U.S. patent application number 16/195635 was filed with the patent office on 2019-03-28 for methods and systems for treating heart instability.
This patent application is currently assigned to THE REGENTS OF THE UNIVERSITY OF CALIFORNIA. The applicant listed for this patent is THE REGENTS OF THE UNIVERSITY OF CALIFORNIA. Invention is credited to Sanjiv M. Narayan.
Application Number | 20190090822 16/195635 |
Document ID | / |
Family ID | 41380735 |
Filed Date | 2019-03-28 |
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United States Patent
Application |
20190090822 |
Kind Code |
A1 |
Narayan; Sanjiv M. |
March 28, 2019 |
METHODS AND SYSTEMS FOR TREATING HEART INSTABILITY
Abstract
A system and method for assessing risk associated with a
suspected heart rhythm disorder includes at least one sensor for
generating a signal received from a beating human heart over a
plurality of time segments, and an analytic engine that receives
the signal and calculates a change in the signal among at least a
first time segment and a second time segment in response to a
change in at least one of rate and regularity induced in the
beating human heart. The analytic engine generates a risk score for
the heart rhythm disorder based at least in part on the change in
the signal. The system may further be configured to control
modification of tissue of the beating human heart based on the risk
score.
Inventors: |
Narayan; Sanjiv M.; (Palo
Alto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
THE REGENTS OF THE UNIVERSITY OF CALIFORNIA |
Oakland |
CA |
US |
|
|
Assignee: |
THE REGENTS OF THE UNIVERSITY OF
CALIFORNIA
Oakland
CA
|
Family ID: |
41380735 |
Appl. No.: |
16/195635 |
Filed: |
November 19, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15193105 |
Jun 27, 2016 |
10136860 |
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16195635 |
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14135472 |
Dec 19, 2013 |
9393425 |
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15193105 |
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12454181 |
May 12, 2009 |
8676303 |
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14135472 |
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61052970 |
May 13, 2008 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/0464 20130101;
A61B 5/0452 20130101; A61B 5/4836 20130101; A61N 1/36114 20130101;
A61B 5/042 20130101; A61B 18/14 20130101; G06F 19/00 20130101; A61B
5/0036 20180801; A61B 5/0472 20130101; A61N 1/36592 20130101; A61B
5/0402 20130101; A61B 5/4839 20130101; A61B 5/0245 20130101; A61N
1/3621 20130101; A61B 18/1492 20130101; A61B 5/024 20130101; A61B
5/7275 20130101; A61B 5/046 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/0402 20060101 A61B005/0402; A61B 5/0472 20060101
A61B005/0472; G06F 19/00 20180101 G06F019/00; A61N 1/362 20060101
A61N001/362; A61B 5/0452 20060101 A61B005/0452; A61B 18/14 20060101
A61B018/14; A61N 1/365 20060101 A61N001/365; A61B 5/024 20060101
A61B005/024; A61B 5/042 20060101 A61B005/042; A61B 5/046 20060101
A61B005/046; A61B 5/0464 20060101 A61B005/0464 |
Goverment Interests
GOVERNMENT RIGHTS
[0002] The present invention was made with government support under
Grants HL070529 and HL083359 awarded by the National Institutes of
Health. The Government has certain rights in the invention.
Claims
1. A method for assessing a risk associated with a suspected heart
rhythm disorder in a beating heart, the method comprising:
detecting, using one or more sensors, one or more signals from the
beating heart for a plurality of time segments; pacing the beating
heart for a portion of the plurality of time segments to modify at
least one of rate or regularity of the beating heart; calculating,
via a computer processor, fluctuations in at least one of signal
shape, duration of signal components, signal amplitude and signal
characteristics between a first time segment and a second time
segment of the plurality of time segments, wherein the second time
segment is selected from the portion of the plurality of time
segments during which pacing occurs; generating, via the computer
processor, a risk score for the heart rhythm disorder determined
based at least in part on the fluctuations; and displaying on a
display device in communication with the computer processor a
clinical representation based at least in part on the risk
score.
2. The method of claim 1, wherein the signals comprise action
potentials and the fluctuations comprise changes in a slope of an
action potential restitution curve.
3. The method of claim 2, wherein the action potential duration
restitution curve is defined by action potential durations
corresponding to preceding diastolic intervals associated with the
one or more signals
4. The method of claim 2, wherein the slope of the action potential
duration restitution curve exceeds a threshold.
5. The method of claim 4, wherein the threshold is a value of
one.
6. The method of claim 1, further comprising detecting conduction
slowing.
7. The method of claim 6, wherein the conduction slowing affects at
least one of the fluctuations calculated in response to the
pacing.
8. The method of claim 1, wherein pacing comprises progressively
increasing pacing to determine oscillations in atrial signals prior
to onset of atrial fibrillation.
9. The method of claim 1, wherein pacing is increased or decreased
to prevent a beat from initiating atrial fibrillation.
10. The method of claim 1, wherein pacing is applied by one or more
of an implanted pacemaker electrode, an implanted
cardioverter-defibrillator electrode, and an ablation catheter.
11. The method of claim 1, further comprising ablating tissue of
the beating heart to modify the at least one of the tissue
structure and function.
12. The method of claim 11, further comprising, after ablating,
repeating the steps of pacing, calculating, generating and
displaying until the fluctuations are attenuated or abolished.
13. The method of claim 11, wherein ablating tissue comprises one
or more of applying radiofrequency, infrared, cryoablation,
microwave radiation, or other energy to tissue of the beating
heart.
14. The method of claim 1, wherein the fluctuations are calculated
between alternate beats of the beating heart.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of application Ser. No.
15/193,105, filed Jun. 27, 2016, issued as patent Ser. No.
10/136,860, which is a continuation of application Ser. No.
14/135,472, filed Dec. 19, 2013, issued as U.S. Pat. No. 9,393,425,
which is a continuation of application Ser. No. 12/454,181, filed
May 12, 2009, issued as U.S. Pat. No. 8,676,303, which claims the
benefit of priority of Provisional Application No. 61/052,970,
filed May 13, 2008. Each of the identified applications is
incorporated herein by reference in its entirety.
BACKGROUND
Field of the Invention
[0003] The invention relates generally to the field of medicine and
more specifically to machines and processes for monitoring heart
instability.
Background Art
[0004] Heart rhythm disorders are extremely common in the United
States, and cause significant mortality and morbidity. However,
there are few methods to predict future rhythm disorders
("arrhythmias") before they occur. Instead, physicians rely upon
detecting the actual rhythm disturbance, which precludes early
detection and possible prevention of these disorders. Many methods
currently used also may have serious side-effects. Over 2 million
Americans suffer from Atrial Fibrillation (AF), a rhythm disorder
of the atria (top heart chambers) that causes serious symptoms,
lost days from work, and potentially death (Chugh, Blackshear et
al. 2001). Sadly, AF sometimes is first detected after it has
caused a serious side-effect such as a stroke. Predicting the
future development of AF in an individual can prevent such
catastrophic events. However, clinical practice is so rudimentary
in this area that it relies upon observing episodes of AF to detect
future risk, yet many episodes of AF are still missed or
misclassified (Chugh, Blackshear et al. 2001). Therefore,
prediction of AF has been difficult.
[0005] The same problems exist for predicting a variety of
important heart instabilities. Sudden cardiac arrest is the leading
cause of mortality in the U.S., taking over 300,000 lives per year,
largely due to the rhythm disorders of ventricular tachycardia (VT)
or fibrillation (VF) (Myerburg and Castellanos 2006). Current
methods of predicting future VT/VF are inadequate. In fact, risk is
often not identified until after sudden cardiac arrest (SCA)
occurs--despite the fact that an individual's chance for surviving
out-of-hospital SCA is <10% (Robertson 2000). The inability to
predict future VT/VF also leads to a potential overuse of
preventive therapy in the form of the implantable cardioverter
defibrillators (Myerburg and Castellanos 2006).
[0006] The field is replete with attempts to detect these
disorders. For instance, it has been shown that human AF, VT and
VF, as well as other less serious heart rhythm disorders, can arise
by the mechanism of reentry (Kuo, Munakata et al. 1983). In this
case, barriers (known as "conduction blocks") can develop and cause
otherwise orderly electrical impulses to disorganize into
electrical waves that circulate around the barrier. In addition,
localized regions of scar tissue or ischemic tissue propagate
electrical activity (depolarization) slower than normal tissue,
causing "slow conduction," which may factor into formation of these
wavelets. This may lead to errant, circular propagation. Further,
"reentry" or "circus motion" may result, which disrupts
depolarization and contraction of the atria or ventricles, and
leads to abnormal rhythms ("arrhythmias"). Many tools have been
developed to predict rhythm disorders from these large-scale
effects, with suboptimal results.
[0007] AF often occurs in patients in whom the atria, the top
chamber of the heart, is enlarged or weakened. However, whether the
AF is the cause or effect of the atrial cardiomyopathy ("heart
failure of the atrium") has been unclear.
[0008] In one study (Narayan, Bode et al. 2002b), it is shown that
action potential oscillations during atrial flutter (a different
abnormal heart rhythm) may enable the transition to AF. However,
action potential oscillations during atrial flutter occur in only a
small minority of sufferers with AF. In addition, the study
observed only the right atrium. However, the left atrium is
critical in the initiation of AF, and is the chamber in which
treatment for AF is most effective (Calkins et al., 2007). Action
potential fluctuations also arise in the left atrium near the
pulmonary veins, which leads to AF in a large number of cases
(Calkins et al, 2007).
