Publications by authors named "Sanjiv M Narayan"

210 Publications

Immediate and Delayed Response of Simulated Human Atrial Myocytes to Clinically-Relevant Hypokalemia.

Front Physiol 2021 26;12:651162. Epub 2021 May 26.

Department of Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada.

Although plasma electrolyte levels are quickly and precisely regulated in the mammalian cardiovascular system, even small transient changes in K, Na, Ca, and/or Mg can significantly alter physiological responses in the heart, blood vessels, and intrinsic (intracardiac) autonomic nervous system. We have used mathematical models of the human atrial action potential (AP) to explore the electrophysiological mechanisms that underlie changes in resting potential (V) and the AP following decreases in plasma K, [K], that were selected to mimic clinical hypokalemia. Such changes may be associated with arrhythmias and are commonly encountered in patients (i) in therapy for hypertension and heart failure; (ii) undergoing renal dialysis; (iii) with any disease with acid-base imbalance; or (iv) post-operatively. Our study emphasizes clinically-relevant hypokalemic conditions, corresponding to [K] reductions of approximately 1.5 mM from the normal value of 4 to 4.5 mM. We show how the resulting electrophysiological responses in human atrial myocytes progress within two distinct time frames: (i) Immediately after [K] is reduced, the K-sensing mechanism of the background inward rectifier current (I) responds. Specifically, its highly non-linear current-voltage relationship changes significantly as judged by the voltage dependence of its region of outward current. This rapidly alters, and sometimes even depolarizes, V and can also markedly prolong the final repolarization phase of the AP, thus modulating excitability and refractoriness. (ii) A second much slower electrophysiological response (developing 5-10 minutes after [K] is reduced) results from alterations in the intracellular electrolyte balance. A progressive shift in intracellular [Na] causes a change in the outward electrogenic current generated by the Na/K pump, thereby modifying V and AP repolarization and changing the human atrial electrophysiological substrate. In this study, these two effects were investigated quantitatively, using seven published models of the human atrial AP. This highlighted the important role of I rectification when analyzing both the mechanisms by which [K] regulates V and how the AP waveform may contribute to "trigger" mechanisms within the proarrhythmic substrate. Our simulations complement and extend previous studies aimed at understanding key factors by which decreases in [K] can produce effects that are known to promote atrial arrhythmias in human hearts.
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http://dx.doi.org/10.3389/fphys.2021.651162DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188899PMC
May 2021

Three-dimensional transmural mapping to guide ventricular arrhythmia ablation.

Heart Rhythm 2021 Aug 6;18(8):1452-1453. Epub 2021 May 6.

Cardiovascular Institute and Cardiovascular Division, Stanford University, Stanford, California.

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http://dx.doi.org/10.1016/j.hrthm.2021.05.003DOI Listing
August 2021

Re-interpreting complex atrial tachycardia maps using global atrial vectors.

J Cardiovasc Electrophysiol 2021 07 20;32(7):1918-1920. Epub 2021 May 20.

Cardiovascular Institute, Cardiology Division, Department of Medicine, Stanford University, Stanford, California, USA.

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http://dx.doi.org/10.1111/jce.15073DOI Listing
July 2021

Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry.

PLoS One 2021 9;16(4):e0249873. Epub 2021 Apr 9.

Department of Physics, UC San Diego, La Jolla, California, United States of America.

Background: The rotational activation created by spiral waves may be a mechanism for atrial fibrillation (AF), yet it is unclear how activation patterns obtained from endocardial baskets are influenced by the 3D geometric curvature of the atrium or 'unfolding' into 2D maps. We develop algorithms that can visualize spiral waves and their tip locations on curved atrial geometries. We use these algorithms to quantify differences in AF maps and spiral tip locations between 3D basket reconstructions, projection onto 3D anatomical shells and unfolded 2D surfaces.

Methods: We tested our algorithms in N = 20 patients in whom AF was recorded from 64-pole baskets (Abbott, CA). Phase maps were generated by non-proprietary software to identify the tips of spiral waves, indicated by phase singularities. The number and density of spiral tips were compared in patient-specific 3D shells constructed from the basket, as well as 3D maps from clinical electroanatomic mapping systems and 2D maps.

