AIMS: To test the ability of four circulating biomarkers of fibrosis, and of low left atrial voltage, to predict recurrence of atrial fibrillation after catheter ablation. BACKGROUND: Circulating biomarkers potentially may be used to improve patient selection for atrial fibrillation ablation. Low voltage areas in the left atrium predict arrhythmia recurrence when mapped in sinus rhythm. This study tested type III procollagen N terminal peptide (PIIINP), galectin-3 (gal-3), fibroblast growth factor 23 (FGF-23), and type I collagen C terminal telopeptide (ICTP), and whether low voltage areas in the left atrium predicted atrial fibrillation recurrence, irrespective of the rhythm during mapping. METHODS: 92 atrial fibrillation ablation patients were studied. Biomarker levels in peripheral and intra-cardiac blood were measured with enzyme-linked immunosorbent assay. Low voltage (<0.5mV) was expressed as a proportion of the mapped left atrial surface area. Follow-up was one year. The primary endpoint was recurrence of arrhythmia. The secondary endpoint was a composite of recurrence despite two procedures, or after one procedure if no second procedure was undertaken. RESULTS: The biomarkers were not predictive of either endpoint. After multivariate Cox regression analysis, high proportion of low voltage area in the left atrium was found to predict the primary endpoint in sinus rhythm mapping (hazard ratio 4.323, 95% confidence interval 1.337-13.982, p = 0.014) and atrial fibrillation mapping (hazard ratio 5.195, 95% confidence interval 1.032-26.141, p = 0.046). This effect was also apparent for the secondary endpoint. CONCLUSION: The studied biomarkers do not predict arrhythmia recurrence after catheter ablation. Left atrial voltage is an independent predictor of recurrence, whether the left atrium is mapped in atrial fibrillation or sinus rhythm.
Atrial fibrillation (AF) is the most common cardiac arrhythmia, and the total number of AF patients is constantly increasing. The mechanisms leading to and sustaining AF are not completely understood yet. Heterogeneities in atrial electrophysiology seem to play an important role in this context. Although some heterogeneities have been used in in-silico human atrial modeling studies, they have not been thoroughly investigated. In this study, the original electrophysiological (EP) models of Courtemanche et al., Nygren et al. and Maleckar et al. were adjusted to reproduce action potentials in 13 atrial regions. The parameter sets were validated against experimental action potential duration data and ECG data from patients with AV block. The use of the heterogeneous EP model led to a more synchronized repolarization sequence in a variety of 3D atrial anatomical models. Combination of the heterogeneous EP model with a model of persistent AF-remodeled electrophysiology led to a drastic change in cell electrophysiology. Simulated Ta-waves were significantly shorter under the remodeling. The heterogeneities in cell electrophysiology explain the previously observed Ta-wave effects. The results mark an important step toward the reliable simulation of the atrial repolarization sequence, give a deeper understanding of the mechanism of atrial repolarization and enable further clinical investigations.
Left atrial fibrosis is thought to contribute to the manifestation of atrial fibrillation (AF). Late Gadolinium enhancement (LGE) MRI has the potential to image regions of low perfusion, which can be related to fibrosis. We show that a simulation with a patient-specific model including left atrial regional fibrosis derived from LGE-MRI reproduces local activation in the left atrium more precisely than the regular simulation without fibrosis. AF simulations showed a spontaneous termination of the arrhythmia in the absence of fibrosis and a stable rotor center in the presence of fibrosis. The methodology may provide a tool for a deeper understanding of the mechanisms maintaining AF and eventually also for the planning of substrate-guided ablation procedures in the future.