[0009] Previous studies from patients with atrial cardiomyopathy
undergoing surgery revealed tissue specimens that showed atrium
enlargement, weakening of atrial wall contractions, thickening of
atrial walls, and cell loss and destruction in cases of
cardiomyopathy of the atrium (Frustaci, Chimenti et al. 1997).
Often, but not always, these signs are secondary to disease of the
ventricles. However, testing using tissue specimens is not a viable
clinical tool. Taking tissue from the heart (biopsy) is a risky
procedure that may potentially cause serious side-effects including
death. In the atrium, this is almost never performed unless a
patient is proceeding to surgery for another reason.
[0010] Echocardiography can show weakening of contraction and
enlargement of the atrium. However, this does not specifically
indicate any disease. In fact, weakening can be seen in individuals
without primary atrial cardiomyopathy or AF, who may have other
common and even non-serious diseases of the ventricles including
left ventricular hypertrophy from mild high blood pressure (Thomas,
Levett et al. 2002). Weakening and fibrosis of the wall of the
atrium can slow electrical conduction through its walls. This leads
to a prolonged P-wave duration on the surface ECG. Many studies
have used this measurement to predict AF (Steinberg, Prystowski et
al. 1994), but with modest results because this factor may not be
central to all forms of AF (AF can arise in individuals without
atrial fibrosis or conduction slowing). As a result, these and
related measurements are not often used clinically.
[0011] Other methods used to assess atrial function include
elevated levels of natriuretic peptides, yet these methods have not
been incorporated into clinical practice in humans because their
predictive value is also poor (Therkelsen, Groenning et al. 2004).
Other methods exist to measure atrial size, including magnetic
resonance imaging and other techniques, yet these methods do not
correlate with atrial function. As a result, these methods are not
used in clinical practice.
[0012] Methods that have been proposed to predict AF risk, or track
propensity, are non-specific and not often used. Common methods
include clinical associations, such as identifying-individuals with
thyroxicosis or heart valve disease as having increased risk for
AF. Further, individuals with ventricular disease have higher left
atrial pressures which may predispose them to developing AF. Such
ventricular diseases include simple ones (left ventricular
hypertrophy from aging or high blood pressure), and more complex
ones (ventricular cardiomyopathy). Other methods include
identifying a large left atrial size on imaging (echocardiography,
MRI). However, none of these clinical associations accurately
identifies which individuals will develop AF, or when.
[0013] Other methods have been described that focus on reentrant
mechanisms for AF, but are also not used clinically. Steinberg et
al. (Steinberg, Zelenkofske et al. 1993a), Klein et al. (Klein,
Evans et al. 1995) and others showed that prolonged atrial activity
indicates slow conduction which identifies patients at risk for AF.
Work by Narayan et al. (Narayan, Bode et al. 2002b) suggested that
the presence of alternate beat variations ("alternans") of the
timing, shape or amplitude of the P-wave (or an atrial signal
surrogate) predicts AF. However, those studies pertained only to
patients with existing atrial flutter (a related rhythm disorder)
in the right atrium (that is less important for AF), and used
pacing for studying some of the patients. Thus, the studies did not
demonstrate results relevant to most patients with AF, who do not
have preceding atrial flutter. Other methods include detecting
abnormalities of the sinus node rate that may precede AF (Faddis,
Narayan et al. 1999), but also have limited predictive value.
[0014] Methods directed to preventing heart rhythm disorders
propose fast pacing rates for prevention of arrhythmias, such as
overdrive pacing (at faster rates than observed naturally in the
individual). These methods have had very limited success.
[0015] Atrial cardiomyopathy (heart failure of the top chamber of
the heart) is a relatively new concept, and is not often diagnosed
clinically. As a result, few therapies have been described to treat
atrial cardiomyopathy. However, there is increasing interest in
methods of improving atrial function.
[0016] New evidence suggests that drugs such as
angiotensin-receptor antagonists and angiotensin receptor blockers
can prevent atrial fibrosis and progression of atrial
cardiomyopathy (Wachtell, Lehto et al. 2005). Similar benefits have
been shown for beta-receptor antagonists, and also for agents such
as HMG co-A reductase inhibitors (Ehrlich, Biliczki et al. 2008).
However, these drugs act over years, not acutely, and it is unclear
how well they reverse or stabilize atrial cardiomyopathy that has
already developed. Further, many of these drug benefits were
discovered indirectly in trials designed to examine benefits of the
drugs on ventricular function. Thus, it is not clear if the drugs
would lead to similar benefits in direct prospective trials, and in
the vast majority of patients with atrial cardiomyopathy without
ventricular cardiomyopathy. Finally, many anti-arrhythmic drugs
used to prevent and treat AF are suboptimal (Ehrlich, Biliczki et
al. 2008).
[0017] Ventricular cardiomyopathy is currently viewed predominantly
from a structural perspective. Therefore, an individual's
ventricular disease is tracked by repeated measurement of left
ventricular ejection fraction (LVEF) or the ventricular dimension
on an echocardiography. This poses several problems. First, the
difference between LVEF, for example 30% versus 25%, is of unclear
significance in terms of identifying symptoms, treatment,
prognosis, or risk for VT/VF. Second, echocardiography or
ventriculography are only reproducible for LVEF within broad
ranges, and other methods such as radionuclide angiography are more
cumbersome. Third, clinical practice does not show significance in
day-to-day or week-to-week fluctuations in structural indices.
[0018] Current methods to predict the risk for VT/VF are
non-specific. As mentioned above, the most common risk factor is
the presence of reduced LVEF or heart failure symptoms. However,
these methods over-detect at risk individuals by a factor of up to
18:1 (i.e. 18 individuals have to receive prophylactic ICD therapy
to save one individual who will actually develop VT/VF) (Myerburg
and Castellanos 2006). This method also fails to identify over 50%
of all individuals who experience SCA and whose LVEF is not
reduced. Thus, these criteria are suboptimal.
[0019] Rate response of ventricular action potentials at a slow
heart rate (109 beats per minute--within the range expected for
only mild exertion such as light walking) do not predict VT/VF.
(Narayan et al, 2007): This is consistent with other art (such as
U.S. Pat. Nos. 6,915,156 and 7,313,437 by Christini and colleagues)
which describes methods to control cardiac alternans in action
potential duration by controlling the interval separating beats.
These approaches have not translated into the patient care
setting.
[0020] Other methods proposed to predict VT/VF have had mixed
success. Most of these methods focus on presumed reentrant
mechanisms, and are indirect. These methods include detecting slow
conduction in an ECG (signal averaged ECG) that may indicate a
predisposition to reentry (Cain, Anderson et al. 1996a). Work by
Kleiger et al. (Kleiger, Millar et al. 1987) shows that reduced
24-hour variability in the interval between heart beats ("heart
rate variability") predicts VT or VF. A related method examines
heart rate variability after premature beats (Schmidt, Malik et al.
1999). In a related method, abnormal innervation of the ventricle
assessed using nuclear imaging may identify risk for VT/VF (Arora,
Ferrick et al. 2003b). U.S. Pat. No. 4,802,481 issued to Cohen
(Cohen and Smith 1989) and work by others (Smith, Clancy et al.
1988a; Rosenbaum, Jackson et al. 1994; Narayan, Lindsay et al.
1999d) describes techniques for assessing myocardial electrical
instability as strictly alternate-beat fluctuations in T-wave
energy (also known as "T-wave alternans"). Newer methods such as
described in U.S. Pat. No. 5,555,888 issued to Brewer (Brewer and
Taghizadeh 1996) and work by Marrouche et al. (Marrouche, Pavia et
al. 2002), use alterations in the ventricular activation after
subthreshold current to assess the risk for VT or VF. Finally,
abnormal delayed enhancement of the ventricle using magnetic
resonance imaging may identify risk for VT/VF (Schmidt, Azevedo et
al. 2007). For VT and VF, success has been suboptimal for tools
that probe the reentry circuit with electrophysiologic study
(Buxton, Lee et al. 2000), the signal-averaged ECG to examine slow
conduction (Cain, Anderson et al. 1996a), and indices of
repolarization including T-wave alternans (Narayan 2006a) and QT
dispersion (Brendorp, Elming et al. 2001).
[0021] More recent work suggests that nervous activity/innervation
can increase the risk for VT/VF (Stein, Domitrovich et al. 2005)
and for AF (Patterson, Po et al. 2005). However, the mechanism
linking autonomic activity with arrhythmias is
unclear--particularly in humans. Of note, none of these techniques
are part of implantation planning for cardioverter defibrillators
(ACC/AHA/ESC 2006) or are used routinely in the clinic. All have
suboptimal predictive value, and therefore tend to be more of a
rough guide to risk rather than a predictive tool.
[0022] Several approaches have been described to improve
ventricular cardiomyopathy. However, none of these methods work in
all patient populations, and some failed to reduce VT/VF in tandem
with improvements in heart failure (Bradley 2003b). Some drugs have
been shown to improve ventricular function in cardiomyopathy. These
include angiotensin-receptor antagonists and angiotensin receptor
blockers, and beta-receptor antagonists (Poole-Wilson, Swedberg et
al. 2003). However, these drugs act over years, rather than
acutely.
[0023] Over the past decade, it has been shown that cardiac
resynchronization therapy also improves ventricular cardiomyopathy
in patients with reduced LVEF, heart failure and evidence for
delayed activation between ventricles (Abraham, Fisher et al.
2002). However, it remains unclear whether cardiac
resynchronization therapy itself improves the aspects of heart
failure that lead to VT/VF, which is why many physicians implant an
ICD in tandem with a resynchronization device (ACC/AHA/ESC 2006).