Results: Patients (59.4±12.7 yrs, 60% M) showed 1.7±0.8 phase singularities/patient, in whom ablation terminated AF in 11/20 patients (55%). There was no difference in the location of phase singularities, between 3D curved surfaces and 2D unfolded surfaces, with a median correlation coefficient between phase singularity density maps of 0.985 (0.978-0.990). No significant impact was noted by phase singularities location in more curved regions or relative to the basket location (p>0.1).

Conclusions: AF maps and phase singularities mapped by endocardial baskets are qualitatively and quantitatively similar whether calculated by 3D phase maps on patient-specific curved atrial geometries or in 2D. Phase maps on patient-specific geometries may be easier to interpret relative to critical structures for ablation planning.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249873PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034734PMC
April 2021

Competing risks in patients with primary prevention implantable cardioverter-defibrillators: Global Electrical Heterogeneity and Clinical Outcomes study.

Heart Rhythm 2021 Jun 6;18(6):977-986. Epub 2021 Mar 6.

Department of Medicine, Cardiovascular Division, Oregon Health & Science University, Portland, Oregon. Electronic address:

Background: Global electrical heterogeneity (GEH) is associated with sudden cardiac death in the general population. Its utility in patients with systolic heart failure who are candidates for primary prevention (PP) implantable cardioverter-defibrillators (ICDs) is unclear.

Objective: The purpose of this study was to investigate whether GEH is associated with sustained ventricular tachycardia/ventricular fibrillation leading to appropriate ICD therapies in patients with heart failure and PP ICDs.

Methods: We conducted a multicenter retrospective cohort study. GEH was measured by spatial ventricular gradient (SVG) direction (azimuth and elevation) and magnitude, QRS-T angle, and sum absolute QRST integral on preimplant 12-lead electrocardiograms. Survival analysis using cause-specific hazard functions compared the strength of associations with 2 competing outcomes: sustained ventricular tachycardia/ventricular fibrillation leading to appropriate ICD therapies and all-cause death without appropriate ICD therapies.

Results: We analyzed 2668 patients (mean age 63 ± 12 years; 624 (23%) female; 78% white; 43% nonischemic cardiomyopathy; left ventricular ejection fraction 28% ± 11% from 6 academic medical centers). After adjustment for demographic, clinical, device, and traditional electrocardiographic characteristics, SVG elevation (hazard ratio [HR] per 1SD 1.14; 95% confidence interval [CI] 1.04-1.25; P = .004), SVG azimuth (HR per 1SD 1.12; 95% CI 1.01-1.24; P = .039), SVG magnitude (HR per 1SD 0.75; 95% CI 0.66-0.85; P < .0001), and QRS-T angle (HR per 1SD 1.21; 95% CI 1.08-1.36; P = .001) were associated with appropriate ICD therapies. Sum absolute QRST integral had different associations in infarct-related cardiomyopathy (HR 1.29; 95% CI 1.04-1.60) and nonischemic cardiomyopathy (HR 0.78; 95% CI 0.62-0.96) (P = .022).

Conclusion: In patients with PP ICDs, GEH is independently associated with appropriate ICD therapies. The SVG vector points in distinctly different directions in patients with 2 competing outcomes.
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http://dx.doi.org/10.1016/j.hrthm.2021.03.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169548PMC
June 2021

Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping.

Front Physiol 2020 6;11:611266. Epub 2021 Jan 6.

Stanford University School of Medicine, Stanford, CA, United States.