Computational atrial models aid the understanding of pathological mechanisms and therapeutic measures in basic research. The use of biophysical models in a clinical environment requires methods to personalize the anatomy and electrophysiology (EP). Strategies for the automation of model generation and for evaluation are needed. In this manuscript, the current efforts of clinical atrial modeling in the euHeart project are summarized within the context of recent publications in this field. Model-based segmentation methods allow for the automatic generation of ready-to-simulate patient-specific anatomical models. EP models can be adapted to patient groups based on a-priori knowledge, and to the individual without significant further data acquisition. ECG and intracardiac data build the basis for excitation personalization. Information from late enhancement (LE) MRI can be used to evaluate the success of radio-frequency ablation (RFA) procedures and interactive virtual atria pave the way for RFA planning. Atrial modeling is currently in a transition from the sole use in basic research to future clinical applications. The proposed methods build the framework for model-based diagnosis and therapy evaluation and planning. Complex models allow to understand biophysical mechanisms and enable the development of simplified models for clinical applications.
Multiscale cardiac modeling has made great advances over the last decade. Highly detailed atrial models were created and used for the investigation of initiation and perpetuation of atrial fibrillation. The next challenge is the use of personalized atrial models in clinical practice. In this study, a framework of simple and robust tools is presented, which enables the generation and validation of patient-specific anatomical and electrophysiological atrial models. Introduction of rule-based atrial fiber orientation produced a realistic excitation sequence and a better correlation to the measured electrocardiograms. Personalization of the global conduction velocity lead to a precise match of the measured P-wave duration. The use of a virtual cohort of nine patient and volunteer models averaged out possible model-specific errors. Intra-atrial excitation conduction was personalized manually from left atrial local activation time maps. Inclusion of LE-MRI data into the simulations revealed possible gaps in ablation lesions. A fast marching level set approach to compute atrial depolarization was extended to incorporate anisotropy and conduction velocity heterogeneities and reproduced the monodomain solution. The presented chain of tools is an important step towards the use of atrial models for the patient-specific AF diagnosis and ablation therapy planing.
BACKGROUND: The prevalence of atrial fibrillation is increased in patients with end-stage renal disease. Previous studies suggested that extracellular electrolyte alterations caused by hemodialysis (HD) therapy could be proarrhythmic. METHODS: Multiscale models were used for a consequent analysis of the effects of extracellular ion concentration changes on atrial electrophysiology. Simulations were based on measured electrolyte concentrations from patients with end-stage renal disease. RESULTS: Simulated conduction velocity and effective refractory period are decreased at the end of an HD session, with potassium having the strongest influence. P-wave is prolonged in patients undergoing HD therapy in the simulation as in measurements. CONCLUSIONS: Electrolyte concentration alterations impact atrial electrophysiology from the action potential level to the P-wave and can be proarrhythmic, especially because of induced hypokalemia. Analysis of blood electrolytes enables patient-specific electrophysiology modeling. We are providing a tool to investigate atrial arrhythmias associated with HD therapy, which, in the future, can be used to prevent such complications.
ECG imaging is an emerging technology for the reconstruction of cardiac electric activity from non-invasively measured body surface potential maps. In this case report, we present the first evaluation of transmurally imaged activation times against endocardially reconstructed isochrones for a case of sustained monomorphic ventricular tachycardia (VT). Computer models of the thorax and whole heart were produced from MR images. A recently published approach was applied to facilitate electrode localization in the catheter laboratory, which allows for the acquisition of body surface potential maps while performing non-contact mapping for the reconstruction of local activation times. ECG imaging was then realized using Tikhonov regularization with spatio-temporal smoothing as proposed by Huiskamp and Greensite and further with the spline-based approach by Erem et al. Activation times were computed from transmurally reconstructed transmembrane voltages. The results showed good qualitative agreement between the non-invasively and invasively reconstructed activation times. Also, low amplitudes in the imaged transmembrane voltages were found to correlate with volumes of scar and grey zone in delayed gadolinium enhancement cardiac MR. The study underlines the ability of ECG imaging to produce activation times of ventricular electric activity-and to represent effects of scar tissue in the imaged transmembrane voltages.