Placing a pacing lead close to an arrhythmia circuit enables easier
termination than if leads are remote from that location (Stevenson,
Khan et al. 1993; Morton, Sanders et al. 2002a). However, current
studies poorly describe methods of placing a permanent pacemaker or
defibrillator lead to reduce VT/VF. Tse et al. (Xu, Tse et al.
2002), Leclercq et al. (LeClercq, Victor et al. 2000), and Meisel
et al. (Meisel, Pfeiffer et al. 2001) among others, show that
carefully selected ventricular pacing--particularly in the left
ventricle--can improve hemodynamics and, based on work by Zagrodzky
et al. (Zagrodzky, Ramaswamy et al. 2001), reduce arrhythmia
incidence.
[0024] Further, it is known that pacing in certain regions of the
heart, such as the right ventricle, can lead to right ventricular
cardiomyopathy (DAVID 2002). However, many patients do not
experience right ventricular cardiomyopathy due to pacing, and
physicians still practice right ventricular pacing. However, the
only way to determine if the detrimental effect is developing is to
examine worsening in LVEF.
[0025] In animals, AF or atrial cardiomyopathy is not spontaneous
but rather is caused by very rapid pacing or toxic drugs. In
animals with experimentally induced atrial fibrillation, one can
see evidence of the changes in atrial cardiomyopathy from histology
or at the sub-cellular level (Ausma, van der Velden et al. 2003).
However, as described above, obtaining tissue samples from human
atria in human being is very difficult. Human AF is also different
from AF in animal models. Therefore, there exists a need to better
detect heart instability in humans.
[0026] The following references provide additional background
information: Abraham, W. T., et al. (2002), "Cardiac
Resynchronization in Chronic Heart Failure", N Engl J Med. 346:
1845-1853; ACC/AHA/ESC (2006). "ACC/AHA/ESC 2006 Guidelines for
Management of Patients With Ventricular Arrhythmias and the
Prevention of Sudden Cardiac Death--Executive Summary. A Report of
the American College of Cardiology/American Heart Association Task
Force and the European Society of Cardiology Committee for Practice
Guidelines (Writing Committee to Develop Guidelines for Management
of Patients With Ventricular Arrhythmias and the Prevention of
Sudden Cardiac Death)", J Am Coll Cardiol 48(5): 1064-1108; Arora,
R., et al. (2003b), "I-123 MIBG imaging and heart rate variability
analysis to predict the need for an implantable cardioverter
defibrillator", Journal of Nuclear Cardiology 10(2): 121-131;
Ausma, J., et al. (2003), "Reverse Structural and Gap-Junctional
Remodeling After Prolonged Atrial Fibrillation in the Goat",
Circulation 107(15): 2051-2058; Bloomfield, D. M., et al. (2002).
"Interpretation and Classification of Microvolt T-Wave Alternans
Tests", J. Cardiovasc Electrophysiol. 13(5): 502-512; Bradley, D.
J. (2003b), "Combining Resynchronization and Defibrillation
Therapies for Heart Failure", JAMA 289(20): 2719; Brendorp, B., et
al. (2001), "QT Dispersion Has No Prognostic Information for
Patients With Advanced Congestive Heart Failure and Reduced Left
Ventricular Systolic Function", Circulation 103: 831-5; Brewer, J.
E. and E. Taghizadeh, U.S. Pat. No. 5,555,888, "Method for
automatic, adaptive, active facilitation to access myocardial
electrical instability"; Bristow, M. R., et al. (2004),
"Cardiac-Resynchronization Therapy with or without an Implantable
Defibrillator in Advanced Chronic Heart Failure", N Engl J Med
350(21): 2140-2150; Buxton, A. E., et al. (2000),
"Electrophysiologic testing to identify patients with coronary
artery disease who are at risk for sudden death. Multicenter
Unsustained Tachycardia Trial Investigators (MUSTT)." N Engl J Med.
342(26): 1937-45; Cain, M. E., et al. (1996a), "Signal-Averaged
Electrocardiography: ACC Consensus Document", J. Am. Coll. Cardiol.
27(1): 238-49; Calkins H., et al. (2007), Heart Rhythm 4:816-861;
Chugh, S. S., et al. (2001), "Epidemiology and natural history of
atrial fibrillation: clinical implications." J Am Coll Cardiol
37(2): 371-8; Cohen, R. J. and J. M. Smith U.S. Pat. No. 4,802,491
(1989), "Method and apparatus for assessing myocardial electrical
instability"; David, D. T. I. (2002), "Dual-Chamber Pacing or
Ventricular Backup Pacing in Patients With an Implantable
Defibrillator: The Dual Chamber and VVI Implantable Defibrillator
(DAVID) Trial", J Am Medical Association 288(No. 24): 3115-3123;
Ehrlich, J. R., et al. (2008), "Atrial-selective approaches for the
treatment of atrial fibrillation." J Am Coll Cardiol 51(8): 787-92;
Faddis, M. N., et al. (1999), "A Decrease in Approximate Entropy
Predicts The Onset of Atrial Fibrillation" [abstract], Pacing and
Clinical Electrophysiology 22(4 (part II)): 358; Franz, M. R., et
al. (1988a). "Cycle length dependence of human action potential
duration in vivo. Effects of single extrastimuli, sudden sustained
rate acceleration and deceleration, and different steady-state
frequencies", J Clin Invest 82(3): 972-979; Frustaci, A., et al.
(1997), "Histological Substrate of Atrial Biopsies in Patients With
Lone Atrial Fibrillation." Circulation 96(4): 1180-1184; Gold, M.
R., et al. (2000a). "A Comparison of T Wave Alternans, Signal
Averaged Electrocardiography and Programmed Ventricular Stimulation
for Arrhythmia Risk Stratification." J. Am. Coll. Cardiol. 36:
2247-2253; Gong et al. (2007) Circulation 115: 2092-2102. [0050]
Hao, S., D. Christini, et al. (2004), "Effect of beta-adrenergic
blockade on dynamic electrical restitution in vivo", Am J Physiol
Heart Circ Physiol 287(1): H390-4; Kalb, S., et al. (2004). "The
restitution portrait: a new method for investigating rate-dependent
restitution", J Cardiovasc Electrophysiol 15(6): 698-709. Kleiger,
R. E., P. Millar, et al. (1987). "Decreased heart rate variability
and its association with increased mortality after acute myocardial
infarction", Am. J. Cardiol. 59: 256-262. [0053] Klein, M., S. J.
Evans, et al. (1995). "Use of P-wave triggered, P-wave
signal-averaged electrocardiogram to predict atrial fibrillation
after coronary bypass surgery." Am. Heart J. 129(5): 895-901; Kuo,
C.-S., et al. (1983), "Characteristics and possible mechanism of
ventricular arrhythmia dependent on the dispersion of action
potential durations", Circulation 67: 1356-1367; Laurita, K. R. and
D. S. Rosenbaum (2008), "Mechanisms and potential therapeutic
targets for ventricular arrhythmias associated with impaired
cardiac calcium cycling." J Mol Cell Cardiol 44(1): 31-43;
LeClercq, C., F. Victor, et al. (2000), "Comparative effects of
permanent biventricular pacing for refractory heart failure in
patients with stable sinus rhythm or chronic atrial fibrillation",
The American Journal of Cardiology 85(9): 1154-1156; Marrouche, et
al. (2002), "Nonexcitatory stimulus delivery improves left
ventricular function in hearts with left bundle branch block", J
Cardiovasc Electrophysiol 13(7): 691-5; Meisel, E., et al. (2001),
"Investigation of coronary venous anatomy by retrograde venography
in patients with malignant ventricular tachycardia", Circulation
104(4): 442-447; Morton, J. B., et al. (2002a), "Sensitivity and
specificity of concealed entrainment for the identification of a
critical isthmus in the atrium: relationship to rate, anatomic
location and antidromic penetration", J Am Coll Cardiol 39(5):
896-906; Myerburg, R. J. and A. Castellanos (2006), "Emerging
paradigms of the epidemiology and demographics of sudden cardiac
arrest", Heart Rhythm 3(2): 235-239; Narayan, S. M. (2006a),
"T-Wave Alternans and The Susceptibility to Ventricular
Arrhythmias: State of the Art Paper", J Am Coll Cardiol 47(2):
269-281; Narayan, S. M., et al. (2002b), "Alternans Of Atrial
Action Potentials As A Precursor Of Atrial Fibrillation",
Circulation 106: 1968-1973; Narayan, S. M., et al., "T-wave
alternans, Restitution of Ventricular action potential duration and
outcome", J Am Coll Cardiol 2007; 50: 2385-2392; Narayan, S. M., et
al. (1999d), "Demonstrating the Pro-arrhythmic Preconditioning of
Single Premature Extrastimuli Using the Magnitude, Phase and
Temporal Distribution of Repolarization Alternans", Circulation
100: 1887-1893; Narayan, S. M. and J. M. Smith (1999b), "Spectral
Analysis of Periodic Fluctuations in ECG Repolarization", IEEE
Transactions in Biomedical Engineering 46(2): 203-212; Narayan, S.
M. and J. M. Smith (2000c), "Exploiting Rate Hysteresis in
Repolarization Alternans to Optimize the Sensitivity and
Specificity for Ventricular Tachycardia", J. Am. Coll. Cardiol.
35(5): 1485-1492; Patterson, E., et al. (2005). "Triggered firing
in pulmonary veins initiated by in vitro autonomic nerve
stimulation", Heart Rhythm 2(6): 624-31; Poole-Wilson, P. A., K.
Swedberg, et al. (2003), "Comparison of carvedilol and metoprolol
on clinical outcomes in patients with chronic heart failure in the
Carvedilol Or Metoprolol European Trial (COMET): randomised
controlled trial", The Lancet 362(9377): 7-13; Robertson, R. M.