Regional differences in activation rates may contribute to the electrical substrates that maintain atrial fibrillation (AF), and estimating them non-invasively may help guide ablation or select anti-arrhythmic medications. We tested whether non-invasive assessment of regional AF rate accurately represents intracardiac recordings. : In 47 patients with AF (27 persistent, age 63 ± 13 years) we performed 57-lead non-invasive Electrocardiographic Imaging (ECGI) in AF, simultaneously with 64-pole intracardiac signals of both atria. ECGI was reconstructed by Tikhonov regularization. We constructed personalized 3D AF rate distribution maps by Dominant Frequency (DF) analysis from intracardiac and non-invasive recordings. Raw intracardiac and non-invasive DF differed substantially, by 0.54 Hz [0.13 - 1.37] across bi-atrial regions ( = 0.11). Filtering by high spectral organization reduced this difference to 0.10 Hz (cycle length difference of 1 - 11 ms) [0.03 - 0.42] for patient-level comparisons ( = 0.62), and 0.19 Hz [0.03 - 0.59] and 0.20 Hz [0.04 - 0.61] for median and highest DF, respectively. Non-invasive and highest DF predicted acute ablation success ( = 0.04). Non-invasive estimation of atrial activation rates is feasible and, when filtered by high spectral organization, provide a moderate estimate of intracardiac recording rates in AF. Non-invasive technology could be an effective tool to identify patients who may respond to AF ablation for personalized therapy.
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http://dx.doi.org/10.3389/fphys.2020.611266DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873897PMC
January 2021

Prognostication for Sudden Cardiac Arrest Patients Achieving ROSC.

J Am Coll Cardiol 2021 02;77(4):372-374

Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.

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http://dx.doi.org/10.1016/j.jacc.2020.11.052DOI Listing
February 2021

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

Circ Res 2021 01 10;128(2):172-184. Epub 2020 Nov 10.

Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University.

Rationale: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.

Objective: To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes.

Methods And Results: We recorded 5706 ventricular MAPs in 42 patients with coronary artery disease and left ventricular ejection fraction ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points: (1) sustained VT/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI, 0.76-1.00) and 0.91 for mortality (95% CI, 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT/VF.

Conclusions: Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.
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http://dx.doi.org/10.1161/CIRCRESAHA.120.317345DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855939PMC
January 2021

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.

Nat Rev Cardiol 2021 02 9;18(2):75-91. Epub 2020 Oct 9.

Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.

Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.
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http://dx.doi.org/10.1038/s41569-020-00445-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7545156PMC
February 2021

Re-evaluating the multiple wavelet hypothesis for atrial fibrillation.

Heart Rhythm 2020 12 13;17(12):2219-2220. Epub 2020 Jul 13.

Cardiovascular Institute and Division of Cardiology, Stanford University, Stanford, California. Electronic address:

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http://dx.doi.org/10.1016/j.hrthm.2020.07.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704678PMC
December 2020

Populations of in silico myocytes and tissues reveal synergy of multiatrial-predominant K -current block in atrial fibrillation.

Br J Pharmacol 2020 10 9;177(19):4497-4515. Epub 2020 Aug 9.

Department of Pharmacology, University of California, Davis, CA, USA.

Background And Purpose: Pharmacotherapy of atrial fibrillation (AF), the most common cardiac arrhythmia, remains unsatisfactory due to low efficacy and safety concerns. New therapeutic strategies target atrial-predominant ion-channels and involve multichannel block (poly)therapy. As AF is characterized by rapid and irregular atrial activations, compounds displaying potent antiarrhythmic effects at fast and minimal effects at slow rates are desirable. We present a novel systems pharmacology framework to quantitatively evaluate synergistic anti-AF effects of combined block of multiple atrial-predominant K currents (ultra-rapid delayed rectifier K current, I , small conductance Ca -activated K current, I , K 3.1 2-pore-domain K current, I ) in AF.

Experimental Approach: We constructed experimentally calibrated populations of virtual atrial myocyte models in normal sinus rhythm and AF-remodelled conditions using two distinct, well-established atrial models. Sensitivity analyses on our atrial populations was used to investigate the rate dependence of action potential duration (APD) changes due to blocking I , I or I and interactions caused by blocking of these currents in modulating APD. Block was simulated in both single myocytes and one-dimensional tissue strands to confirm insights from the sensitivity analyses and examine anti-arrhythmic effects of multi-atrial-predominant K current block in single cells and coupled tissue.