Electrocardiographic imaging (ECG imaging) is a method to depict electrophysiological processes in the heart. It is an emerging technology with the potential of making the therapy of cardiac arrhythmia less invasive, less expensive, and more precise. A major challenge for integrating the method into clinical workflow is the seamless and correct identification and localization of electrodes on the thorax and their assignment to recorded channels. This work proposes a camera-based system, which can localize all electrode positions at once and to an accuracy of approximately 1+/-1 mm. A system for automatic identification of individual electrodes is implemented that overcomes the need of manual annotation. For this purpose, a system of markers is suggested, which facilitates a precise localization to subpixel accuracy and robust identification using an error-correcting code. The accuracy of the presented system in identifying and localizing electrodes is validated in a phantom study. Its overall capability is demonstrated in a clinical scenario.
Cardiac electrophysiology procedures are routinely used to treat patients with rhythm disorders. The success rates of ablation procedures and cardiac resynchronization therapy are still sub-optimal. Recent advances in medical imaging, image processing and cardiac biophysical modeling have the potential to improve patient outcome. This manuscript provides an overview of how these advances have been translated into the clinical environment.
Current models of the human atria represent geometries of single individuals or base on statistical data. We present a work-flow for the creation of patient-specific atrial models. Furthermore we show a framework to compare simulated P- waves and body surface potential maps (BSPMs) of individual patients with measurements. Models of the atrial and thorax anatomy were segmented from MRI data. Volumetric atrial models were semi-automatically enhanced with electrophys- iologically (EP) relevant structures. Simulations were performed on an anisotropic voxel-based mesh and were forward calculated to obtain simulated BSPMs. BSPMs were acquired using a 64 electrode ECG system. Comparison of simulated and measured P-waves in Einthoven leads showed a general agreement of both, although no personalization of the atrial electrophysiology model was performed. P-wave duration was longer in the simulations, highlighting the need for elec- trophysiological model personalization. Simulated and measured BSPMs revealed similar patterns. The presented method enables realistic simulations of atrial activation on patient-specific volumetric atrial models with EP relevant myocardial structures resulting in computed ECGs (P-wave) and BSPMs with show physiological morphologies
Atrial myofiber orientation is complex and has multiple discrete layers and bundles. A novel robust semi-automatic method to incorporate atrial anisotropy and heterogeneities into patient-specific models is introduced. The user needs to provide 22 distinct seed-points from which a network of auxiliary lines is constructed. These are used to define fiber orientation and myocardial bundles. The method was applied to 14 patient-specific volumetric models derived from CT, MRI and photographic data. Initial electrophysiological simulations show a significant influence of anisotropy and heterogeneity on the excitation pattern and P-wave duration (20.7% shortening). Fiber modeling results show good overall correspondence with anatomical data. Minor modeling errors are observed if more than four pulmonary veins exist in the model. The method is an important step towards creating realistic patient-specific atrial models for clinical applications.
Abstract. Atrial fibrillation (AF) is the most common cardiac arrhyth- mia. Patient-specific computational modeling of the atria can provide a better understanding about mechanisms underlying the arrhythmia and will potentially be used for model-based ablation therapy evaluation and planning. Electrical excitation spreads from the left to the right atrium at discrete locations. The location of the muscular bridges cannot be determined from image data. In the present study, left atrial activation sources were manually identified in local activation time maps of 4 AF patients. This information was used to adjust rule-based placed intera- trial bridges in anatomical atrial models of the patients. Sinus rhythm simulations showed a better qualitative agreement to the measured left atrial activation patterns after the adjustment of the bridges. For one patient, the simulated body surface potential (BSP) pattern after the adjustment correlated better to measured BSP maps. The results show that the fusion of intracardiac electrical measurements of early left atrial activation can be used to refine patient atria models with information of the myocardial structure which cannot be imaged. In future, such personalized atrial models may be used to support EP interventions.