(2000, "Sudden Death from Cardiac Arrest--Improving the Odds", N
Engl J Med 343(17): 1259-1260; Rosenbaum, D. S., et al. (1994),
"Electrical alternans and vulnerability to ventricular
arrhythmias", N Engl J Med 330(4): 235-41; Schauerte, P., et al.
(2000a), "Transvenous Parasympathetic Nerve stimulation in the
Inferior Vena Cava and Atrioventricular Conduction", J.
Cardiovascular Electrophysiol. 11(1): 64-69; Scherlag, B. J., et
al. (2005), "Electrical Stimulation to Identify Neural Elements on
the Heart: Their Role in Atrial Fibrillation", Journal of
Interventional Cardiac Electrophysiology 13(0): 37-42; Schmidt, A.,
et al. (2007), "Infarct tissue heterogeneity by magnetic resonance
imaging identifies enhanced cardiac arrhythmia susceptibility in
patients with left ventricular dysfunction", Circulation 115(15):
2006-14; Schmidt, G., et al. (1999), "Heart-rate turbulence after
ventricular premature beats as a predictor of mortality after acute
myocardial infarction", Lancet 353: 1390-1396; Smith, J. M., et al.
(1988a), "Electrical Alternans and cardiac electrical instability",
Circulation 77(1): 110-21; Stambler, B. S. and K. A. Ellenbogen
(1996b), "Elucidating the Mechanisms of Atrial Flutter Cycle Length
Variability Using Power Spectral Analysis Techniques", Circulation
94(10): 2515-2525; Stein, P. K., et al. (2005), "Traditional and
Nonlinear Heart Rate Variability Are Each Independently Associated
with Mortality after Myocardial Infarction", Journal of
Cardiovascular Electrophysiology 16(1): 13-20; Steinberg, J. S., et
al. (1994), "Use of the signal-averaged electrocardiogram for
predicting inducible ventricular tachycardia in patients with
unexplained syncope. Relationship to clinical variables in a
multivariate analysis", J. Am. Coll. Card. 23: 99; Steinberg, J.
S., et al. (1993a), "The value of the P-wave signal-averaged
electrocardiogram for predicting atrial fibrillation after cardiac
surgery", Circulation 88:2618; Stevenson, W. G., et al. (1993),
"Identification of reentry circuit sites during catheter mapping
and radiofrequency ablation of ventricular tachycardia late after
myocardial infarction", Circulation 88: 1647-1670; Therkelsen, S.,
et al. (2004), "Atrial Volume and ANP in Persistent Atrial
Fibrillation--Before and After Cardioversion" (abstract),
Circulation 110(17 Suppl); Thomas, L., et al. (2002), "Compensatory
changes in atrial volumes with normal aging: is atrial enlargement
inevitable?", Journal of the American College of Cardiology 40(9):
1630-1635; Wachtell, K., M. Lehto, et al. (2005), "Angiotensin II
receptor blockade reduces new-onset atrial fibrillation and
subsequent stroke compared to atenolol: The Losartan Intervention
For End point reduction in hypertension (LIFE) study", Journal of
the American College of Cardiology 45(5): 712-719; Walker, et al.
(2003), "Hysteresis Effect Implicates Calcium Cycling as a
Mechanism of Repolarization Alternans", Circulation 108(21):
2704-2709; Watanabe, K., et al. (1980), "Computer Analysis of the
Exercise ECG: A Review", Prog. Cardiovasc. Dis. 22(6): 423-446;
Weiss, J. N., et al. (2006). "From Pulsus to Pulseless: The Saga of
Cardiac Alternans (Review)", Circ Res 98: 1244; Xu, W., et al.
(2002), "New Bayesian Discriminator for Detection of Atrial
Tachyarrhythmias", Circulation 105: 1472-1479; Zagrodzky, J. D., et
al. (2001), "Biventricular pacing decreases the inducibility of
ventricular tachycardia in patients with ischemic cardiomyopathy",
Am J Cardiol 87(10): 1208-1210.
SUMMARY OF THE INVENTION
[0027] In one embodiment, a method of diagnosing the onset of heart
instability comprises monitoring signals from a beating human
heart, detecting one or a combination of oscillations in the signal
shape, oscillations in the signal duration or changes in signal
characteristics in response to changes in rate or beat
irregularity; and assigning a risk for heart instability based at
least in part on the detecting.
[0028] In another embodiment, a method of treating the potential
onset of heart instability comprises monitoring signals from a
beating human heart, detecting one or a combination of oscillations
in the signal shape, oscillations in the signal duration or changes
in signal characteristics in response to changes in rate or beat
irregularity, modifying tissue structure and/or function, detecting
attenuated oscillations and/or changes in signal characteristics in
response changes in rate and/or beat irregularity.
[0029] In another embodiment, an apparatus for diagnosing and
treating heart instability comprises one or more sensors configured
to detect signals from a beating human heart, means for detecting
one or a combination of oscillations in the signal shape,
oscillations in the signal duration or changes in signal
characteristics in response to changes in rate or beat
irregularity, means for assigning a risk for heart rhythm
irregularities based at least in part on the detecting, and means
for modifying tissue structure and/or function based at least in
part on the detecting.
[0030] In another embodiment, an apparatus for creating a risk
assessment for heart rhythm irregularities comprises at least one
sensor, an analytic engine comprising a module configured to
measure a signal received via the at least one sensor, a module
configured to measure a rate-response of the signal at a plurality
of rates, and a module configured to produce and/or change a
determined risk for heart rhythm irregularities based at least in
part on the rate-response measurement.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawing(s) will be provided by the Office
upon request and payment of the necessary fee.
[0032] FIGS. 1A-1C illustrate monophasic heart action potential
signals that may be analyzed according to certain embodiments.
[0033] FIGS. 2A-2B illustrate one embodiment of analyzing human
ventricular action potential signals to compute a rate-behavior
(restitution) curve.
[0034] FIG. 2C illustrates the direct relationship between steep
atrial rate-behavior (restitution) of action potentials, and
immediate onset of atrial fibrillation in a human subject.
[0035] FIGS. 3A-3B illustrate one embodiment of analyzing human
atrial signals to compute a rate-behavior (restitution) curve;
[0036] FIG. 4 illustrates one embodiment of analyzing human heart
signals for dynamic (rate-related) conduction slowing, when beats
are early (and have short diastolic intervals);
[0037] FIGS. 5A and 5B further illustrate rate-related conduction
slowing, again most prominent for early beats (short diastolic
intervals).
[0038] FIG. 6 illustrates fluctuations/oscillations in human
ventricular signals (action potentials) in a human subject who
later developed ventricular arrhythmias after many months.
[0039] FIGS. 7A-7C illustrate rate-related
fluctuations/oscillations in human atrial signals (action
potentials) in a human subject who was largely without symptoms but
who subsequently developed atrial fibrillation.
[0040] FIG. 8 illustrates one embodiment of a method of treatment
of altering activation sequence to allow cellular metabolic
components to regain equilibrium.
[0041] FIG. 9 illustrates embodiments of major electrical (pacing)
modes of treatment.
[0042] FIG. 10 illustrates out-of-phase pacing modes of treatment,
to attenuate oscillations that may lead to disease.
[0043] FIG. 11 illustrates one embodiment of a system for analyzing
and treating heart instability.
[0044] FIGS. 12A and 12B are a flowchart of one embodiment of a
method of determining a risk level of a patient for developing
heart instability and determining efficacy of applied treatment
protocols.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0045] Detailed descriptions of certain embodiments are provided
herein. It is to be understood, however, that the present invention
may be embodied in various forms. Therefore, specific details
disclosed herein are not to be interpreted as limiting, but rather
as a basis for the claims and as a representative basis for
teaching one skilled in the art to employ the embodiments in
virtually any appropriately detailed system, structure or manner.
Before moving on to specific details, certain aspects of certain
embodiments of the invention are presented below.
[0046] Embodiments of methods and systems for treating heart or
possibly other organ instability are disclosed herein. In
accordance with one embodiment, there is disclosed a method to
create an index of health and disease computed from fluctuations in
a biologic signal. The index of health or disease may pertain to
components of an organ. The signal may comprise many types.
[0047] Certain embodiments comprise an apparatus to measure the
biological signal with sufficient fidelity to detect small
variations. The apparatus may perturb the biological signal. The
apparatus may attenuate the index of disease and thus improve
health.
[0048] Certain embodiments may include a method to create an index
of health and disease computed from fluctuations in a biologic
signal including one or more of the following: wherein said index
measures the periodicity, amplitude and phase of oscillations in
the signal over time; wherein said oscillations are repetitive;
wherein said oscillations do not repeat; wherein said signal
oscillations vary after a perturbation; wherein the perturbation is
altered activation rate or sequence; wherein the perturbation is
altered activation of the autonomic nervous system; wherein the
perturbation is modification of tissue structure; wherein the
perturbation is modification of tissue function (behavior); wherein
said signal oscillations measure biochemical changes in the organ
cells responsible for disease; wherein said biochemical changes are
fluctuations in calcium; wherein said biochemical changes are
fluctuations in potassium; wherein said biochemical changes are
fluctuations in metabolic components; wherein said index measures
variations of said signal between regions of the organ or within
the body; wherein variations over time are measured from
activations of components of an organ; and wherein the organ is the
heart.