Key Results: In both virtual atrial myocytes and tissues, multiple atrial-predominant K -current block promoted favourable positive rate-dependent APD prolongation and displayed positive rate-dependent synergy, that is, increasing synergistic antiarrhythmic effects at fast pacing versus slow rates.

Conclusion And Implications: Simultaneous block of multiple atrial-predominant K currents may be a valuable antiarrhythmic pharmacotherapeutic strategy for AF.
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http://dx.doi.org/10.1111/bph.15198DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484508PMC
October 2020

Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation.

Circ Arrhythm Electrophysiol 2020 08 6;13(8):e008160. Epub 2020 Jul 6.

Department of Medicine (M.I.A., A.J.R., J.A.B.Z., T.B., P.C., P.J.W., S.M.N.), Stanford University.

Background: Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained.

Methods: We performed panoramic recording of bi-atrial electrical signals in AF. We used the Hilbert-transform to produce 175 000 image grids in 35 patients, labeled for rotational activation by experts who showed consistency but with variability (kappa [κ]=0.79). In each patient, ablation terminated AF. A CNN was developed and trained on 100 000 AF image grids, validated on 25 000 grids, then tested on a separate 50 000 grids.

Results: In the separate test cohort (50 000 grids), CNN reproducibly classified AF image grids into those with/without rotational sites with 95.0% accuracy (CI, 94.8%-95.2%). This accuracy exceeded that of support vector machines, traditional linear discriminant, and k-nearest neighbor statistical analyses. To probe the CNN, we applied gradient-weighted class activation mapping which revealed that the decision logic closely mimicked rules used by experts (C statistic 0.96).

Conclusions: CNNs improved the classification of intracardiac AF maps compared with other analyses and agreed with expert evaluation. Novel explainability analyses revealed that the CNN operated using a decision logic similar to rules used by experts, even though these rules were not provided in training. We thus describe a scaleable platform for robust comparisons of complex AF data from multiple systems, which may provide immediate clinical utility to guide ablation. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02997254. Graphic Abstract: A graphic abstract is available for this article.
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http://dx.doi.org/10.1161/CIRCEP.119.008160DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438307PMC
August 2020

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Circ Arrhythm Electrophysiol 2020 08 6;13(8):e007952. Epub 2020 Jul 6.

Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.).

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.
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http://dx.doi.org/10.1161/CIRCEP.119.007952DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808396PMC
August 2020

Continuous ablation improves lesion maturation compared with intermittent ablation strategies.

J Cardiovasc Electrophysiol 2020 07 27;31(7):1687-1693. Epub 2020 Apr 27.

Section of Cardiac Electrophysiology, Division of Cardiovascular Medicine, Stanford University, Stanford, California.

Background: Interrupted ablation is increasingly proposed as part of high-power short-duration radiofrequency ablation (RFA) strategies and may also result from loss of contact from respiratory patterns or cardiac motion. To study the extent that ablation interruption affects lesions.

Methods: In ex vivo and in vivo experiments, lesion characteristics and tissue temperatures were compared between continuous (group 1) and interrupted (groups 2 and 3) RFA with equal total ablation duration and contact force. Extended duration ablation lesions were also characterized from 1 to 5 minutes.

Results: In the ex vivo study, continuous RFA (group 1) produced larger total lesion volumes compared with each interrupted ablation lesion group (273.8 ± 36.5 vs 205.1 ± 34.2 vs 174.3 ± 32.3 mm , all P < .001). Peak temperatures for group 1 were higher at 3 and 5 mm than groups 2 and 3. In vivo, continuous ablation resulted in larger lesions, greater lesion depths, and higher tissue temperatures. Longer ablation durations created larger lesion volumes and increased lesion depths. However, after 3 minutes of ablation, the rate of lesion volume, and depth formation decreased.

Conclusions: Continuous RFA delivery resulted in larger and deeper lesions with higher tissue temperatures compared with interrupted ablation. This study may have implications for high-power short duration ablation strategies, motivates strategies to reduce variations in ablation delivery, and provides an upper limit for ablation duration beyond which power delivery has diminishing returns.
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http://dx.doi.org/10.1111/jce.14510DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534961PMC
July 2020

Termination of persistent atrial fibrillation by ablating sites that control large atrial areas.

