U.S. patent application number 14/094334 was filed with the patent office on 2015-06-04 for personalized computational modeling of atrial fibrosis to guide catheter ablation of atrial fibrillation.
This patent application is currently assigned to The Johns Hopkins University. The applicant listed for this patent is The Johns Hopkins University. Invention is credited to Kathleen Mcdowell, Natalia A. Trayanova.
Application Number | 20150150643 14/094334 |
Document ID | / |
Family ID | 53264100 |
Filed Date | 2015-06-04 |
United States Patent
Application |
20150150643 |
Kind Code |
A1 |
Trayanova; Natalia A. ; et
al. |
June 4, 2015 |
PERSONALIZED COMPUTATIONAL MODELING OF ATRIAL FIBROSIS TO GUIDE
CATHETER ABLATION OF ATRIAL FIBRILLATION
Abstract
Methods, system, and media for identifying one or more ablation
locations in an atrial tissue region in an atrial fibrillation (AF)
patient with atrial fibrosis are disclosed. Three-dimensional
imaging data representing the atria of the patient may be received.
A patient-specific model of the atria may be generated from the
three-dimensional imaging data. Simulation of the AF on the
patient-specific model may be conducted to identify AF-perpetrating
regions. One or more ablation locations in the atria may be
identified from the AF-perpetrating regions.
Inventors: |
Trayanova; Natalia A.;
(Baltimore, MD) ; Mcdowell; Kathleen; (Baltimore,
MD) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Johns Hopkins University |
Baltimore |
MD |
US |
|
|
Assignee: |
The Johns Hopkins
University
Baltimore
MD
|
Family ID: |
53264100 |
Appl. No.: |
14/094334 |
Filed: |
December 2, 2013 |
Current U.S.
Class: |
606/34 |
Current CPC
Class: |
A61B 2034/104 20160201;
A61B 2034/101 20160201; A61B 34/10 20160201; A61B 2018/00351
20130101; A61B 2034/105 20160201 |
International
Class: |
A61B 19/00 20060101
A61B019/00; A61B 18/12 20060101 A61B018/12 |
Goverment Interests
[0001] This invention was made with Government support of Grant
Nos. HL103428, HL105216, and P41 GM103545-14, awarded by NIH, and
Grant No. NSF-OCI-108849, awarded by the National Science
Foundation. The U.S. Government has certain rights in this
invention.
Claims
1. A method for identifying one or more ablation locations in an
atrial tissue region in an atrial fibrillation (AF) patient with
atrial fibrosis, the method comprising: receiving three-dimensional
imaging data representing the atria of the patient; generating a
patient-specific model of the atria from the three-dimensional
imaging data; conducting simulation of AF using the
patient-specific atrial model to identify AF-perpetrating regions;
and identifying from the AF-perpetrating regions one or more
ablation locations in the atria.
2. The method of claim 1, wherein the AF-perpetrating regions
comprise regions of tissue that contain the organizing centers of
persistent electrical rotors during simulation.
3. The method of claim 2, wherein persistent electrical rotors are
re-circulating electrical waves in cardiac tissue
4. The method of claim 1, wherein identifying from the
AF-perpetrating regions one or more ablation locations in the atria
further comprises: simulating ablation of lesions within and around
the AF-perpetrating regions to determine the size and shape of the
lesion necessary to render the atria non-inducible to AF.
5. The method of claim 1, wherein conducting simulation on the
patient-specific model to identify AF-perpetrating regions further
comprises: determining whether pacing stimuli will degrade into
reentrant activity.
6. The method of claim 1, wherein fibrotic lesions are modeled with
electrophysiological properties that are distinct from non-fibrotic
regions.
7. The method of claim 1, wherein induction of AF is simulated
using a dynamic pacing from different locations.
8. The method of claim 1, wherein conducting simulation on the
patient-specific atrial model to identify AF-perpetrating regions
further comprises: determining fibrotic lesions that lead to the
breakup of pacing-induced wavefronts and degeneration of the
wavefronts into reentrant waves that form AF rotors; and
determining spatial location of the resulting AF rotors.
9. The method of claim 1, wherein the patient-specific atrial model
includes modeling of fibrosis as at least one of ion channel
function remodeling, collagen deposition, gap junction remodeling,
fibroblast proliferation and phenotype switching into
myofibroblasts, or any combination thereof.
10. The method of claim 1, wherein said three-dimensional imaging
data is at least one of magnetic resonance imaging (MRI), computed
tomography (CT), positron emission tomography (PET), ultrasound, or
nuclear tracer three-dimensional imaging data.
11. The method of claim 1, further comprising: receiving additional
patient-specific data that includes at least one of biopsy data,
electrocardiogram data, recorded data from an implantable device,
or invasive electrical mapping data, wherein the simulation uses
the additional patient-specific data.
12. The method of claim 1, wherein conducting simulation on the
patient-specific atrial model to identify AF-perpetrating regions
further comprises: constructing a geometric model of the atria,
said geometric model including normal tissue regions and remodeled
tissue regions determined for said patient using the
three-dimensional imaging data.
13. The method of claim 12 further comprising: estimating atrial
fiber orientations in said geometric model.
14. The method of claim 12, wherein the geometric model of the
tissue region includes at least a geometric model of right and left
atria of the patient's heart, and wherein said remodeled tissue
regions are segmented into a plurality of different regions based
on said three-dimensional imaging data, said plurality of different
regions including imaging data consistent with fibrotic regions,
scar tissue regions, normal tissues regions, and transition zones
between normal and fibrotic or scar tissue regions.
15. The method of claim 1, wherein conducting simulation on the
patient-specific atrial model to identify AF-perpetrating regions
includes simulating electrophysiological activity of at least the
right and left atria of the patient's heart.
16. A non-transitory computer-readable medium comprising
computer-executable code for identifying one or more ablation
locations in an atrial tissue region in an atrial fibrillation (AF)
patient with atrial fibrosis, said computer-executable code
comprising instructions that, when executed by the computer, causes
the computer to: receive three-dimensional imaging data
representing the atria of the patient; generate a patient-specific
atrial model of AF from the three-dimensional imaging data; conduct
a simulation of AF using the patient-specific atrial model to
identify AF-perpetrating regions; and identify from the
AF-perpetrating regions one or more ablation locations in the
atria.