[0049] In certain embodiments, the index of health or disease
pertains to components of an organ. Some of the embodiments may
include one or more of the following: wherein said index of health
or disease pertains to components of an organ; wherein the organ is
the heart; wherein the components are the atria; wherein the
components are the ventricles; wherein disease refers to
cardiomyopathy; wherein disease refers to rhythm disorders; wherein
disease refers to coronary artery disease; wherein rhythm disorders
include atrial tachycardia, atrial fibrillation, ventricular
tachycardia or ventricular fibrillation; wherein disease refers to
medication side-effects; wherein health refers to absence of
cardiomyopathy; wherein health refers to absence of heart rhythm
disorders; wherein health refers to improved autonomic nervous
system regulation; wherein health refers to improved hormonal
regulation; wherein health refers to effectiveness of therapy;
wherein therapy is cardiac device therapy; wherein therapy is
transplantation; wherein the organ is smooth muscle, such as in the
gastrointestinal or respiratory systems; wherein the organ is
skeletal muscle; wherein the organ is the brain.
[0050] In certain embodiments, the biological signal comprises one
or more of the following: an intracellular or extracellular action
potential wherein variations pertain to the shape, wherein
variations pertain to the duration, and/or wherein variations
pertain to the amplitude; a monophasic action potential; an organ
electrogram, wherein variations pertain to activation, wherein
variations pertain to repolarization, and/or wherein variations
pertain to diastole; an electrocardiogram wherein variations
pertain to the QRS and T-waves, wherein variations pertain to the
QT interval, wherein variations pertain to the ST segment, and/or
wherein variations pertain to the TP segment; a magnetocardiogram
wherein variations pertain to the QRS and T-waves, wherein
variations pertain to the QT interval, represents conduction time
between regions of the organ, and/or wherein conduction slows for
certain perturbations; a measure of tissue motion, wherein
variations measure wall motion on echocardiography, wherein
variations measure wall motion on tissue Doppler imaging, and/or
wherein variations pertain to the echocardiographic displacement of
the atrioventricular ring.
[0051] In certain embodiments, the biological signal indicates one
or more of the following: functioning of the central nervous
system; functioning of the respiratory system; functioning of the
urogenital system; functioning of the gastrointestinal system;
functioning of smooth muscle; functioning of skeletal muscle.
[0052] Other embodiments may include an apparatus to measure the
biological signal with sufficient fidelity to detect small
variations, the apparatus comprising: a sensor; noise-reduction and
filtering apparatus; means of transmitting said signal using
physical media, as electrical signals along wires or within body
fluid; means of transmitting said signal wirelessly; an apparatus
to analyze the signal to construct said index; an apparatus to
communicate the said index to the health care provider and patient;
wherein the sensor is in contact with the organ; wherein the sensor
is a pacing lead; wherein the sensor is elsewhere in the body but
not in contact with the organ; wherein the sensor does not contact
the body and remotely measures said signal.
[0053] Certain embodiments may include an apparatus to perturb said
biological signal comprising: means for altering the rate of
activation of the organ; means for altering the sequence
(regularity or irregularity) of activation of the organ; means for
altering autonomic nervous control of the organ; wherein the
autonomic nervous control is of the atria of the heart; wherein the
autonomic nervous control is of the ventricles of the heart; means
for altering hormonal influences of the organ; and means for
altering biochemical equilibrium in the organ.
[0054] Certain embodiments may include an apparatus to attenuate
said index of disease, prevent onset of the heart instability and
thus improve health, the apparatus including one or more of the
following: means for increasing or decreasing activation rate if
said index is found to be dependent upon heart rate; means for
altering the pattern of activation of the organ, based on said
index; wherein the new pattern enables cellular processes to
normalize; wherein the new pattern includes slow activations for
cellular processes to regain equilibrium; wherein the new pattern
includes fast activations to achieve a desired average heart rate;
wherein the new pattern attenuates disease-forming oscillations to
prevent the disease onset; wherein the new pattern is non-regular;
wherein the new pattern is regular; means for modifying tissue
structure, such as by ablation; means for modifying tissue
function, such as by using an external electrical field; means for
modifying autonomic nervous regulation of the organ by altering the
structure or function or nerves; wherein the nerves alter
functioning in the heart atria; wherein the nerves alter
functioning in the heart ventricles; wherein the nerves alter
functioning in the gastrointestinal tract; wherein the nerves are
in the central nervous system; wherein the nerves alter functioning
in the urogenitary system; wherein the nerves alter functioning in
the respiratory system.
[0055] Biological systems require many checks and balances to
ensure stability and health of the individual. These checks and
balances are implemented by complex regulatory systems. Although
many potentially serious diseases arise if these systems fail, few
methods exist in humans to detect impending failure of these
systems to diagnose disease at an early stage, to guide therapy, or
to track its effectiveness. Systems and methods are described
herein that may detect impending failure of such regulatory systems
and, in the process, predict and prevent disease.
[0056] Without being bound to any particular theory of operation,
the inventor has recognized that AF and VT/VF may start from subtle
cellular metabolic abnormalities, rather than clear structural
disease. Fundamentally, the failure of the arrhythmia prediction
tools utilized to date may stem from their inability to accurately
or consistently detect these abnormalities. Thus, in some
embodiments, it is postulated that subtle abnormalities in cell
functioning can be uncovered to diagnose disease at an early stage.
Methods and systems of treatment are described which attenuate
abnormalities and prevent the onset and the progression of disease
(e.g. in the atrium and/or the ventricle), including the onset of
heart rhythm instability.
[0057] As described herein, the inventor has found that AF is
linked with oscillations and abnormal rate-behavior of atrial
action potentials in humans. The inventor has also linked VT/VF
with oscillations and abnormal rate-behavior of ventricular action
potentials in humans. These signals can indicate impending failure
of cell-functions such as regulation of intracellular calcium,
which is linked to the eventual failure of mechanical function
(cardiomyopathy) and electrical function (heart rhythm disorders).
Certain embodiments thus detect risk at an earlier stage and also
guide effective therapy to stabilize such oscillations and prevent
disease.
[0058] It has also been found by the inventor that nervous
activation predisposes humans to arrhythmias (atrial and
ventricular) by altering cell-level mechanisms which cause
fluctuations or abnormal rate-behavior of action potentials. Thus,
certain embodiments are based on the inventor's recognition that
these fluctuations represent the abnormal cellular handling of
calcium or other biochemicals. The abnormal cellular handling of
calcium or other biochemicals has been seen to indicate arrhythmias
in canines and other animal studies (Patterson, Po et al. 2005),
but has not previously been shown in humans. Over time, this
imbalance may worsen which explains progressive cardiomyopathy
(increasing atrial size) and an increasing risk for AF. Thus,
certain embodiments described herein detect failure in the cellular
regulation of calcium, and thus potential atrial cardiomyopathy
(heart failure), in the beating heart of patients by tracking the
underlying failure of regulation mechanisms.
[0059] Abnormal cellular calcium regulation due to ventricular
cardiomyopathy may also explain VT or VF onset. Certain
embodiments, therefore, measure fluctuations in heart electrical
(or mechanical) signals to probe the failure of regulatory
mechanisms underlying VT/VF. In certain embodiments, treatment can
also be provided to attenuate fluctuations and suppress VT/VF, and
certain such embodiments can then track the effectiveness of this
and other treatments.
[0060] The physiological basis of the detection methods described
herein can be described with reference to FIGS. 1A-7C which provide
example signals and signal characteristics that can be analyzed in
some embodiments of the invention. As described further below, a
wide variety of signal types can be analyzed in accordance with the
principles of the invention, and the specific embodiments below are
merely exemplary.
[0061] FIG. 1A illustrates a series of human action potential (AP
or MAP) signals 105 recorded from a location in a human ventricle.
FIG. 1B shows two individual action potentials recorded from a
location in the left atrium 110 and a location in the left
ventricle 120 respectively. Each action potential has phases 0, 1,
2, 3 and 4. Phases 0-1 indicate depolarization and phases 2-3
indicate repolarization. Phase 4 indicates the time interval from
one beat to the next. In certain embodiments, the rate response
(restitution) of one or more components may be determined, focusing
on rate-response of AP duration (time from phase 0-3) and AP phase
2 amplitude.
[0062] FIG. 1C illustrates various signal characteristics of action
potentials that can be analyzed to indicate cellular events and
thus predict propensity for AF and VT/VF. For example, for action
potentials, the time taken from onset of the action potential
(phase 0) to the time of 90% repolarization from the plateau phase
(near the end of phase 3) designated 130 in FIG. 1C is termed
APD90. The diastolic interval 140 is the time from the APD point of
the prior beat to the initiation of the beat in question. In
various embodiments of the invention, any repolarization phase can
be used (for instance, APD70, APD80), and so the term APD will be
used herein to refer to any and all of these repolarization phases.
In addition, certain surrogates may be used to measure
repolarization phases. Some surrogates may better correlate with
certain repolarization phases; in particular, unipolar electrogram
activation recovery intervals correlate well with APD90 (Yue,
Paisey and coworkers, Circulation 2004). Rate response
characteristics of the AP biosignal and their relationship to
arrhythmias are illustrated in FIGS. 2A-7C.
[0063] FIGS. 2A and 2B illustrate one embodiment of analyzing human
ventricular signals to compute a rate-behavior (restitution) curve.
FIGS. 2A and 2B illustrate ventricular action potentials, although
analysis may be analogous for any biosignal. In both FIGS. 2A and
2B, the top panels 210 and 220 respectively each illustrate for two
different subjects a series of regular beats of the heart then an
early beat. These panels thus illustrate a beat irregularity in
that the interval between beats is not constant over the time of
observation. To analyze these signals, action potentials are
separated, and each measured to determine diastolic interval and
APD as shown in FIG. 1C.
