Konstantinos N. Aronis

Obesity, Exercise, Obstructive Sleep Apnea, and Modifiable Atherosclerotic Cardiovascular Disease Risk Factors in Atrial Fibrillation

Classically, the 3 pillars of atrial fibrillation (AF) management have included anticoagulation for prevention of thromboembolism, rhythm control, and rate control. In both prevention and management of AF, a growing body of evidence supports an increased role for comprehensive cardiac risk factor modification (RFM), herein defined as management of traditional modifiable cardiac risk factors, weight loss, and exercise. In this narrative review, we summarize the evidence demonstrating the importance of each facet of RFM in AF prevention and therapy. Additionally, we review emerging data on the importance of weight loss and cardiovascular exercise in prevention and management of AF.

Impact of number of co-existing rotors and inter-electrode distance on accuracy of rotor localization☆☆☆

BACKGROUND Conflicting evidence exists on the efficacy of focal impulse and rotor modulation on atrial fibrillation ablation. A potential explanation is inaccurate rotor localization from multiple rotors coexistence and a relatively large (9-11mm) inter-electrode distance (IED) of the multi-electrode basket catheter. METHODS AND RESULTS We studied a numerical model of cardiac action potential to reproduce one through seven rotors in a two-dimensional lattice. We estimated rotor location using phase singularity, Shannon entropy and dominant frequency. We then spatially downsampled the time series to create IEDs of 2-30mm. The error of rotor localization was measured with reference to the dynamics of phase singularity at the original spatial resolution (IED=1mm). IED has a significant impact on the error using all the methods. When only one rotor is present, the error increases exponentially as a function of IED. At the clinical IED of 10mm, the error is 3.8mm (phase singularity), 3.7mm (dominant frequency), and 11.8mm (Shannon entropy). When there are more than one rotors, the error of rotor localization increases 10-fold. The error based on the phase singularity method at the clinical IED of 10mm ranges from 30.0mm (two rotors) to 96.1mm (five rotors). CONCLUSIONS The magnitude of error of rotor localization using a clinically available basket catheter, in the presence of multiple rotors might be high enough to impact the accuracy of targeting during AF ablation. Improvement of catheter design and development of high-density mapping catheters may improve clinical outcomes of FIRM-guided AF ablation.

Rotors: How Do We Know When They Are Real?

See Article by Rodrigo et al A spiral wave is a common macroscopic behavior of excitable media observed in biological, chemical, and physical systems.1,2 In cardiac tissues, spiral wave reentry occurs when a wavefront of electric propagation encounters functionally inexcitable tissue and rotates around it in a vortex-like fashion.3 A rotor of a spiral wave is a rotation center from which a 2-dimensional spiral wave of excitation rotates outward. Phase mapping has been the de facto standard method to identify rotors and to track their trajectories in animal models of fibrillation.4 On phase maps, a rotor is defined as a phase singularity point around which the phase transitions through a complete cycle from −π to +π.3,5 Phase mapping can falsely detect phase singularities in the absence of rotors,6 and spiral waves can exist without phase singularities.7 Rotors and focal impulses have been proposed to be the underlying drivers of atrial fibrillation (AF) in human.3,8,9 This localized source hypothesis has resulted in the utilization of phase mapping in clinical practice, mainly in 2 settings: invasive focal impulse and rotor modulation (FIRM) and noninvasive electrocardiographic imaging (ECGI). FIRM uses phase mapping to locate stable rotors based on endocardial unipolar electrograms acquired by a 64-lead basket catheter.9 ECGI uses phase mapping to locate rotors based on unipolar electrograms acquired by ≈250 body surface electrocardiographic leads, which are back projected onto the epicardial surface, using a technique called inverse solution. FIRM and ECGI have …

Associations of Lipoprotein(a) levels with incident atrial fibrillation and Ischemic Stroke: The ARIC (Atherosclerosis Risk in Communities) study

