The Year in Review of Clinical Cardiac Electrophysiology

Trends of Acute Kidney Injury Requiring Dialysis Among Hospitalized Patients Undergoing Invasive Electrophysiology Procedures

Electrophysiology (EP) procedures carry the risk of kidney injury due to contrast/hemodynamic fluctuations. We aim to evaluate the national epidemiology of acute kidney injury requiring dialysis (AKI-D) in patients undergoing EP procedures. Using the National Inpatient Sample, we included 2,747,605 adult hospitalizations undergoing invasive diagnostic EP procedures, ablation and implantable device placement from 2006 to 2014. We examined the temporal trend of AKI-D and outcomes associated with AKI-D. The rate of AKI-D increased significantly in both diagnostic/ablation group (8-21/10,000 hospitalizations from 2006 to 2014, P = 0.02) and implanted device group (19-44/10,000 hospitalizations from 2006 to 2014, P < 0.01), but it was explained by temporal changes in demographics and comorbidities. Cardiac resynchronization therapy and pacemaker placement had higher risk of AKI-D compared to implantable cardioverter-defibrillator placement (23 vs. 31 vs. 14/10,000 hospitalizations in cardiac resynchronization therapy, pacemaker placement, and implantable cardioverter-defibrillator group, respectively). Development of AKI-D was associated with significant increase in in-hospital mortality (adjusted odds ratio, 9.6 in diagnostic/ablation group, P < 0.01; adjusted odds ratio, 5.1 in device implantation group, P < 0.01) and with longer length of stay (22.5 vs. 4.5 days in diagnostic/ablation group, 21.1 vs. 5.7 days in implanted device group) and higher cost (282,775 vs. 94,076 USD in diagnostic/ablation group, 295,660 vs. 102,007 USD in implanted device group). The incidence of AKI-D after EP procedures increased over time but largely explained by the change of demographics and comorbidities. This increasing trend, however, was associated with significant increase in resource utilization and in-hospital mortality in these patients.

Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals

Diagnosis and treatment of dilated cardiomyopathy (DCM) is challenging due to a large variety of causes and disease stages. Computational models of cardiac electrophysiology (EP) can be used to improve the assessment and prognosis of DCM, plan therapies and predict their outcome, but require personalization. In this work, we present a data-driven approach to estimate the electrical diffusivity parameter of an EP model from standard 12-lead electrocardiograms (ECG). An efficient forward model based on a mono-domain, phenomenological Lattice-Boltzmann model of cardiac EP, and a boundary element-based mapping of potentials to the body surface is employed. The electrical diffusivity of myocardium, left ventricle and right ventricle endocardium is then estimated using polynomial regression which takes as input the QRS duration and electrical axis. After validating the forward model, we computed 9500 EP simulations on 19 different DCM patients in just under three seconds each to learn the regression model. Using this database, we quantify the intrinsic uncertainty of electrical diffusion for given ECG features and show in a leave-one-patient-out cross-validation that the regression method is able to predict myocardium diffusion within the uncertainty range. Finally, our approach is tested on the 19 cases using their clinical ECG. 84% of them could be personalized using our method, yielding mean prediction errors of 18.7ms for the QRS duration and 6.5° for the electrical axis, both values being within clinical acceptability. By providing an estimate of diffusion parameters from readily available clinical data, our data-driven approach could therefore constitute a first calibration step toward a more complete personalization of cardiac EP.

Atrial fibrillation therapy now and in the future: drugs, biologicals, and ablation

Atrial fibrillation (AF) is a complex disease with multiple inter-relating causes culminating in rapid, seemingly disorganized atrial activation. Therapy targeting AF is rapidly changing and improving. The purpose of this review is to summarize current state-of-the-art diagnostic and therapeutic modalities for treatment of AF. The review focuses on reviewing treatment as it relates to the pathophysiological basis of disease and reviews preclinical and clinical evidence for potential new diagnostic and therapeutic modalities, including imaging, biomarkers, pharmacological therapy, and ablative strategies for AF. Current ablation and drug therapy approaches to treating AF are largely based on treating the arrhythmia once the substrate occurs and is more effective in paroxysmal AF rather than persistent or permanent AF. However, there is much research aimed at prevention strategies, targeting AF substrate, so-called upstream therapy. Improved diagnostics, using imaging, genetics, and biomarkers, are needed to better identify subtypes of AF based on underlying substrate/mechanism to allow more directed therapeutic approaches. In addition, novel antiarrhythmics with more atrial specific effects may reduce limiting proarrhythmic side effects. Advances in ablation therapy are aimed at improving technology to reduce procedure time and in mechanism-targeted approaches.

Fast data-driven calibration of a cardiac electrophysiology model from images and ECG

Recent advances in computational electrophysiology (EP) models make them attractive for clinical use. We propose a novel data-driven approach to calibrate an EP model from standard 12-lead electrocardiograms (ECG), which are in contrast to invasive or dense body surface measurements widely available in clinical routine. With focus on cardiac depolarization, we first propose an efficient forward model of ECG by coupling a mono-domain, Lattice-Boltzmann model of cardiac EP to a boundary element formulation of body surface potentials. We then estimate a polynomial regression to predict myocardium, left ventricle and right ventricle endocardium electrical diffusion from QRS duration and ECG electrical axis. Training was performed on 4,200 ECG simulations, calculated in aproximately 3 s each, using different diffusion parameters on 13 patient geometries. This allowed quantifying diffusion uncertainty for given ECG parameters due to the ill-posed nature of the ECG problem. We show that our method is able to predict myocardium diffusion within the uncertainty range, yielding a prediction error of less than 5 ms for QRS duration and 2 degree for electrical axis. Prediction results compared favorably with those obtained with a standard optimization procedure, while being 60 times faster. Our data-driven model can thus constitute an efficient preliminary step prior to more refined EP personalization.