[0064] Bottom panels 230 and 250 respectively illustrate the rate
response (restitution) curves for APD for these two subjects. The
rate-behavior (restitution) curve is traditionally plotted as the
APD (vertically) against the preceding diastolic interval
separating one beat from the next (shown in item 140). The
illustrated restitution curve is created from early beats (as in
panels 210, 220), but can also be created during any rate
variations, such as variations in heart rate between rest (slow),
minimal exertion (moderate range rate) and maximal exertion
(fastest rate).
[0065] The APD restitution curve is conventionally described by
several parameters, including its maximum slope (illustrated in
items 230, 250), range between minimum and maximum APD and the
longest diastolic interval for which slope is greater than 1. It
has been shown in animals, but never before in humans, that maximum
APD restitution slope >1 predicts spontaneous arrhythmias. For
example, the individual associated with the data in FIG. 2A had
maximum APD restitution slope <1, and did not experience
arrhythmias on follow-up. The individual associated with FIG. 2B
had APD restitution slope >1 and did experience arrhythmias.
Notably, restitution (rate response) can be measured for any signal
component, such as upstroke velocity (phase I, sodium channel
functioning), plateau voltage (phase II, sodium, calcium and other
channel functioning), duration (phase III/IV, calcium and potassium
channel function), and fluctuations in diastole (between action
potentials) that may indicate disequilibrium in a variety of cell
components.
[0066] FIG. 2C illustrates atrial action potential restitution from
a human patient with minimal structural disease who has paroxysmal
AF. This shows the direct relationship between steep atrial
rate-behavior (restitution) of action potentials, and immediate
onset of atrial fibrillation in a human subject. Panel 260 shows
that a single premature beat (S2) initiates AF (beats labeled
F1-F7) in the patient. The APD for each beat, and its preceding
diastolic interval, is shown. Notably, extreme APD oscillations are
seen leading to wave break and AF. Panel 270 illustrates how steep
APD restitution in this patient results from extreme oscillations
from beat S1 to S2 to F1, F2, F3 and so on in AF. This further
validates the importance of steep atrial restitution in causing
human AF.
[0067] FIGS. 3A and 3B illustrate another example of analyzing
human atrial signals to compute a rate-behavior (restitution)
curve. Top panels 310 and 320 show a series of atrial beats then an
early beat. The signal illustrated in FIG. 3A was obtained from a
subject that had no AF history. The signal illustrated in FIG. 3B
was obtained from a subject that had paroxysmal AF. For both
signals, action potentials are measured and parsed as before (FIG.
1C, FIGS. 2A-2C).
[0068] Lower panels 330 and 350 show the rate response
(restitution) curves for APD for these subjects. Again, the
rate-behavior (restitution) curve is plotted as the APD
(vertically) against the diastolic interval separating one beat
from the next. As above, the illustrated restitution curve is
created from early beats (as in panels 310 and 320), but can also
be created from beat-to-beat variations between rest (slow rates),
minimal exertion (moderate rates) and maximal exertion (fastest
rates). The restitution curve is described by several parameters,
including its maximum slope, the range between maximum and minimum
APD, and the longest diastolic interval for which slope is greater
than 1.
[0069] The individual in FIG. 3A is a control subject with no AF,
whose maximum APD restitution slope <1. The individual in FIG.
3B did experience AF and had APD restitution slope >1.
[0070] Interestingly, FIG. 4 shows an individual with longstanding
AF and conduction slowing on extra beats. This delayed the actual
timing of the early beat, thus truncating the left portion of the
APD restitution curve and producing slope <1. This may be the
reason that it has never before been shown in human atria that APD
restitution slope >1 identifies patients who will develop AF.
Because conduction slowing in subjects with longstanding AF has
masked recognition of the correlation, the inventor is the first to
utilize this relationship in human diagnosis and treatment.
[0071] The effect of conduction slowing is illustrated further in
FIGS. 5A and 5B. FIGS. 5A and 5B illustrate one embodiment of
analyzing human heart signals for dynamic (rate-related) conduction
slowing. The effect of conduction slowing on interpretation of
results obtained from restitution analysis is described with
respect to this Figure. FIG. 5A shows that conduction slowing
(prolonged activation time) in subjects with early stage
(paroxysmal) AF occurs only for very early beats (with very short
diastolic intervals). It is relatively difficult to uncover such
slowing. This is similar to predictions from computer models (Gong,
et al. 2007), but has never previously been observed in the atria
of humans. Conversely, FIG. 5B shows that, in a subject with
longstanding advanced AF, conduction slows even for less-early
beats (or relatively slow rates, with long diastolic intervals). In
other words, conduction slowing is observed more easily.
Accordingly, conduction slowing explains observed APD restitution
flattening in patients with persistent AF (atrial
cardiomyopathy)--in which conduction delay for the earliest beats
truncates the leftmost portion of the APD rate-behavior
(restitution) curve.
[0072] In addition to APD rate behavior, the inventor has also
found that beat to beat fluctuations or oscillations in MAP wave
shape (such as phase II amplitudes) can also be predictive of
arrhythmias.
[0073] FIG. 6 illustrates fluctuations in human ventricular signals
(action potentials) in a human subject who later developed
ventricular arrhythmias after many months. These examples of
fluctuations in human action potentials mechanistically precede and
thus predict arrhythmias. As described below, baseline corrected
biosignals may be used first.
[0074] In FIG. 6, panel 610 shows ventricular action potentials in
a human subject who went on to develop potentially lethal
ventricular arrhythmias. In panel 620, these action potentials were
aligned as described below. As can be seen, the even beats (coded
blue) and odd beats (coded red) segregate based on shape. In other
words, there are beat-to-beat fluctuations that alternate in this
case (alternans; other patients may demonstrate fluctuations on a
third-beat basis or with some other periodicity). Although this can
be quantified visually, spectral decomposition as illustrated by
use of a fast Fourier transform (FFT) provides reproducible
quantification.
[0075] Panel 630 of FIG. 6 illustrates a spectral analysis of the
action potentials of panel 610. In this case, 64 contiguous APs
were selected, baseline corrected to the mean of a 10 ms segment
starting 20 ms prior to phase 0 maximum dV/dt, and aligned to their
upstroke (phases 0-1) (Narayan and Smith 1999b). Successive APs
were represented as 2-D matrices R (n, t), where n indicates beat
number (0.ltoreq.n.ltoreq.63), and t the time sample (Narayan and
Smith 1999b). A Fast Fourier Transform (FFT) was used to compute
power spectra across beats (arrow-wise in FIG. 1C) for each t, and
then spectra were summated for portions of the AP. Spectral AP
fluctuations magnitude was represented by the dimensionless
k-score:
T - .mu. noise .sigma. noise , ##EQU00001##
where .SIGMA.T is spectral magnitude at 0.5 cycles/beat, and
.mu..sub.noise and .sigma..sub.noise are the mean and SD of noise,
respectively. The noise window was selected adjacent to alternans
frequency (0.33-0.49 Hz) to avoid the 0.125-0.25 Hz respiratory
peak (Narayan and Smith 1999b). A k>0 indicates that the
magnitude of fluctuations (which may represent alternans) exceeds
noise (Bloomfield, Hohnloser et al. 2002). The mean voltage of
alternation V.sub.alt across the AP duration (also referenced to
the noise floor) was estimated as:
T - .mu. noise AP duration ( in V ) . ##EQU00002##
[0076] Panel 630 shows the resulting frequency spectrum, where
alternate-beat fluctuations result in a peak at a frequency of
half-the-heart-beat. The inventor has further noted that the above
described action potential fluctuations correlate strongly with
T-wave alternans on the surface ECG. As stated above, this patient
developed serious ventricular arrhythmias some months later. Data
from Narayan and Smith (Narayan and Smith 2000c) and Walker and
Rosenbaum (Walker, Wan et al. 2003) provide strong evidence that
such fluctuations represent calcium fluctuations.
[0077] FIGS. 7A-7C illustrate fluctuations in human atrial signals
(action potentials) in a human subject who was largely without
symptoms but who subsequently developed atrial fibrillation. With
this subject, oscillations in atrial signals are seen which
increase with progressively faster pacing prior to the onset of AF.
Panel 710 of FIG. 7A shows atrial action potentials during slow
pacing. Panel 720 of FIG. 7A shows the aligned (red/blue) beats
superimposed as described above. Minimal fluctuations are visible,
although spectral analysis (FIG. 7A, panel 730) reveals small
fluctuations (in this case, alternating fluctuations).
[0078] FIG. 7B illustrates signals from the same subject at faster
heart rates. At faster rates (panel 740), fluctuations are visible
(indicated as Short (S), Long (L), but also seen in dome
amplitude). In panel 750, aligned beats are separated into red and
blue groups, and spectral analysis (FIG. 7B, panel 760) reveals
marked oscillations.
[0079] As shown in FIG. 7C, fluctuations can be dramatic prior to
AF onset (panel 770). Panel 780 shows that this patient had APD
restitution slope >1 (although this is not seen in all
patients). Notably, some patients may have electrical fluctuations
at slow rates, which may represent cellular calcium
abnormalities.
[0080] The relationship of disease risk to rate-response
(restitution) is complex. In the absence of significant structural
disease, a restitution slope >1 may cause signal oscillations
and predict/cause disease. However, in the presence of cellular
and/or structural disease, mechanisms such as conduction slowing
are involved in the initiation and maintenance of disease and add
complexity to the relationship with restitution slope. These
factors are summarized below in a risk score table for atrial
fibrillation; similar risk score tables can be constructed for
ventricular rhythm disorders with analogous elements.
