Atrial fibrillation (AF) is a major arrhythmia worldwide, causing palpitations, stroke and mortality, and affecting 2-5 million Americans. Unfortunately, therapy to eliminate AF has had limited success. In our last funding cycle, we focused on localized drivers as potential AF mechanisms. Mapping of drivers has now been validated by concurrent optical mapping of human AF, and their features and have been validated by several other methods in patients. Nevertheless, ablation results for these and other proposed mechanisms for AF outside the pulmonary veins are mixed. It is unclear if this reflects difficulties of AF mapping, or different mechanisms between patients. The project will develop a novel mechanistic framework for AF that simplifies existing indices by building on scientific consensus that organized AF is easier to treat, and disorganized AF has worse prognosis. This concept spans many existing indices and may help to reconcile them. We have 3 specific aims: (1) To define if the impact of ablation depends on the extent of organizing surrounding the ablation site; (2) To establish candidate mechanisms for organized and disorganized AF zones in individual patients with specific profiles, using machine learning applied to known cases with and without ablation success in our large registry. This comprises detailed AF maps during ablation and after Maze surgery, clinical data and outcomes. (3) To use novel clinical tools to predict whether patients will respond to PVI, other ablation or Maze surgery based on whether targeted regions control larger atrial areas and their locations. This study will deliver immediate translational and clinical impact, and directly enable personalized medicine for AF ablation. We use detailed clinical mapping in patients, signal processing and computer modeling to develop a novel mechanistic framework and widely applicable clinical tools. We will use tools including machine learning and statistics to classify mechanisms based upon outcomes from ablation in individual patients. We will make our data and code available online. Our team is experienced in electrophysiology, computer science, machine learning, biological physics and statistics. The proposal is thus highly feasible.

Public Health Relevance

The Dynamics of Human Atrial Fibrillation Narrative Atrial fibrillation (AF) is an enormous public health problem in the United States, affecting 2-5 million Americans and causing rapid heart beats, stroke, heart failure or death. In this project, the applicant will develop a novel framework to better understand human AF that builds on agreement between several concepts for the disease. The applicant will develop strategies to identify AF patients who will best respond to each of several therapies, to guide personalized therapy.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
2R01HL083359-11A1
Application #
10071621
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Sopko, George
Project Start
2008-07-01
Project End
2024-06-30
Budget Start
2020-07-20
Budget End
2021-06-30
Support Year
11
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Baykaner, Tina; Rogers, Albert J; Zaman, Junaid A B et al. (2018) Editorial commentary: What can lung transplantation teach us about the mechanisms of atrial arrhythmias? Trends Cardiovasc Med 28:62-63
Navara, Rachita; Leef, George; Shenasa, Fatemah et al. (2018) Independent mapping methods reveal rotational activation near pulmonary veins where atrial fibrillation terminates before pulmonary vein isolation. J Cardiovasc Electrophysiol 29:687-695
Rogers, Albert J; Tamboli, Mallika; Narayan, Sanjiv M (2018) Integrating mapping methods for atrial fibrillation. Pacing Clin Electrophysiol 41:1286-1288
Zaman, Junaid A B; Sauer, William H; Alhusseini, Mahmood I et al. (2018) Identification and Characterization of Sites Where Persistent Atrial Fibrillation Is Terminated by Localized Ablation. Circ Arrhythm Electrophysiol 11:e005258
Vidmar, David; Alhusseini, Mahmood I; Narayan, Sanjiv M et al. (2018) Characterizing Electrogram Signal Fidelity and the Effects of Signal Contamination on Mapping Human Persistent Atrial Fibrillation. Front Physiol 9:1232
Sahli Costabal, Francisco; Zaman, Junaid A B; Kuhl, Ellen et al. (2018) Interpreting Activation Mapping of Atrial Fibrillation: A Hybrid Computational/Physiological Study. Ann Biomed Eng 46:257-269
Zaman, Junaid A B; Baykaner, Tina; Clopton, Paul et al. (2017) Recurrent Post-Ablation Paroxysmal Atrial Fibrillation Shares Substrates With Persistent Atrial Fibrillation : An 11-Center Study. JACC Clin Electrophysiol 3:393-402
Baykaner, Tina; Zografos, Theodoros A; Zaman, Junaid A B et al. (2017) Spatial relationship of organized rotational and focal sources in human atrial fibrillation to autonomic ganglionated plexi. Int J Cardiol 240:234-239
Zaman, Junaid A B; Kowalewski, Christopher A B; Narayan, Sanjiv M (2017) Mapping Ripples or Waves in Atrial Fibrillation? J Cardiovasc Electrophysiol 28:383-385
Baykaner, Tina; Zaman, Junaid A B; Rogers, Albert J et al. (2017) Spatial relationship of sites for atrial fibrillation drivers and atrial tachycardia in patients with both arrhythmias. Int J Cardiol 248:188-195

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