17. The non-transitory computer-readable medium of claim 16,
wherein the AF-perpetrating regions comprise regions of atrial
tissue that contain the organizing centers of electrical rotors
during simulation.
18. The non-transitory computer-readable medium of claim 17,
wherein persistent electrical rotors are re-circulating electrical
waves in cardiac tissue.
19. The non-transitory computer-readable medium of claim 16,
further comprising computer-executable code that, when executed by
the computer, causes the computer to: simulate ablation of lesions
within and around the AF-perpetrating regions to determine the size
and shape of the lesions necessary to render the atria
non-inducible to AF.
20. The non-transitory computer-readable medium of claim 16 further
comprising computer-executable code that, when executed by the
computer, causes the computer to: determine whether pacing stimuli
will degrade into reentrant activity.
21. The non-transitory computer-readable medium of claim 16,
wherein fibrotic lesions are modeled with electrophysiological
properties that are distinct from non-fibrotic regions.
22. The non-transitory computer-readable medium of claim 16,
wherein induction of AF is simulated using a dynamic pacing from
different locations.
23. The non-transitory computer-readable medium of claim 16 further
comprising computer-executable code that, when executed by the
computer, causes the computer to: determine fibrotic lesions that
lead to the breakup of pacing-induced wavefronts and degeneration
of the wavefronts into reentrant waves that form AF rotors; and
determine spatial location of the resulting AF rotors.
24. The non-transitory computer-readable medium of claim 16,
wherein the patient-specific atrial model includes modeling of
fibrosis as at least one of ion channel function remodeling,
collagen deposition, gap junction remodeling, fibroblast
proliferation and phenotype switching into myofibroblasts, or any
combination thereof.
25. The non-transitory computer-readable medium of claim 16,
wherein said three-dimensional imaging data is at least one of
magnetic resonance imaging (MRI), computed tomography (CT),
positron emission tomography (PET), ultrasound, or nuclear tracer
three-dimensional imaging data.
26. The non-transitory computer-readable medium of claim 16 further
comprising computer-executable code that, when executed by the
computer, causes the computer to: receive additional
patient-specific data that includes at least one of biopsy data,
electrocardiogram data, recorded data from an implantable device,
or invasive electrical mapping data, wherein the simulation uses
the additional patient-specific data.
27. The non-transitory computer-readable medium of claim 16 further
comprising computer-executable code that, when executed by the
computer, causes the computer to: construct a geometric model of
the atria, said geometric model including normal tissue regions and
remodeled tissue regions determined for said patient using the
three-dimensional imaging data.
28. The non-transitory computer-readable medium of claim 27 further
comprising computer-executable code that, when executed by the
computer, causes the computer to: estimate atrial fiber
orientations in said geometric model.
29. The non-transitory computer-readable medium of claim 27,
wherein the geometric model of the tissue region includes at least
a geometric model of right and left atria of the patient's heart,
and wherein said remodeled tissue regions are segmented into a
plurality of different regions based on said three-dimensional
imaging data, said plurality of different regions including imaging
data consistent with fibrotic regions, scar tissue regions, normal
tissues regions, and transition zones between normal and fibrotic
or scar tissue regions.
30. The non-transitory computer-readable medium of claim 16,
wherein conducting simulation on the patient-specific atrial model
to identify AF-perpetrating regions includes simulating
electrophysiological activity of at least the right and left atria
of the patient's heart.
31. A system for identifying one or more ablation locations in an
atrial tissue region in an atrial fibrillation (AF) patient with
atrial fibrosis, the system comprising a processor configured to:
receive three-dimensional imaging data representing the atria of
the patient; generate a patient-specific model of the atria from
the three-dimensional imaging data; conduct simulation of AF using
the patient-specific atrial model to identify AF-perpetrating
regions; and identify from the AF-perpetrating regions one or more
ablation locations in the atria.
32. The system of claim 31, wherein the AF-perpetrating regions
comprise regions of tissue that contain the organizing centers of
persistent electrical rotors during simulation.
33. The system of claim 32, wherein persistent electrical rotors
are re-circulating electrical waves in cardiac tissue.
34. The system of claim 31, wherein the processor is further
configured to: simulate ablation of lesions within and around the
AF-perpetrating regions to determine the size and shape of the
lesion necessary to render the atria non-inducible to AF.
35. The system of claim 31, wherein the processor is further
configured to: determine whether pacing stimuli will degrade into
reentrant activity.
36. The system of claim 31, wherein fibrotic lesions are modeled
with electrophysiological properties that are distinct from
non-fibrotic regions.
37. The system of claim 31, wherein induction of AF is simulated
using a dynamic pacing from different locations.
38. The system of claim 31 wherein the processor is further
configured to: determine fibrotic lesions that lead to the breakup
of pacing-induced wavefronts and degeneration of the wavefronts
into reentrant waves that form AF rotors; and determine spatial
location of the resulting AF rotors.
39. The system of claim 31, wherein the patient-specific atrial
model includes modeling of fibrosis as at least one of ion channel
function remodeling, collagen deposition, gap junction remodeling,
fibroblast proliferation and phenotype switching into
myofibroblasts, or any combination thereof.
40. The system of claim 31, wherein said three-dimensional imaging
data is at least one of magnetic resonance imaging (MRI), computed
tomography (CT), positron emission tomography (PET), ultrasound, or
nuclear tracer three-dimensional imaging data.
41. The system of claim 31, wherein the processor is further
configured to: receive additional patient-specific data that
includes at least one of biopsy data, electrocardiogram data,
recorded data from an implantable device, or invasive electrical
mapping data, wherein the simulation uses the additional
patient-specific data.
42. The system of claim 31, wherein the processor is further
configured to: construct a geometric model of the atria, said
geometric model including normal tissue regions and remodeled
tissue regions determined for said patient using the
three-dimensional imaging data.
43. The system of claim 42, wherein the processor is further
configured to: estimate atrial fiber orientations in said geometric
model.
44. The system of claim 42, wherein the geometric model of the
tissue region includes at least a geometric model of right and left
atria of the patient's heart, and wherein said remodeled tissue
regions are segmented into a plurality of different regions based
on said three-dimensional imaging data, said plurality of different
regions including imaging data consistent with fibrotic regions,
scar tissue regions, normal tissues regions, and transition zones
between normal and fibrotic or scar tissue regions.
45. The system of claim 31, wherein conducting simulation on the
patient-specific atrial model to identify AF-perpetrating regions
includes simulating electrophysiological activity of at least the
right and left atria of the patient's heart.
