhibits greater inclination, had an impact to distinguish the control group vs. the thrombus group. These insights shed light on the intricate relationship between PV configuration, LAA morphology, and thrombus formation, underscoring the importance of comprehensive blood flow pattern analyses.

Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data.

The treatment of outflow tract ventricular arrhythmias (OTVA) through radiofrequency ablation requires the precise identification of the site of origin (SOO). Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates. Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician's expertise. Although computational models and machine learning (ML) approaches have been proposed to assist OTVA procedures, they either consume substantial time, lack interpretability or do not use clinical information. Here, we propose an alternative strategy for automatically predicting the ventricular origin of OTVA patients using ML. Our objective was to classify ventricular (left/right) origin in the outflow tracts (LVOT and RVOT, respectively), integrating ECG and clinical data from each patient. Extending beyond differentiating ventricle origin, we explored specific SOO characterization. Utilizing four databases, we also trained supervised learning models on the QRS complexes of the ECGs, clinical data, and their combinations. The best model achieved an accuracy of 89%, highlighting the significance of precordial leads V1-V4, especially in the R/S transition and initiation of the QRS complex in V2. Unsupervised analysis revealed that some origins tended to group closer than others, e.g., right coronary cusp (RCC) with a less sparse group than the aortic cusp origins, suggesting identifiable patterns for specific SOOs.

Delineation of intracavitary electrograms for the automatic quantification of decrement-evoked potentials in the coronary sinus with deep-learning techniques.

Cardiac arrhythmias cause depolarization waves to conduct unevenly on the myocardial surface, potentially delaying local components with respect to a previous beat when stimulated at faster frequencies. Despite the diagnostic value of localizing the distinct local electrocardiogram (EGM) components for identifying regions with decrement-evoked potentials (DEEPs), current software solutions do not perform automatic signal quantification. Electrophysiologists must manually measure distances on the EGM signals to assess the existence of DEEPs during pacing or extra-stimuli protocols. In this work, we present a deep learning (DL)-based algorithm to identify decrement in atrial components (measured in the coronary sinus) with respect to their ventricular counterparts from EGM signals, for disambiguating between accessory pathways (APs) and atrioventricular re-entrant tachycardias (AVRTs). Several U-Net and W-Net neural networks with different configurations were trained on a private dataset of signals from the coronary sinus (312 EGM recordings from 77 patients who underwent AP or AVRT ablation). A second, separate dataset was annotated for clinical validation, with clinical labels associated to EGM fragments in which decremental conduction was elucidated. To alleviate data scarcity, a synthetic data augmentation method was developed for generating EGM recordings. Moreover, two novel loss functions were developed to minimize false negatives and delineation errors. Finally, the addition of self-attention mechanisms and their effect on model performance was explored. The best performing model was a W-Net model with 6 levels, optimized solely with the Dice loss. The model obtained precisions of 91.28%, 77.78% and of 100.0%, and recalls of 94.86%, 95.25% and 100.0% for localizing local field, far field activations, and extra-stimuli, respectively. The clinical validation model demonstrated good overall agreement with respect to the evaluation of decremental properties. When compared to the criteria of electrophysiologists, the automatic exclusion step reached a sensitivity of 87.06% and a specificity of 97.03%. Out of the non-excluded signals, a sensitivity of 96.77% and a specificity of 95.24% was obtained for classifying them into decremental and non-decremental potentials. Current results show great promise while being, to the best of our knowledge, the first tool in the literature allowing the delineation of all local components present in an EGM recording. This is of capital importance at advancing processing for cardiac electrophysiological procedures and reducing intervention times, as many diagnosis procedures are performed by comparing segments or late potentials in subsequent cardiac cycles.

Left Atrial Thrombus-Are All Atria and Appendages Equal?

Although the left atrial appendage (LAA) seems useless, it has several critical functions that are not fully known yet, such as the causes for being the main origin of cardioembolic stroke. Difficulties arise due to the extreme range of LAA morphologic variability, making the definition of normality challenging and hampering the stratification of thrombotic risk. Furthermore, obtaining quantitative metrics of its anatomy and function from patient data is not straightforward. A multimodality imaging approach, using advanced computational tools for their analysis, allows a complete characterization of the LAA to individualize medical decisions related to left atrial thrombosis patients.