TABLE-US-00001 RISK SCORE TABLE FOR ATRIAL FIBRILLATION Structural
APD Rest Fluctuation Disease? Slope (Alternans) CV Slowing?
Diagnosis Risk No <1 No Only for v. Minimal atrial Low early
beats disease No <1 Yes (at fast Only for v. Minimal atrial Low
rates only) early beats disease No >1 Yes (at fast Only for v.
Paroxysmal AF Medium. AF at rates) early beats (early disease) fast
rates Yes <1 No Yes Consistent with Low to medium. Aging Yes
<1 Yes (at slow Yes Persistent AF High or fast rates) Yes >1
Yes (at many No Persistent AF with High rates) high adrenergic
tone
[0081] The embodiments of risk score assessment described herein
differ from current methods. For example, the observation that
alternans of atrial intracardiac signals predict the onset of AF
(Narayan, Bode et al. 2002b) has only been shown in the right
atrium and only in patients with pre-existing atrial flutter whose
rhythms transitioned to AF. Systems and method described herein may
measure fluctuations which represent cellular abnormalities, and
may be detected at slow rates. These measured fluctuations may
better predict arrhythmia initiation than shown in studies by
others (Narayan, Bode et al. Circulation 2002b).
[0082] These principles allow for an unparalleled method for
identifying AF risk. In some embodiments, these methods use human
electrical signals that directly measure cellular pathophysiology,
rather than associations (such as age, left atrial diameter and so
on (Chugh, Blackshear, J Am Coll Cardiol. 2001). As described, the
input signals may be monophasic action potentials, but can be
approximated from other clinical signals such as unipolar or
bipolar signals with sufficient contact pressure with the heart
chamber. In addition, signals can be derived from any clinical
electrode, catheter, or pacemaker lead.
[0083] The embodiments described herein are different from
frequency analysis of electrograms, such as shown in work by
Stambler et al. (Stambler and Ellenbogen 1996b). Those authors used
FFT to analyze frequency components constituting the entire
waveform. Conversely, embodiments of methods described herein use
FFT to document beat-to-beat variability on a second beat, third
beat (and so on) basis. Much of the current studies, such as U.S.
Pat. No. 6,064,906 issued to Langberg, and U.S. Pat. No. 6,178,347
issued to Olsson, also do not disclose use of FFT in this fashion.
Furthermore, the embodiments described herein may examine
beat-to-beat fluctuations and thus exclude the confounding effects
of sub-harmonic and harmonic frequencies.
[0084] Certain embodiments additionally provide a novel means of
detecting the basis for T-wave fluctuations, and more specifically
T-wave alternans (an ECG tool to predict lethal ventricular
arrhythmias described in U.S. Pat. No. 4,802,491 issued to Cohen
and U.S. Pat. No. 5,148,812 issued to Verrier). The presence of
T-wave alternans predicts the presence of VT substrates in several
studies by Gold et al. (Gold, Bloomfield et al. 2000a), a review by
Narayan et al and others (Narayan 2006a). T-wave alternans may
reflect signal fluctuations from within the heart, which may also
reflect calcium oscillations. Thus, detecting such fluctuations
such as described above with respect to certain embodiments, likely
provides a more robust method of predicting future arrhythmias than
T-wave alternans.
[0085] The above Figures illustrate biosignal analysis for early
detection of potential arrhythmias. Another aspect of the invention
focuses on stabilization of abnormal measured cell regulation
detected by cardiac fluctuations and/or large fluctuations in the
biological signal such as changes in rate. In these embodiments,
slower pacing rates or rhythm perturbations may be used which may
help regain equilibrium in cellular handling of calcium and/or
other biochemicals. This is illustrated in FIGS. 8 and 9.
[0086] FIG. 8 illustrates how a metabolic process may fluctuate
because the rate is too fast to allow equilibrium to develop. Using
concepts described with respect to FIG. 8, embodiments of methods
of providing therapy to patients are described. In some
embodiments, the metabolic process may be uptake of calcium content
into the sarcoplasmic reticulum of a heart cell. Treatment begins
by slowing the heart rate to allow the metabolic process (such as
calcium) to replenish its stores. Faster activations can then be
delivered to approximate the desired average heart rate, but for a
short time to prevent re-emergence of oscillations. The process can
then be repeated.
[0087] The graph in FIG. 8 shows the dynamics of an example of an
intracellular process. The cellular process shown in FIG. 8 is
regulation of calcium content in the sarcoplasmic reticulum (SR) of
a heart cell. During a normal heart beat, calcium is released from
the SR and then pumped back into the SR. As labeled, SR calcium
content should remain between the 2 top horizontal bars. Above this
range, SR calcium overload may cause early after depolarizations
(EADs), which may trigger beats and cause arrhythmias. Below this
range, low SR calcium may create fluctuations (with periodicities
ranging from every-other-beat to every third beat or other) and
also cause arrhythmias.
[0088] At the left of the graph (label 810), rapid rate pacing
(narrow spacing between arrows) has depleted SR calcium so that SR
calcium fluctuations are seen. In certain embodiments, therapy may
now be initiated. This may assist in replenishing SR calcium, and
thereby prevent oscillations and the effects of cellular
derangements. Interval 820 of the graph illustrates slow pacing
(wider spacing between arrows), which causes SR calcium to rise
rapidly to the normal range. Preferably, pacing is not too slow, as
this may cause calcium overload (that is, above the top line) and
initiate arrhythmias. Pacing may then be applied more rapidly as
shown in interval 830 of the graph to achieve the desired heart
rate. If this is continued only for a few beats, SR calcium is not
depleted and fluctuations do not re-occur. The cycle may then be
repeated to approximate the previous heart rate but without
fluctuations in calcium
[0089] FIG. 9 illustrates embodiments of major electrical (pacing)
modes of treatment. These embodiments may be used to attenuate
oscillations by preventing cellular oscillations. Certain
embodiments may also be used to attenuate large fluctuations in
response to rate (restitution) and thus attenuate signal
fluctuations that indicate cardiomyopathy and propensity for rhythm
disorders.
[0090] Panel 910 shows a steep APD restitution relationship, which,
as described above, may be linked with arrhythmias in the atrium
(AF) or in the ventricle (ventricular tachycardia or ventricular
fibrillation). If a tissue has this steep APD restitution
relationship, then a premature beat (labeled X) that falls where
APD slope >1 can cause wavebreak and fibrillation. This is also
illustrated in FIG. 2C.
[0091] In certain embodiments, pacing modes to alter biosignal
rate-response (such as APD restitution) that may be used include
faster or slower pacing to alter APD rate-behavior (restitution) to
prevent beat X from initiating fibrillation. For example, in panel
920, point A indicates APD of a baseline beat. Pacing at a slightly
faster rate is applied to move point A to point B, with a slightly
shorter APD. However, continued pacing at this faster rate moves
the heart onto a different rate-behavior (restitution) curve, that
is lower and left-shifted (e.g. APD shortens, and the slope is
steep only for very short diastolic intervals). In panel 920, this
is indicated by shifting from point B to point C. Now, the same
premature beat X no longer falls on the steep portion of the
rate-behavior (restitution) curve, and is less likely to induce
wavebreak and fibrillation (Weiss, Karma et al. 2006). Conversely,
in some individuals (particularly with more severe;
cardiomyopathy), slower activation rates may flatten the
rate-response (restitution) curve. Accordingly, certain embodiments
allow this abnormality to be tracked so that therapy and heart rate
can be tailored accordingly.
[0092] In certain embodiments, some drug treatments may be used to
flatten or steepen APD restitution, and thus alter the risk for
action potential fluctuations. For instance, administration of an
agent to mimic adrenaline (e.g. isoproterenol) may result in
similar steepening of APD restitution as discussed above, which is
likely due to calcium accumulation in heart cells. Other drug
treatments may be beneficial, such as beta-receptor-antagonists. In
some embodiments, pacing stimulation of autonomic nerves may be
performed. Such nerves lie in the inferior vena cava, superior vena
cava and widely elsewhere within the body (Schauerte, Scherlag et
al. 2000a). They may be accessible by pacing in the neck and other
regions. In addition, it is possible to alter activation in these
nerves by behaviors such as swallowing, activation of the
gastrointestinal tract, or coughing. Further, in certain
embodiments pacing in complex irregular intervals may flatten
rate-behavior (restitution), as seen from a close inspection of
data by Kalb et al. (Kalb, Dobrovolny et al. 2004). Beta-blockers
and other drugs may also be used to flatten rate-behavior
(restitution). Use of such drugs has been shown to work in dogs by
Hao et al. (Hao, Christini et al. 2004), but no description of
human rate-behavior (restitution) has been shown. Improving heart
failure status may also make rate-behavior (restitution) curves
less steep.
[0093] Notably, certain embodiments of described methods measure
and indicate to the healthcare provider or the patient whether
rate-response is favorably or unfavorably altered by each of these
interventions. The measurements and interventions can be repeated
until the desired result is achieved.
[0094] FIG. 10 depicts beat-to-beat biosignal fluctuations and
therapy therefor. Panel 1020 shows fluctuations in biosignals (e.g.
APD, unipolar electrogram activation-recovery-intervals) on
consecutive beats. In this example, these fluctuations occur
every-other beat ("alternans"), although it should be noted that
the same principles apply to other beat oscillations (e.g. every
3.sup.rd beat or every 4.sup.th beat). Certain embodiments may
disrupt this fluctuation by pacing out-of-phase to the initial
fluctuations (arrows). That is, if the original fluctuation is
long-short, then short-long pacing may be applied. In certain
embodiments, the optimal strategy often does not need to pace every
beat. For example, in panel 1030, pacing beat 3 shortens the
biosignal (e.g. APD), and the oscillation has already broken down
by beat 4. Pacing on beat 5 again prevents lengthening of the
biosignal.