Description
BACKGROUND
[0002] 1. Field of Invention
[0003] The field of the currently claimed embodiments of this
invention relates to systems and methods for identifying one or
more ablation locations in a cardiac tissue region in an atrial
fibrillation (AF) patient.
[0004] 2. Discussion of Related Art
[0005] Atrial fibrillation (AF) is the most common arrhythmia in
humans. AF affects over 2 million people in the United States
alone, and data suggests that its prevalence will continue to
increase as the population ages.sup.1,2. Ectopic beats originating
from the pulmonary veins (PVs) in the left atrium (LA) have been
identified as a trigger that initiates AF.sup.3, Catheter-based
ablation, the delivery of heat to destroy the ability of cardiac
tissue to generate and conduct electrical signals locally, has
emerged, over the last decade, as a promising treatment option; the
procedure has successfully targeted the AF trigger via PV
electrical isolation.sup.4. However, only a 70% success rate in
achieving freedom from AF is reported with this approach.sup.5.
[0006] In an attempt to increase the success rate of the therapy,
recent ablation strategies have begun to target atrial tissue in
the LA wall as the substrate that perpetuates AF. These strategies
include substrate ablation guided by the spatial distribution of
complex fractionated atrial electrograms.sup.6 and/or dominant
frequencies.sup.7. A promising ablation strategy has recently
emerged.sup.8, which involves identifying and ablating AF localized
rotors in the substrate The variable success rates of these
strategies highlight the fact that identifying which components of
the substrate sustain AF is critical for the correct identification
of the targets for ablation.
[0007] Clinical evidence has demonstrated that the extent of atrial
fibrosis is correlated with both AF incidence.sup.9 and recurrence
after ablation.sup.10, highlighting its critical role in AF
pathogenesis. Recent studies have also shown that the spatial
distribution of fibrosis impacts AF dynamics.sup.11,12, suggesting
that the unique distribution of atrial fibrosis in each patient may
govern the location of AF rotors, and could therefore potentially
be used to identify critical targets for AF ablation, using a
patient-specific approach.
[0008] Therefore, there remains a need for improved systems and
methods for identifying one or more ablation locations in a tissue
region in an atrial fibrillation (AF) patient.
SUMMARY
[0009] A method for identifying one or more ablation locations in
an atrial tissue region in an atrial fibrillation (AF) patient with
atrial fibrosis according to an embodiment of the current invention
includes receiving three-dimensional imaging data representing the
atria of the patient; generating a patient-specific model of AF
from the three-dimensional imaging data; conducting simulation on
the patient-specific atrial model to identify AF-perpetrating
regions; and identifying from the AF-perpetrating regions one or
more ablation locations in the atria.
[0010] A non-transitory computer-readable medium comprising
computer-executable code for identifying one or more ablation
locations in an atrial tissue region in an atrial fibrillation (AF)
patient with atrial fibrosis according to an embodiment of the
current invention includes computer-executable code comprising
instructions that, when executed by the computer, causes the
computer to: receive three-dimensional imaging data representing
the atria of the patient; generate a patient-specific model of AF
from the three-dimensional imaging data; conduct a simulation on
the patient-specific atrial model to identify AF-perpetrating
regions; and identify from the AF-perpetrating regions one or more
ablation locations in the atria.
[0011] A system for identifying one or more ablation locations in
an atrial tissue region in an atrial fibrillation (AF) patient with
atrial fibrosis may include a processor configured to: receive
three-dimensional imaging data representing the atria of the
patient; generate a patient-specific model of AF from the
three-dimensional imaging data; conduct a simulation on the
patient-specific atrial model to identify AF-perpetrating regions;
and identify from the AF-perpetrating regions one or more ablation
locations in the atria. The system may be coupled to a memory that
one or more instructions for configuring the processor or causing
the processor to configured in any manner described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] Further objectives and advantages will become apparent from
a consideration of the description, drawings, and examples.
[0013] FIGS. 1A-1D depict patient-specific distributions of LA
fibrosis for substrates Utah I-IV. Pacing locations in the left PVs
(L1-LS) and right PVs (R1-RS) are indicated for each substrate of
FIGS. 1A-1D. Anatomical locations for the left superior PV (LSPV),
left inferior PV (LIPV), right superior PV (RSPV), right inferior
PV (RIPV), and LA appendage (LAA) are indicated in the substrate of
FIG. 1A; all substrates in FIGS. 1A-1D are presented in identical
orientations.
[0014] FIG. 2 depicts transmembrane potential maps at five time
instants, documenting AF initiation in substrates Utah III (top two
rows) and Utah IV (bottom three rows) from different PV pacing
locations (as noted at left). Lines of block are marked in red.
White arrows indicate direction of propagation. Darker areas
(left-most column) indicate pacing location.
[0015] FIG. 3 depicts a plot of the distance between each pacing
site and its closest fibrotic lesion vs. the pacing cycle length at
which a stimulus either failed to excite the tissue or resulted in
AF in substrates Utah III (red squares) and Utah IV (blue
triangles). Stimulus locations are labeled for each data point.
Groups 1-3 are indicated for discussion purposes.
[0016] FIG. 4 depicts transmembrane potential maps at two time
instants in substrates Utah III (top row) and Utah IV (bottom row)
demonstrating the meandering of the mother rotor organizing centers
termed phase singularities (shaded areas). Regions of meander are
outlined with dashed lines.
[0017] FIG. 5 depicts transmembrane potential maps at five time
instants in substrates Utah III (top two rows) and Utah IV (bottom
three rows) from different pacing locations (as noted at left) with
ablation lesions modeled. Red circles (as indicated in the figure
and in left-most column) indicate the extent of ablation lesions.
White arrows indicate direction of propagation. Time instances and
pacing locations are identical to those presented in FIG. 2.
DETAILED DESCRIPTION
[0018] Some embodiments of the current invention are discussed in
detail below. In describing embodiments, specific terminology is
employed for the sake of clarity. However, the invention is not
intended to be limited to the specific terminology so selected. A
person skilled in the relevant art will recognize that other
equivalent components can be employed and other methods developed
without departing from the broad concepts of the current invention.
All references cited anywhere in this specification, including the
Background and Detailed Description sections, are incorporated by
reference herein in their entireties as if each had been
individually incorporated.