Europace 2020 06;22(6):897-905

Department of Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, MC 5773, Stanford, CA 94305, USA.

Aims: Persistent atrial fibrillation (AF) has been explained by multiple mechanisms which, while they conflict, all agree that more disorganized AF is more difficult to treat than organized AF. We hypothesized that persistent AF consists of interacting organized areas which may enlarge, shrink or coalesce, and that patients whose AF areas enlarge by ablation are more likely to respond to therapy.

Methods And Results: We mapped vectorial propagation in persistent AF using wavefront fields (WFF), constructed from raw unipolar electrograms at 64-pole basket catheters, during ablation until termination (Group 1, N = 20 patients) or cardioversion (Group 2, N = 20 patients). Wavefront field mapping of patients (age 61.1 ± 13.2 years, left atrium 47.1 ± 6.9 mm) at baseline showed 4.6 ± 1.0 organized areas, each separated by disorganization. Ablation of sites that led to termination controlled larger organized area than competing sites (44.1 ± 11.1% vs. 22.4 ± 7.0%, P < 0.001). In Group 1, ablation progressively enlarged unablated areas (rising from 32.2 ± 15.7% to 44.1 ± 11.1% of mapped atrium, P < 0.0001). In Group 2, organized areas did not enlarge but contracted during ablation (23.6 ± 6.3% to 15.2 ± 5.6%, P < 0.0001).

Conclusion: Mapping wavefront vectors in persistent AF revealed competing organized areas. Ablation that progressively enlarged remaining areas was acutely successful, and sites where ablation terminated AF were surrounded by large organized areas. Patients in whom large organized areas did not emerge during ablation did not exhibit AF termination. Further studies should define how fibrillatory activity is organized within such areas and whether this approach can guide ablation.
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http://dx.doi.org/10.1093/europace/euaa018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273336PMC
June 2020

The interconnected atrium: Acute impact of pulmonary vein isolation on remote atrial tissue.

J Cardiovasc Electrophysiol 2020 04 23;31(4):913-914. Epub 2020 Feb 23.

Department of Medicine and Stanford Cardiovascular Institute, Stanford University, Stanford, California.

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http://dx.doi.org/10.1111/jce.14389DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500578PMC
April 2020

Getting in Contact With Atrial Fibrillation or Not.

JACC Clin Electrophysiol 2020 02;6(2):182-184

Department of Medicine, and Cardiovascular Institute, Stanford University, Stanford University Medical Center, Stanford, California, USA. Electronic address:

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http://dx.doi.org/10.1016/j.jacep.2019.10.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534962PMC
February 2020

Noninvasive Assessment of Complexity of Atrial Fibrillation: Correlation With Contact Mapping and Impact of Ablation.

Circ Arrhythm Electrophysiol 2020 03 13;13(3):e007700. Epub 2020 Feb 13.

Cardiology Department, Hospital General Universitario Gregorio Marañón, Instituto de Investigacion Sanitaria Gregorio Marañon (IISGM) (M.R., A.M.C., I.H.-R., A.L., F.F.-A., F.A.), Madrid, Spain.

Background: It is difficult to noninvasively phenotype atrial fibrillation (AF) in a way that reflects clinical end points such as response to therapy. We set out to map electrical patterns of disorganization and regions of reentrant activity in AF from the body surface using electrocardiographic imaging, calibrated to panoramic intracardiac recordings and referenced to AF termination by ablation.

Methods: Bi-atrial intracardiac electrograms of 47 patients with AF at ablation (30 persistent, 29 male, 63±9 years) were recorded with 64-pole basket catheters and simultaneous 57-lead body surface ECGs. Atrial epicardial electrical activity was reconstructed and organized sites were invasively and noninvasively tracked in 3-dimension using phase singularity. In a subset of 17 patients, sites of AF organization were targeted for ablation.