Fibrosis is strongly linked with the mechanisms of atrial fibrillation (AF), the most common arrhythmia. Direct electrotonic coupling between atrial myocytes and fibroblast has been suggested to contribute to these mechanisms. We use a 3D biophysical model of the atria to study the effects of fibrosis on atrial electrophysiology. Realistic tissue geometry, regional heterogeneity and myofiber anisotropy are integrated in the model. The model also accounts for the effects AF induced ionic remodeling, which has been shown to promote AF. The model simulations demonstrated that fibrosis significantly reduced both the atrial conduction velocity and action potential duration. Both these factors contributed to a large (45%) reduction of the atrial activation wavelength. This is comparable with the wavelength reduction (65%) due to ionic remodeling. As a result, the sustenance of re- entrant waves in the 3D atria was substantially increased with both fibrosis and remodeling. Hence, the elecrotonic changes induced by fibrosis can be comparable to those due to ionic remodeling, and both factors can provide substrate for re-entry in the 3D atria model.
# BackgroundMethods for the non-invasive imaging of atrial activation times could provide cardiologists with valuable information on pathological excitation conduction patterns, e.g. for treatment planning.In this study, the source representation functions used in the critical times method (Greensite et al. 1997) are expanded with a range adjustment to generate more accurate activation time maps from ECG measurements.# Materials and methodsExcitation conduction in the atria was simulated for various excitation origins with a cellular automaton. Body surface potential maps were obtained from forward calculations using a bidomain approach.As introduced in Greensite et al. 1995, the method of critical times can be used to quantitatively localize critical point locations and times, and to reconstruct surface activation in a qualitative manner. To this end, all atrial surface nodes were treated as critical points and the corresponding critical times were reconstructed using the zero-crossing method by Greensite, which is the subtraction of the two representation functions.For the heart surface nodes, it was observed that the minuend representation function in the zero-crossing term is often by magnitudes greater than the subtrahend. For the minuend to not dominate the subtrahend before the desired zero-crossing, which is supposed to occur at the time of depolarization, the minuend was therefore weighted with a sigmoid function and normalized to the range of the subtrahend.# ResultsAtrial activation times were reconstructed with both the zero-crossing method by Greensite and the sigmoid-weighted zero-crossing. Two effects were observed. The overall reconstruction quality of the established method improves in the presence of 30dB additive white Gaussian noise. This effect results from a gradual offset that is imposed on the reconstructed critical times under these circumstances (see Huiskamp and Greensite 1997). Second, it could be shown that a significant reduction of reconstruction error can be achieved in the absence of noise with the sigmoid-weighted adaptation of the formula.# ConclusionWith the newly introduced sigmoidal normalization, the quality of reconstruction can be improved significantly if noise levels are below 30dB. Clinical studies need to be made in order to validate the method and assess its performance in a realistic environment.
Body surface potential mapping (BSPM) can be used to non- invasively measure the electrical activity of the heart using a dense set of thorax electrodes and a CT/MR scan of the thorax to solve the inverse problem of electrophysiology (ECGi). This technique now shows potential clinical value for the assessment and treatment of patients with arrhythmias. Co-localisation of the electrode positions and the CT/MR thorax scan is essential. This manuscript describes a method to perform the co-localisation using multiple biplane X-ray images. The electrodes are automatically detected and paired in the X-ray images. Then the 3D positions of the electrodes are computed and mapped onto the thorax surface derived from CT/MR. The proposed method is based on a multi-scale blob detection algorithm and the generalized Hough transform, which can automatically discriminate the leads used for BSPM from other ECG leads. The pairing method is based on epi-polar constraint matching and line pattern detection which assumes that BSPM electrodes are arranged in strips. The proposed methods are tested on a thorax phantom and two clinical cases. Results show an accuracy of 0.33 ± 0.20mm for detecting electrodes in the X-ray images and a success rate of 95.4%. The automatic pairing method achieves a 91.2% success rate.