[0019] The following abbreviations and acronyms may be used
throughout this disclosure: AF: atrial fibrillation; PV: pulmonary
vein; LA: left atrium; LGE-MRI: late gadolinium-enhanced magnetic
resonance imaging; V.sub.REST: resting membrane potential; APD:
action potential duration; I.sub.K(ACh): acetylcholine-activated
potassium current; Cx: Connexin; LSPV: left superior pulmonary
vein; LIPV: left inferior pulmonary vein; RSPV: right superior
pulmonary vein; RIPV: right inferior pulmonary vein; and LAA: left
atrial appendage.
[0020] According to some embodiments of the current invention, we
determine how the spatial distribution of atrial fibrosis governs
AF initiation and maintenance. Specifically, we aimed to
demonstrate that virtual electrophysiological study that combines
i) the anatomical structure and morphology of the patient atria and
the unique distribution of atrial fibrosis as quantified from
clinical MRI scans in vivo, and ii) computer modeling of
electrophysiology of the atria could be used to predict: (1) how
the unique patient-specific atrial fibrosis distribution determines
the locations from which ectopic stimuli will degrade into
reentrant activity in the fibrotic substrate; (2) the dynamic
behavior of persistent AF rotors in the individual atrial
substrate; and (3) the optimal locations of ablation of the
fibrotic substrate in each patient. To achieve the study goal, we
constructed four patient-specific atrial models with distributed
fibrosis generated from high-resolution late gadolinium-enhanced
magnetic resonance images (LGE-MRI) acquired in vivo from patients
suffering from atrial arrhythmias. Fibrotic lesions were modeled
with electrophysiological properties that were distinct from
non-fibrotic regions; simulations were conducted with dynamic
pacing from different, predominantly PV, locations and analysis was
performed to determine how fibrotic lesions led to the breakup of
pacing-induced wavefronts and their degeneration into reentrant
waves, and to the spatial localization of the resulting AF rotors.
Finally, simulations were performed to demonstrate that AF could be
rendered non-inducible by ablating the fibrotic substrate at
locations determined on the basis of knowledge regarding the
spatial localization of AF rotors. The simulations may be conducted
on the personalized model in order to determine the regions of
tissue which maintain the persistent electrical rotors, and
ablation lesions may be simulated within these regions to determine
the size and shape of the ablation lesion necessary to render the
substrate non-inducible to AF. The simulations and analysis may be
performed after the LGE-MRI images are captured for the screening
process but before an individual undergoes ablation.
[0021] The identification of ablation lesions via personalized
simulations has the potential to improve outcomes of catheter
ablation, shorten the duration of each procedure, and limit the
number of repeat procedures.
[0022] Methods
[0023] A group of patients with atrial arrhythmias presenting to
the University of Utah for catheter ablation underwent late LGE-MRI
acquisition at a resolution of 1250.times.1250.times.1500
.mu.m.sup.3, following the methodology of Akoum et al.sup.13. The
extent of pre-ablation LA fibrosis was quantified.sup.13, and each
patient was stratified into one of four groups: Utah I (<5% LA
wall enhancement), Utah II (>5% to <20), Utah III (>20% to
<35), or Utah IV (>35). One set of patient data from each of
these groups was selected for use in this computational study; the
simulations were blinded to the patient history. The amount of LA
fibrosis was quantified as 0.8%, 18.0%, 22.8%, and 42.0% for the
selected 4 patients from categories Utah I-IV, respectively.
[0024] Patient-Specific Modeling
[0025] We have recently developed a pipeline for the generation of
a 3D patient-specific geometry of the'atria with accurate
distribution of fibrosis.sup.14. This pipeline was used here for
the creation of the four biophysically detailed fibrotic
patient-specific LA models, each from a different Utah category. In
this study, we modeled only the LA due to the fact that the
clinical correlations drawn. between fibrosis and AF recurrence
following ablation were based on quantification of fibrosis in the
LA only.sup.10.
[0026] FIGS. 1A-1D depict patient-specific distributions of LA
fibrosis for substrates Utah I-IV. Briefly, LGE-MRI image
segmentation and interpolation was used to produce a
high-resolution image of the LA wall with accurate fibrotic lesion
distribution for each of the four patients (FIGS. 1A-1D). A finite
element tetrahedral mesh was generated from each of the segmented
image stacks of the LA,.sup.15 and fiber orientation was estimated
as previously described.sup.14,16. Non-fibrotic regions of the
tissue were represented with a modified version of the
Courtemanche-Ramirez-Nattel model of the human atrial action
potential under AF conditions.sup.17, as described by Krummen et
al.sup.18; the model also included the formulation of the
acetylcholine-activated potassium current, I.sub.K(Ach), from
Kneller et al..sup.19 Conductivities were chosen such that
conduction velocity fell within the range recorded in the human
atrium.sup.20, as described in ref.sup.12. Although this is one way
to represent the electrophysiology of the atria, there are many
other ways of representing the electrophysiology of the atria, and
any of them may also be used with the systems, media, and methods
discussed herein.
[0027] In our previous study.sup.12 we examined several different
representations of fibrotic remodeling in a patient-specific model
with extensive fibrosis, generated from a patient suffering
persistent AF, to investigate how each mechanistically contributed
to AF propensity. In that study we determined that sustained AF
resulting from PV pacing matched the AF clinical manifestation when
the patient's extensive fibrotic lesions were modeled with a
three-component representation of fibrosis, based on experimental
evidence. The three aspects of remodeling were: (1) diffuse
collagen deposition, (2) gap junction remodeling due to connexin 43
(Cx43) protein down-regulation and lateralization, and (3)
fibroblast proliferation and phenotype switching into
myofibroblasts. Based on our previous findings, we incorporated
these three aspects of remodeling into the fibrotic lesions of each
of the four patient-specific models in order to determine how the
distribution of fibrosis governs the potential degeneration of
pacing-induced wavefronts into reentrant circuits as well as the
dynamic behavior of the resulting AF rotors.