Results: Body surface mapping showed greater AF organization near intracardially detected drivers than elsewhere, both in phase singularity density (2.3±2.1 versus 1.9±1.6; =0.02) and number of drivers (3.2±2.3 versus 2.7±1.7; =0.02). Complexity, defined as the number of stable AF reentrant sites, was concordant between noninvasive and invasive methods (r=0.5; CC=0.71). In the subset receiving targeted ablation, AF complexity showed lower values in those in whom AF terminated than those in whom AF did not terminate (<0.01).

Conclusions: AF complexity tracked noninvasively correlates well with organized and disorganized regions detected by panoramic intracardiac mapping and correlates with the acute outcome by ablation. This approach may assist in bedside monitoring of therapy or in improving the efficacy of ongoing ablation procedures.
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http://dx.doi.org/10.1161/CIRCEP.119.007700DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508259PMC
March 2020

Automatic quality electrogram assessment improves phase-based reentrant activity identification in atrial fibrillation.

Comput Biol Med 2020 02 27;117:103593. Epub 2019 Dec 27.

ITACA Institute, Universitat Politècnica de València, Spain. Electronic address:

Identification of reentrant activity driving atrial fibrillation (AF) is increasingly important to ablative therapies. The goal of this work is to study how the automatically-classified quality of the electrograms (EGMs) affects reentrant AF driver localization. EGMs from 259 AF episodes obtained from 29 AF patients were recorded using 64-poles basket catheters and were manually classified according to their quality. An algorithm capable of identifying signal quality was developed using time and spectral domain parameters. Electrical reentries were identified in 3D phase maps using phase transform and were compared with those obtained with a 2D activation-based method. Effect of EGM quality was studied by discarding 3D phase reentries detected in regions with low-quality EGMs. Removal of reentries identified by 3D phase analysis in regions with low-quality EGMs improved its performance, increasing the area under the ROC curve (AUC) from 0.69 to 0.80. The EGMs quality classification algorithm showed an accurate performance for EGM classification (AUC 0.94) and reentry detection (AUC 0.80). Automatic classification of EGM quality based on time and spectral signal parameters is feasible and accurate, avoiding the manual labelling. Discard of reentries identified in regions with automatically-detected poor-quality EGMs improved the specificity of the 3D phase-based method for AF driver identification.
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http://dx.doi.org/10.1016/j.compbiomed.2019.103593DOI Listing
February 2020

Catheter ablation or surgery to eliminate longstanding persistent atrial fibrillation.

Int J Cardiol 2020 03 27;303:54-55. Epub 2019 Dec 27.

Department of Medicine, Stanford University, Stanford, USA; Cardiovascular Institute, Stanford University, Stanford, USA. Electronic address:

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http://dx.doi.org/10.1016/j.ijcard.2019.12.048DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534956PMC
March 2020

Novel three-dimensional imaging approach for cryoballoon navigation and confirmation of pulmonary vein occlusion.

Pacing Clin Electrophysiol 2020 03 17;43(3):269-277. Epub 2020 Feb 17.

Department of Medicine, Stanford University, Stanford, California.

Background: Cryoballoon apposition is crucial for durable pulmonary vein isolation (PVI) in atrial fibrillation, yet the balloon is difficult to visualize by conventional mapping systems, and pulmonary venography may miss small or out-of-plane leaks. We report a novel imaging system that offers real-time 3D navigation of the cryoballoon within atrial anatomy that may circumvent these issues.

Methods And Results: A novel overlay guidance system (OGS) registers already-acquired segmented atrial cardiac tomography (CT) with fluoroscopy, enabling real-time visualization of the cryoballoon within tomographic left atrial imaging during PVI. Phantom experiments in a patient-specific 3D printed left atrium showed feasibility for confirming PV apposition and leaks. We applied OGS prospectively to 68 PVs during PVI in 17 patients. The cryoballoon was successfully reconstructed in all cases, and its apposition was compared to concurrent PV venography. The OGS uncovered leaks undetected by venography in nine veins (eight cases), which enabled repositioning, confirming apposition in remaining 68 veins. Concordance of OGS to venography was 83.8% (χ , P < .01) CONCLUSIONS: We report a new system for real-time imaging of cryoballoon catheters to ensure PV apposition within the tomography of the left atrium. While providing high concordance with other imaging modalities for confirming balloon apposition or leak, the system also identified leaks missed by venography. Future studies should determine if this tool can provide a new reference for cryoballoon positioning.
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http://dx.doi.org/10.1111/pace.13858DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8174025PMC
March 2020

Response by Bhatia et al to Letter Regarding Article, "Wavefront Field Mapping Reveals a Physiologic Network Between Drivers Where Ablation Terminates Atrial Fibrillation".