Background Lipoprotein(a) (Lp[a]) is proatherosclerotic and prothrombotic, causally related to coronary disease, and associated with other cardiovascular diseases. The association of Lp(a) with incident atrial fibrillation (AF) and with ischemic stroke among individuals with AF remains to be elucidated. Methods and Results In the community‐based ARIC (Atherosclerosis Risk in Communities) study cohort, Lp(a) levels were measured by a Denka Seiken assay at visit 4 (1996–1998). We used multivariable‐adjusted Cox models to compare AF and ischemic stroke risk across Lp(a) levels. First, we evaluated incident AF in 9908 participants free of AF at baseline. AF was ascertained by electrocardiography at study visits, hospital International Statistical Classification of Diseases, 9th Revision (ICD‐9) codes, and death certificates. We then evaluated incident ischemic stroke in 10 127 participants free of stroke at baseline. Stroke was identified by annual phone calls, hospital ICD‐9 Revision codes, and death certificates. The baseline age was 62.7±5.6 years. Median Lp(a) levels were 13.3 mg/dL (interquartile range, 5.2–39.7 mg/dL). Median follow‐up was 13.9 and 15.8 years for AF and stroke, respectively. Lp(a) was not associated with incident AF (hazard ratio, 0.98; 95% confidence interval, 0.82–1.17), comparing those with Lp(a) ≥50 with those with Lp(a) <10 mg/dL. High Lp(a) was associated with a 42% relative increase in stroke risk among participants without AF (hazard ratio, 1.42; 95% confidence interval, 1.07–1.90) but not in those with AF (hazard ratio, 1.06; 95% confidence interval, 0.70–1.61 [P interaction for AF=0.25]). There were no interactions by race or sex. No association was found for cardioembolic stroke subtype. Conclusions High Lp(a) levels were not associated with incident AF. Lp(a) levels were associated with increased ischemic stroke risk, primarily among individuals without AF but not in those with AF.

Bridging the gap for lipid lowering therapy: plaque regression, coronary computed tomographic angiography, and imaging-guided personalized medicine

ABSTRACT Introduction: Lipid-lowering therapy effectively decreases cardiovascular risk on a population level, but it remains difficult to identify an individual patient’s personal risk reduction while following guideline directed medical therapy, leading to overtreatment in some patients and cardiovascular events in others. Recent improvements in cardiac CT technology provide the ability to directly assess an individual’s atherosclerotic disease burden, which has the potential to personalize risk assessment for lipid-lowering therapy. Areas covered: We review the current unmet need in identifying patients at elevated residual risk despite guideline directed medical therapy, the evidence behind plaque regression as a potential marker of therapeutic response, and highlight state-of-the-art advances in coronary computed tomographic angiography (CCTA) for measurement of quantitative and qualitative changes in coronary atherosclerosis over time. Literature search was performed using PubMed and Google Scholar for literature relevant to statin therapy and residual risk, coronary plaque regression measurement, and CCTA assessment of quantitative and qualitative change in coronary atherosclerosis. Expert commentary: We discuss the potential ability of CCTA to guide lipid-lowering therapy as a bridge between population and personalized medicine in the future, as well as the potential barriers to its use.

Is human atrial fibrillation stochastic or deterministic?—Insights from missing ordinal patterns and causal entropy-complexity plane analysis

The mechanism of atrial fibrillation (AF) maintenance in humans is yet to be determined. It remains controversial whether cardiac fibrillatory dynamics are the result of a deterministic or a stochastic process. Traditional methods to differentiate deterministic from stochastic processes have several limitations and are not reliably applied to short and noisy data obtained during clinical studies. The appearance of missing ordinal patterns (MOPs) using the Bandt-Pompe (BP) symbolization is indicative of deterministic dynamics and is robust to brief time series and experimental noise. Our aim was to evaluate whether human AF dynamics is the result of a stochastic or a deterministic process. We used 38 intracardiac atrial electrograms during AF from the coronary sinus of 10 patients undergoing catheter ablation of AF. We extracted the intervals between consecutive atrial depolarizations (AA interval) and converted the AA interval time series to their BP symbolic representation (embedding dimension 5, time delay 1). We generated 40 iterative amplitude-adjusted, Fourier-transform (IAAFT) surrogate data for each of the AA time series. IAAFT surrogates have the same frequency spectrum, autocorrelation, and probability distribution with the original time series. Using the BP symbolization, we compared the number of MOPs and the rate of MOP decay in the first 1000 timepoints of the original time series with that of the surrogate data. We calculated permutation entropy and permutation statistical complexity and represented each time series on the causal entropy-complexity plane. We demonstrated that (a) the number of MOPs in human AF is significantly higher compared to the surrogate data (2.7 ± 1.18 vs. 0.39 ± 0.28, p < 0.001); (b) the median rate of MOP decay in human AF was significantly lower compared with the surrogate data (6.58 × 10-3 vs. 7.79 × 10-3, p < 0.001); and (c) 81.6% of the individual recordings had a rate of decay lower than the 95% confidence intervals of their corresponding surrogates. On the causal entropy-complexity plane, human AF lay on the deterministic part of the plane that was located above the trajectory of fractional Brownian motion with different Hurst exponents on the plane. This analysis demonstrates that human AF dynamics does not arise from a rescaled linear stochastic process or a fractional noise, but either a deterministic or a nonlinear stochastic process. Our results justify the development and application of mathematical analysis and modeling tools to enable predictive control of human AF.