[0028] Diffuse collagen deposition was represented by employing a
method of 3D element decoupling to introduce fine conduction
barriers along fiber orientation in the fibrotic lesions, as
previously described.sup.12,14. Gap junction downregulation and
lateralization was accounted for, as previously described.sup.12,
by altering conductivities to represent the .about.30% reduction in
Cx43 expression in atria in AF compared to sinus rhythm.sup.21, as
well as the 3.9-fold higher lateral Cx43 labeling in atrial
myocytes in AF compared to sinus rhythm.sup.22. Lastly,
myofibroblasts were randomly assigned to 1% of the fibrotic lesions
and formed electrical connections with adjacent myocytes, as
observed experimentally.sup.23,24; myofibroblast membrane kinetics
were modeled following our previously published
methodology.sup.14,25. Our previous study demonstrated that
myofibroblast influences can be equivalently modeled via electrical
coupling or paracrine effects.sup.12. Although this is one way to
represent fibrosis, there are many other ways of representing the
fibrosis, and any of them may also be used with the systems, media,
and methods discussed herein.
[0029] Simulation Protocol and Data Analysis
[0030] Mathematical description of current flow was based on the
monodomain representation of the myocardium and simulations were
executed using the simulation package CARP (CardioSolv LLC).sup.26.
To investigate how the distribution of fibrosis determines whether
dynamic pacing will degrade into reentrant activity and initiate
AF, ten pacing locations were chosen in each substrate to represent
ectopic triggers. Stimulus locations were distributed around the
PVs (where most ectopic beats originate.sup.3) such that five
stimuli were located within the left PVs (stimuli L1-L5 in each LA
model, FIGS. 1A-1D) and five were located within the right PVs
(stimuli R1-R5 in each LA model, FIGS. 1A-1D). Specifically, two
pacing sites were located on opposing sides of each PV, one site
was located between the left PVs, and one site was located between
the right PVs. For each pacing location, a dynamic pacing protocol
was used to assess arrhythmia inducibility, as performed
clinically.sup.8. The protocol consisted of pacing for 5 beats at a
365 ms cycle length, followed by two beats at cycle lengths of each
of the following: 290 ms, 285 ms, 280 ms, 275 ms, 270, 268 ms, 266
ms, 264 ms, 262 ms, and 260 ms. Sustained AF was defined as
fibrillatory activity lasting for 10 seconds after the delivery of
the last stimulus. Additional stimuli were placed as necessitated
by initial simulation results.
[0031] In order to investigate how patient-specific, distributions
of atrial fibrosis govern AF initiation and maintenance, the
following analysis was performed: [0032] To determine if
simulations using the patient-specific models could accurately
represent the ability of an LA substrate to support AF, the
simulations were performed blinded to patient history. Substrates
in which stimuli from one or more locations initiated AF were
considered susceptible to AF. Predictions were considered accurate
if the simulation outcomes (susceptible vs. not susceptible to AF)
accurately matched the patient's clinical history. [0033] To assess
the influence of fibrotic lesion distribution on AF inducibility
from different PV pacing locations, the distance between each
stimulus site and its closest fibrotic lesion was calculated in
substrates in which AF was observed.
[0034] Identifying Rotors and Representing Ablation
[0035] In models in which AF resulted from PV pacing, phase
singularities (rotor organizing centers) were calculated over a
period of 10 seconds, and the phase singularity meander quantified
for each rotor. Ablation lesions were implemented in the models to
target the regions of tissue which maintained the persistent phase
singularities. Lesions were modeled as one or more transmural
regions.sup.31 of inexcitable tissue; they were circular to account
for catheter tip shape, and 7 mm in diameter (within the range of
clinical ablation lesions.sup.8). Arrhythmia inducibility was
tested after the implementation of each ablation lesion until AF
could no longer be initiated following dynamic pacing.
[0036] Results
[0037] Arrhythmia Inducibility in Patient-Specific Models
[0038] Substrate propensity to AF was initially tested from the ten
PV locations in each of the four patient-specific models of the
fibrotic LA. Neither the Utah I nor the Utah II substrates gave
rise to arrhythmia following pacing from any of the ten locations.
The Utah III substrate gave rise to sustained AF following pacing
from two of the ten pacing locations; those were pacing sites L2
and L4 (as marked in FIG. 1C). Pacing from three of the ten PV
locations (L1, L4, and R1, as seen in FIG. 1D) resulted in
sustained AF in the Utah IV substrate.
[0039] FIG. 2 presents the events leading to the formation of the
first cycle of reentry resulting from dynamic pacing at locations
that initiated AF in substrates Utah III and Utah IV. In each case,
unidirectional conduction block (as denoted by the red lines in
FIG. 2) took place. It resulted from either the stimulus-induced
wavefront encountering tissue in a refractory state (Utah IV: R1)
or the collision of the stimulus-induced wavefront with the
wavefront that was the result of the preceding beat from the pacing
protocol (Utah III: L2 and L4; Utah IV: L1 and L4). The latter case
was possible because of the slow and discontinuous conduction that
occurred in the large fibrotic lesions in substrates Utah III and
Utah IV, resulting in the wave taking a propagation direction
different from that in nonfibrotic tissue. Following unidirectional
conduction block in substrates Utah III and Utah IV, a reentrant
circuit was formed, marking AF onset.
[0040] After the computational study was complete, patient history
was provided to our team. In all substrates, simulation outcomes
faithfully represented the patient clinical manifestation. The
patients from which the Utah I and II LA models were generated did
not suffer from AF, while the patients whose scans were used to
create the Utah III and IV LA models both suffered from AF.
[0041] Distribution of Atrial Fibrosis Determines Whether Paced
Beats Degrade into AF
[0042] The results from our simulations revealed that all stimuli
of the dynamic pacing protocol were delivered in all cases in which
AF was initiated (i.e., in these cases the dynamic pacing protocol,
which consisted of pacing starting at cycle lengths of 365 ms and
proceeding down to 260 ms, resulted in reentry formation). In most
cases in which AF was not initiated by pacing from a given PV site
in substrates Utah III and IV, a stimulus from the pacing train
failed to excite tissue and pacing-induced propagation thus failed
before the pacing protocol was completed (that is, prior to
reaching cycle lengths of 260 ms). The observation that AF ensued
only after propagation was elicited following the shortest pacing
cycle lengths in the dynamic pacing protocol is consistent with
experimental findings that show short effective refractory periods
are significantly correlated with an increased probability of
sustained AF.sup.27.