Circ Arrhythm Electrophysiol 2019 11 15;12(11):e008022. Epub 2019 Nov 15.

Cardiovascular Institute and Department of Medicine/Division of Cardiology, Stanford University, CA (N.K.B., S.M.N.).

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http://dx.doi.org/10.1161/CIRCEP.119.008022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365521PMC
November 2019

Mapping and Ablation of Rotational and Focal Drivers in Atrial Fibrillation.

Card Electrophysiol Clin 2019 12;11(4):583-595

Department of Medicine/Cardiovascular Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, Suite CJ250F, MC 5773, Stanford, CA 94305, USA. Electronic address:

Drivers are increasingly studied ablation targets for atrial fibrillation (AF). However, results from ablation remain controversial. First, outcomes vary between centers and patients. Second, it is unclear how best to perform driver ablation. Third, there is a lack of practical guidance on how to identify critical from secondary sites using different AF mapping methods. This article addresses each of these issues.
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http://dx.doi.org/10.1016/j.ccep.2019.08.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954051PMC
December 2019

Moving the needle: Tissue characterization and lesion formation during infusion-needle ablation.

Heart Rhythm 2020 03 12;17(3):406-407. Epub 2019 Oct 12.

Cardiovascular Institute and Department of Medicine, Stanford University, Stanford, California. Electronic address:

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http://dx.doi.org/10.1016/j.hrthm.2019.10.018DOI Listing
March 2020

Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Circ Cardiovasc Qual Outcomes 2019 10 15;12(10):e005595. Epub 2019 Oct 15.

Veterans Affairs Palo Alto Health Care System, Palo Alto, CA (M.A., S.K.S., J.F., M.P.T.).

Background: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitoring data from cardiac implantable electronic devices, we sought to evaluate if machine learned signatures of AF burden could provide prognostic information on near-term risk of stroke when compared to conventional risk scores.

Methods And Results: We retrospectively identified Veterans Health Administration serviced patients with cardiac implantable electronic device remote monitoring data and at least one day of device-registered AF. The first 30 days of remote monitoring in nonstroke controls were compared against the past 30 days of remote monitoring before stroke in cases. We trained 3 types of models on our data: (1) convolutional neural networks, (2) random forest, and (3) L1 regularized logistic regression (LASSO). We calculated the CHADS-VASc score for each patient and compared its performance against machine learned indices based on AF burden in separate test cohorts. Finally, we investigated the effect of combining our AF burden models with CHADS-VASc. We identified 3114 nonstroke controls and 71 stroke cases, with no significant differences in baseline characteristics. Random forest performed the best in the test data set (area under the curve [AUC]=0.662) and convolutional neural network in the validation dataset (AUC=0.702), whereas CHADS-VASc had an AUC of 0.5 or less in both data sets. Combining CHADS-VASc with random forest and convolutional neural network yielded a validation AUC of 0.696 and test AUC of 0.634, yielding the highest average AUC on nontraining data.

Conclusions: This proof-of-concept study found that machine learning and ensemble methods that incorporate daily AF burden signature provided incremental prognostic value for risk stratification beyond CHADS-VASc for near-term risk of stroke.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.118.005595DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284982PMC
October 2019

Integrating blockchain technology with artificial intelligence for cardiovascular medicine.

Nat Rev Cardiol 2020 01;17(1):1-3

Cardiovascular Institute and Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.

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http://dx.doi.org/10.1038/s41569-019-0294-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186804PMC
January 2020
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