[0043] We have previously demonstrated that the
electrophysiological effects of fibrosis in the atria extends
beyond the borders of fibrotic lesions themselves, well into
non-fibrotic tissue.sup.12. Indeed, APD changes occur in
non-fibrotic tissue due to influences from nearby fibrosis; the
extent of APD changes attenuates with the distance from the
fibrotic lesion.sup.25. Therefore, given that repolarization
dynamics throughout the LA are altered non-uniformly by fibrosis,
in a distance-to-fibrotic-region-dependent manner, we sought to
determine how a PV pacing location's distance to the region of
fibrosis affects the ability of a stimulus from that location to
elicit excitation in the LA in the Utah III and IV models. To
perform this analysis, the distance between each PV pacing site and
its closest fibrotic lesion was plotted against the pacing cycle
length of the stimulus from that location's pacing train that
failed to elicit excitation in substrates Utah III and IV.
[0044] Results are presented in FIG. 3. In cases when AF was
initiated, the cycle length is 260 ms, the shortest cycle length in
the pacing protocol. The graph demonstrates a clustering of three
groups of data points: Group 1, PV pacing locations for which there
was a fibrotic lesion in close proximity (<120 .mu.m) and for
which stimuli with cycle lengths of 285-290 ms in the pacing
protocol failed to excite the tissue; Group 2, PV pacing locations
that were between 378 and 1052 .mu.m from fibrotic lesions and for
which sustained AF resulted following the execution of the entire
dynamic pacing protocol; and Group 3, stimulus locations that were
far from fibrotic lesions (>1330 .mu.m) and for which stimuli
with cycle lengths of 260-280 ms failed to excite the tissue.
[0045] Based on the finding that stimuli that are located at a
distance between 378 and 1052 .mu.m from fibrotic lesions can
initiate AF in models Utah III and IV, the distance between each
point in the non-fibrotic tissue and the closest fibrotic lesion
was calculated to determine what locations would fall within the
range of "sweet spot" distances to fibrotic lesions and could
therefore be considered "prime ectopic spots" for AF initiation. We
found that "prime" locations comprise 3.56% and 4.43% of LA volume
(including the PVs) in the Utah III and IV models, respectively. A
disproportionately large amount of this "prime" tissue was
localized in the PVs, 19.78% and 32.56% in the Utah III and IV
substrates, respectively, although the PVs comprised only 14.9% and
22.93% of total tissue volume, respectively, in these models.
Building on this finding, pacing trains were delivered from two
locations in "prime" tissue outside of the PV regions in substrates
Utah III and IV to test AF inducibility; consistent with our
predictions above, sustained AF was initiated by pacing from these
locations following the dynamic protocol (data not shown).
[0046] Distribution of Atrial Fibrosis Determines AF Rotor
Location
[0047] Epicardial phase singularities were calculated in all of the
cases in which pacing resulted in AF initiation. Phase
singularities, as opposed to filaments, were examined due to the
fact that the walls of the LA are thin (<3 mm) as well as for
ease of analyzing the meandering of the rotor organizing centers
over time in our models. In the Utah III and IV models AF was
characterized with the formation of one or more mother rotors,
which persisted for the duration of the AF simulations, as well as
other "breakoff" fibrillatory activity, the phase singularities of
which appeared and vanished quickly. Once AF was initiated, the
phase singularities associated with the persistent reentries
meandered within the same tissue regions in each substrate,
regardless of the pacing location from which AF was induced,
indicating that the patient-specific distribution of fibrosis,
rather than the location of atrial trigger, was the most important
factor governing AF mother rotor location(s). In the Utah III
substrate, one persistent (mother rotor) phase singularity was
found to meander within an approximately oval region of tissue with
long and short diameters of 13.2 mm and 6.7 mm, respectively (FIG.
4, top row; mother rotor phase singularities locations at the given
instant of time are shown by the pink dots; regions of meander are
outlined by red dashed line). In the Utah IV substrate, there were
two persistent phase singularities, one meandering within a larger
oval region of tissue with long and short diameters of 13.7 mm and
6.2 mm, respectively, and one meandering in a smaller approximately
circular region of tissue that was 6.6 mm in diameter (FIG. 4,
bottom row).
[0048] Ablation was applied, as described in Methods, to regions
within which the persistent (mother rotor) phase singularities
(which are the rotor organizing centers) meandered. Snapshots of
transmembrane potential distributions at time instants throughout
simulations with ablation lesions modeled are shown in FIG. 5; the
selected time instants are identical to those presented in FIG. 2
to allow for comparison. The implementation of ablation consisting
of two overlapping circular lesions (indicated in red in the
left-most images in FIG. 5), fully covering the long diameter (13.2
mm) of the region of mother rotor phase singularity meander was
necessary to result in AF non-inducibility in the Utah III
substrate following pacing train delivery from both the L2 and L4
locations. In the Utah IV substrate, two separate ablations were
implemented to render inexcitable the regions of meander of the two
persistent phase singularities. The smaller region of meander was
targeted with one circular ablation, while the larger region of
meander was targeted with two overlapping circular lesions to again
fully cover the long diameter of the region of persistent phase
singularity meander (13.7 mm). The ablations (indicated in red in
the left-most substrates in FIG. 5) resulted in AF non-inducibility
following pacing from all three original locations (L1, L4, and R4)
in the Utah IV substrate. It was not possible to achieve AF
non-inducibility with fewer or smaller ablation lesions.
DISCUSSION
[0049] This study demonstrates the feasibility of using a
patient-specific simulation approach to predict the role of the
unique atrial fibrotic distribution in the dynamics of the
patient's persistent AF. The study elucidated how patient-specific
fibrosis distribution determines whether ectopic stimuli result in
persistent AF, and from which specific atrial locations. We
evaluated the dynamic behavior of persistent AF rotors in the
patient atria; based on this analysis, and particularly, on the
predicted spatial localization of persistent AF rotors, we
performed feasibility simulations showing that AF could be rendered
non-inducible, in a patient-specific approach, by ablating regions
of persistent rotor meander in the fibrotic substrate. The present
study has, thus, two contributions, each discussed below: one in
mechanistic understanding of the role of the fibrotic substrate in
persistent AF, and another, in demonstrating that it is feasible to
identify the optimal targets of atrial ablation in the patient
using the approach presented here prior to the clinical procedure.
The main mechanistic findings in this study are:
[0050] (1) The mechanisms which determine whether pacing from a
given atrial site will degrade into AF, operate in a
distance-to-fibrosis-dependent fashion, with pacing from locations
only in the mid-range of distances (378-1052 .mu.m) to fibrotic
lesions resulting in sustained AF.
[0051] (2) A disproportionate amount of all non-fibrotic tissue
that falls in the mid-range (i.e arrhythmogenic "sweet spot")
distance to fibrotic lesions is located in the PVs.
[0052] (3) Patient-specific distribution of fibrosis, rather than
location of pacing (i.e. location of arrhythmia trigger), governs
AF "mother rotor" location(s) and meander; phase singularities
associated with persistent reentries meander within the same tissue
regions once AF is initiated, regardless of the pacing
location.
[0053] In this study, two out of ten PV stimulus locations resulted
in AF initiation in the Utah III substrate (with 22.8% fibrosis),
while three out of ten PV stimulus locations caused AF in the Utah
IV substrate (with 42.0% fibrosis); no PV stimuli caused AF in the
Utah I and II models. This finding demonstrates a correlation
between the amount of fibrosis and the probability of a trigger
initiating AF; it is consistent with the correlation found between
AF incidence and percent fibrosis in the LA.sup.9, the underlying
cause of which has remained unknown. Our study findings suggest a
new paradigm, that AF incidence may be higher in patients with more
fibrosis due to the fact that larger degrees of fibrosis cause
locations in the substrate to become "prime" for triggering AF.
Indeed, we demonstrated that "prime" trigger locations comprise
3.56% and 4.43% of the Utah III and IV substrates by volume,
respectively, of which disproportionate amounts were located in the
PVs. The concept that ablation may eliminate "prime" tissue may
help explain the perplexing clinical finding that triggered
activity, which can remain frequent years after linear LA ablation,
often does not initiate AF.sup.37. Should linear LA ablation
eliminate "prime" tissue itself or create an electrical barrier
that prevents fibrotic lesions from altering nearby tissue
electrophysiology, such that "prime" regions are not created,
substrates could be rendered noninducible to AF, despite the
presence of triggered activity.
[0054] Traditionally, AF treatments have either been aimed at
suppressing atrial triggers that initiate AF.sup.4, or at modifying
the substrate that sustains it.sup.6,7. In this study, however, the
spatial distribution of atrial fibrosis was found both to determine
whether pacing initiates AF and to govern the dynamics of the
resulting AF rotors. The fact that the distribution of atrial
fibrosis modulates both AF initiation and its maintenance creates a
new paradigm for AF treatment, in which one treatment strategy
could possibly target both AF-triggering and -perpetuating
mechanisms. Indeed, this study demonstrated that in all cases when
the regions of meander of persistent phase singularities were
ablated, reentrant circuits could not form following pacing from
locations that had previously resulted in sustained AF (FIG. 5).
This concept may explain why a novel ablation strategy, aimed at
ablating AF electrical rotors, has reported a higher degree of
successful patient outcomes as compared to PV electrical
isolation.sup.8. Consistent with our finding that the
implementation of 1 to 2 ablation lesions led to AF
non-inducibility, Narayan et al. reported that an average of
2.1+1.0 electrical rotors were observed in patients with sustained
AF, and that the ablation of these rotors led to successful
termination of AF.sup.8.
[0055] This study was conducted without knowledge of the history of
the patients the scans of whom were used to create the models; the
simulations accurately predicted the clinical outcome of each of
the four patients. Indeed, models which resulted in AF (Utah III
and IV) turned out to have been generated from patients suffering
AF, while models which did not result in AF (Utah I and II), turned
out to have been derived from AF-free patients. Our
proof-of-concept simulations thus indicate that patient-specific
modeling of atrial arrhythmogenesis under fibrotic conditions could
become in the future a powerful new non-invasive tool to stratify
atrial arrhythmia risk.
[0056] In this study we demonstrated that a virtual
electrophysiological study using patient-specific atrial models
could provide a novel way to identify regions of meander of
persistent (mother rotor) phase singularities based on the
individual spatial distribution of fibrosis. We showed that
ablating these regions of meander in the models results in AF
non-inducibility from any stimulus location. This study therefore
presents the proof-of-concept of a non-invasive approach to the
identification of the ablation targets for persistent AF in the
fibrotic atria. In its translation to the clinic, we envision that
the approach will entail the use, prior to the clinical procedure,
of an MRI-based subject-specific multiscale electrophysiological
model of the fibrotic atria to analyze AF dynamics and rotor
meander, and to determine the targets of ablation. Once the targets
of ablation are deter mined and visualized by the present approach,
we envision that ablation delivery for AF termination could be
swift and precise, eradicating, with a minimal number of lesions,
all rotors in the fibrotic substrate. This could dramatically
improve the efficacy of ablation, increase the tolerance for the
procedure, and reduce post-procedure complications and long-term
deleterious effects resulting from the lengthy invasive mapping and
the numerous unnecessary ablation lesions.
[0057] Study Limitations
[0058] Because fibrosis was identified in the LA only, for
computational tractability, simulations used only the LA to
determine AF rotor dynamics and perform ablation. The methodology
would not change when modeling both atria; similarly, the insights
obtained here will remain the same. With additional fibrosis in the
RA, persistent rotors might meander in the RA, necessitating
ablation lesions there as well. Another limitation of the study is
the small number of patient scans due to the low resolution of the
scans. However, image resolution is likely to improve with time and
advances in MRI technology, which will improve the utility of our
approach.
[0059] Funding Sources
[0060] The authors gratefully acknowledge support of this work by
the American Heart Association Predoctoral Fellowship to K. M and
the National Institutes of Health (grants HL103428 and HL105216) to
N. T. This work was also supported in part by the U.S. National
Science Foundation, grant NSF-OCI-108849. We also acknowledge
support for image processing and model generation software from the
Center for Integrative Biomedical Computing (CIBC) at the
University of Utah, sponsored by NIH Grant P41 GM103545-14.
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[0099] The above provides some examples according to particular
embodiments of the current invention. The broad concepts of the
current invention are not limited to only these particular
examples. More generally, a method for identifying one or more
ablation locations in a tissue region in an atrial fibrillation
(AF) patient with atrial fibrosis according to an embodiment of the
current invention includes receiving three-dimensional imaging data
of a patient's atria, simulating at least one of
electrophysiological or electromechanical activity of at least a
portion of the patient's heart using the three-dimensional imaging
data, and planning the patient-specific cardiac procedure based on
the simulating. The cardiac procedure is for providing a
preselected alteration of at least one of electrophysiological or
electromechanical behavior of the patient's heart.
[0100] The three-dimensional imaging data can be MRI data as
described in the examples above. However, the broad concepts of the
current invention are not limited to that particular example. The
three-dimensional imaging data can be can be at least one of
magnetic resonance imaging (MRI), computed tomography (CT),
positron emission tomography (PET), ultrasound, or nuclear tracer
three-dimensional imaging data, for example. The method for
identifying one or more ablation locations in an atrial tissue
region can further include receiving additional patient-specific
data in addition to the three-dimensional imaging data. For
example, some embodiments can include receiving at least one of
biopsy data, electrocardiogram data, recorded data from an
implantable device (pace maker, defibrillator, etc.), or invasive
electrical mapping data (e.g., endoscopic). The simulating can then
use the additional patient-specific data for the simulation.
[0101] The simulating may include simulating at least one or both
of electrophysiological or electromechanical activity of the atria
or a portion of them. Simulating at least one of
electrophysiological or electromechanical activity of the atria or
a portion of them can include constructing a geometric model of the
atria or a portion of them. The geometric model can include normal
tissue regions and remodeled atrial tissue regions that are
determined for the patient using the three-dimensional imaging
data. The term "remodeled tissue" can include fibrosis, infarct
scar, infarct border (gray) zone, or other disease-related
structural, electrophysiological or contractile changes in the
tissue. The simulating can further include estimating tissue fiber
orientations in the geometric model of the atria. The estimation of
fiber orientations can be done in a variety of ways. For example,
the fiber orientations can be calculated using a Laplace-Dirichlet
method to define the local axes at each spatial location in the
atria (J. D. Bayer, R. Blake, G. Plank, Trayanova N, Novel rule
based algorithm for assigning myocardial fiber orientation to
computation heart models. Ann Biomed Eng (2012), the entire
contents of which are incorporated herein by reference). Another
approach could utilize pre-compiled data (i.e., atlas data), which
can be mapped into the specific size and shape of the patient's
heart (Image-Based Estimation of Ventricular Fiber Orientations for
Personalized Modeling of Cardiac Electrophysiology, Vadakkumpadan
F, Arevalo H, Ceritoglu C, Miller M, Trayanova N.,_IEEE Trans Med
Imaging 2012 Jan. 18 (the entire contents of which are incorporated
herein by reference).
[0102] The geometric model of the tissue region may include at
least a geometric model of right and left atria of the patient's
heart. The remodeled tissue regions in this case can be segmented
into a plurality of different regions based on the
three-dimensional imaging data. The plurality of different regions
can include fibrotic regions, scar tissue regions, normal tissues
regions, and transition zones, for example, between normal and
fibrotic tissue regions. The simulating in this case can be
simulating electrophysiological activity of at least the right and
left atria of the patient's heart.
[0103] The geometric model of the tissue region may include a
geometric model of at least right and left atria of the patient's
heart. In this embodiment, the remodeled tissue regions are
fibrotic tissue regions. The simulating at least one of
electrophysiological or electromechanical activity is simulating
electrophysiological activity of at least the right and left atria
of the patient's heart.
[0104] A computing device may perform certain functions in response
to processor executing software instructions contained in a
computer-readable medium, such as a memory. In alternative
embodiments, hardwired circuitry may be used in place of or in
combination with software instructions to implement features
consistent with principles of the disclosure. Thus, implementations
consistent with principles of the disclosure are not limited to any
specific combination of hardware circuitry and software.
[0105] Exemplary embodiments may be embodied in many different ways
as a software component. For example, it may be a stand-alone
software package, a combination of software packages, or it may be
a software package incorporated as a "tool" in a larger software
product. It may be downloadable from a network, for example, a
website, as a stand-alone product or as an add-in package for
installation in an existing software application. It may also be
available as a client-server software application, or as a
web-enabled software application. It may also be embodied as a
software package installed on a hardware device.
[0106] Numerous specific details have been set forth to provide a
thorough understanding of the embodiments. It will be understood,
however, that the embodiments may be practiced without these
specific details. In other instances, well-known operations,
components and circuits have not been described in detail so as not
to obscure the embodiments. It can be appreciated that the specific
structural and functional details are representative and do not
necessarily limit the scope of the embodiments.
[0107] It is worthy to note that any reference to "one embodiment"
or "an embodiment" means that a particular feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment. The appearances of the phrase
"in one embodiment" in the specification are not necessarily all
referring to the same embodiment.
[0108] Although some embodiments may be illustrated and described
as comprising exemplary functional components or modules performing
various operations, it can be appreciated that such components or
modules may be implemented by one or more hardware components,
software components, and/or combination thereof. The functional
components and/or modules may be implemented, for example, by logic
(e.g., instructions, data, and/or code) to be executed by a logic
device (e.g., processor). Such logic may be stored internally or
externally to a logic device on one or more types of
computer-readable storage media.
[0109] Some embodiments may comprise an article of manufacture. An
article of manufacture may comprise a storage medium to store
logic. Examples of a storage medium may include one or more types
of computer-readable storage media capable of storing electronic
data, including volatile memory or non-volatile memory, removable
or non-removable memory, erasable or non-erasable memory, writeable
or re-writeable memory, and so forth. Examples of storage media
include hard drives, disk drives, solid state drives, and any other
tangible or non-transitory storage media.
[0110] It also is to be appreciated that the described embodiments
illustrate exemplary implementations, and that the functional
components and/or modules may be implemented in various other ways
which are consistent with the described embodiments. Furthermore,
the operations performed by such components or modules may be
combined and/or separated for a given implementation and may be
performed by a greater number or fewer number of components or
modules.
[0111] Some of the figures may include a flow diagram. Although
such figures may include a particular logic flow, it can be
appreciated that the logic flow merely provides an exemplary
implementation of the general functionality. Further, the logic
flow does not necessarily have to be executed in the order
presented unless otherwise indicated. In addition, the logic flow
may be implemented by a hardware element, a software element
executed by a processor, or any combination thereof.
[0112] The embodiments discussed in this specification are intended
to explain concepts of the invention. However, the invention is not
intended to be limited to the specific terminology selected and the
particular examples described. The above-described embodiments of
the invention may be modified or varied, without departing from the
invention, as appreciated by those skilled in the art in light of
the above teachings. It is therefore to be understood that, within
the scope of the claims and their equivalents, the invention may be
practiced otherwise than as specifically described.
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