[0095] Panel 1040 illustrates biosignal oscillations every 3.sup.rd
beat, but applies to any odd-beat oscillations. Panel 1050 shows
the effect of out-of-phase pacing (arrows) that disrupts the
original fluctuating pattern. Pacing is generally applied to
stimulate the heart before the next natural beat is anticipated.
This enables pacing to control the pattern and rate of the heart
rhythm. The timing of the next paced beat is computed from the
biosignal restitution curves (see above), to prevent further
oscillations and disrupt present oscillations (examples given in
FIGS. 8 and 9).
[0096] In addition to therapy with pacing strategies, therapy by
altering structure or function can be performed. This may involve
ablation of tissue at sites where rate-response is steep, at sites
where signal fluctuations arise, or sites where nerves may
influence cardiac function.
[0097] FIGS. 11, 12A and 12B illustrate embodiments of a system and
method for analyzing biopotentials to develop a disease index and
provide appropriate therapy in humans. In certain embodiments, the
disease index can be calculated continuously during the day, or at
specific times, including after treatment. FIGS. 11, 12A and 12B
illustrate a variety of system components and method steps for
convenience of discussion and explanation. It will be appreciated
that embodiments of the invention need not incorporate all of the
components or steps described with reference to these figures.
Fundamentally, the various components and steps of the systems and
methods described below can be used implement the sensing,
analysis, and treatment modalities described above in a clinical or
research setting.
[0098] Referring first to FIG. 11, in some embodiments of system
1105, electrical events in the heart 1110 are recorded with a
combination of sensing electrodes. These electrodes may be
electrode catheters 1120 placed within the chambers or vasculature
of the heart. The electrodes may include leads from an implanted
pacemaker or cardioverter-defibrillator, which may be placed via
the superior vena cava or coronary sinus 1121. Electrodes can be
located in proximity to the nerves 1115 supplying the heart, which
are also located in the left atrium and ventricles (Scherlag,
Nakagawa et al. 2005). In some embodiments of system 1105
electrodes can also be virtual (computed) electrodes from a
computerized mapping system, ECG electrodes 1130, and/or stored
electrograms of any database 1165. In certain embodiments an
ablation catheter 1125 placed within the heart or its vasculature
can be used to modify/destroy regions where signals indicate a high
disease index. The ablation catheter 1125 may interface with an
ablation energy generator 1160. The other electrodes may interface
with an electrode controller 1140 and a pacing module 1150, all of
which may communicate with a process controller 1170. Ablation or
pacing electrodes can be directed to the locations of nerves 1115
supplying the heart.
[0099] The process controller 1170 may comprise various modules.
These modules may include a sampling module 1135 to record the
signal at various heart rates and a pacing module 1145 to provide
additional heart rates for sampling the biosignal. As shown, Module
1175 is part I of an Analytic Engine and may compute a disease
index based on the change of the biosignal with rate. Module 1180
is part II of the Analytic Engine and may measure the rate-response
of the biometric signal and determines whether it is fluctuating.
Module 1185 is the therapy module I and may interface with the
pacing module to deliver therapy to disrupt biosignal fluctuations.
Module 1190 is the therapy module II and may interface with an
energy generator to modify structure via ablation (destruction) of
tissue at sites where rate-response is steep, at sites where signal
fluctuations arise, or sites where nerves 1115 may influence
cardiac function. Module 1195 may comprise a display device and may
provide an interactive display of the progress of the computation.
The output of the system may be shown on module 1195 and may
include an assessment of cardiac health (no fluctuations and
absence of large signal variations with rate) or risk (the
opposite). The output may also indicate ideal modes of pacing
therapy and of potential ablation therapy. The modules may be
multifunctional; for example, the process controller may also
sample signals from other sources, such as cardiac motion from
tissue Doppler imaging.
[0100] Some embodiments of system 1105 include an optimal sequence
of actions and modes to provide complete and efficient diagnosis
and therapy in a semi-automated fashion. These embodiments may
include one or more functional modes:
[0101] 1. Sampling the biosignal;
[0102] 2. Analytic Engine I: determining risk score from the
rate-response relationship of biosignal components;
[0103] 3. Analytic Engine II: determining risk score from
fluctuations in biosignal components;
[0104] 4. Therapy module I: disrupt fluctuations by pacing; and
[0105] 5. Therapy module II: disrupt abnormal biosignal
rate-response or fluctuations by tissue modification (e.g.
ablation).
[0106] In certain embodiments of uses of system 1105 involving the
heart, the signal sampling mode uses one or more of ECG electrodes
1130 connected to the body surface, electrode catheters 1120, 1122
in the heart 1110, an electrode 1155 in the esophagus, and/or
virtual (computed) electrograms from a mapping system. In certain
embodiments these electrodes remain stationary relative to the
heart while sampling the cardiac event under investigation. In an
alternative embodiment, signals can be sampled retrospectively by
uploading previously-stored electrograms from the database 1165 to
the processor controller 1170. Alternatively, signals can be
reconstructed from cardiac motion (such as tissue Doppler imaging),
respiratory motion (such as on a respirator in the intensive care
unit), measured pulse oximetry, and/or other biometric signals.
[0107] In certain embodiments, the process controller 1170 directs
the pacing module 1150 to stimulate the heart using electrodes
1120, 1121, 1122, 1125 in the heart 1110, electrodes 1130 on the
body surface, and/or electrodes elsewhere such as in the esophagus
(electrode 1155). The electrode controller 1140 may receive signals
from the electrodes before, during and after pacing, and uses this
information to increase the range of heart rates available for
signal sampling.
[0108] Some embodiments of system 1105 monitor that adequate
contact is maintained between the electrodes and the relevant
tissue. For example, contact is monitored as a physician moves an
electrode catheter or rotates, curves, or straightens the catheter
in a region of the heart. In certain embodiments the degree of
contact can be monitored by the process controller 1170 in various
ways. For example, the process controller 1170 can ascertain
contact by measuring variations in the amplitude of sensed signals.
In another embodiment, the process controller 1170 can control the
pacing module 1150 to emit pacing signals through other electrodes,
and use the amplitude of detected pacing signals to ascertain
contact. In yet another embodiment, the processing module 1170 can
also determine contact by measuring tissue impedance.
[0109] The wider the range of rates, at which each signal is
sampled, particularly at faster rates, the more accurately the
rate-behavior curve may be constructed for each signal by the
Analytic Engine I. By way of example, if the signal is ventricular
it is first measured at resting rates (typically 60-80 beats/min).
Certain embodiments implemented in an ambulatory device, may
continue storing data to record at rates >100 beats/min (such as
exercise and stress) and <60 beats/min (such as rest and sleep).
Such behaviors may exacerbate rhythm disorders such as AF in some
patients. If pacing/stimulating the heart is an option, some
embodiments of system 1105 may increase heart rate by pacing to
further expand the range of rates over which the biosignal is
measured. Different pacing sequences may also be examined. As
described below, certain embodiments of system 1105 may empirically
determine the activation sequence needed to eliminate fluctuations.
Accordingly, therapy can be tailored to an individual person's rate
dynamics.
[0110] In the ventricle certain embodiments of system 1105 may be
used to:
[0111] 1. detect fluctuations in electrical signals;
[0112] 2. detect fluctuations in mechanical signals (from
echocardiography, magnetic resonance imaging, non-contact mapping
or another modality);
[0113] 3. compute rate-behavior of signal components, and detect a
steep relationship (marked variation in response to rate) or a
shallow relationship (minimal variation in response to rate);
[0114] 4. quantify conductions within regions of the heart, and
detect conduction slowing for early or late beats;
[0115] 5. quantify preserved or attenuated sympathovagal
activation; and/or
[0116] 6. compute an index of metabolic balance ("health") from
said signal fluctuations in living patients.
[0117] The concept that fluctuations in clinically detectable
variables indicate lack of equilibrium in cellular mechanisms for
disease is novel. For instance, mechanical fluctuations (on
echocardiography or blood pressure) indicate that calcium
homeostasis may be unable to reach equilibrium under current
conditions. This may involve cytosolic calcium, sarcoplasmic
reticulum calcium, release/reuptake dynamics or inter-related
systems. Electrical fluctuations in the action potential amplitude
(or a surrogate) may also indicate dysregulation of calcium, of
late sodium inactivation, or of transient outward current.
Electrical fluctuations in action potential duration may indicate
dysregulation of potassium or mechanisms related to calcium.
Several other fluctuations may be detected, and this list is not
intended to be exhaustive.
[0118] Fluctuations in the action potential amplitude may occur at
slow rates, likely indicating calcium oscillations, which may
predict ventricular arrhythmias and occur in patients with
abnormalities of heart pump function. Fluctuations in the rate and
shape of the action potentials may thus indicate a wide variety of
cellular abnormalities. Therapy can thus be tailored to an
individual's heart signal fluctuations and calibrated to the
disease process and other co-morbidities in that person. Certain
embodiments may track whether these and other therapies (for
example, biventricular pacing) attenuate these markers.
[0119] In the atrium, certain embodiments of system 1105 may be
used to:
[0120] 1. detect fluctuations in electrical signals;
[0121] 2. detect fluctuations in mechanical signals (e.g., from
echocardiography, non-contact mapping or another modality);
[0122] 3. compute rate-behavior of signal components, and detect a
steep relationship (marked variation in response to rate) or a
shallow relationship (minimal variation in response to rate);
[0123] 4. quantify conduction within regions of the heart, and
detect conduction slowing for early or late beats;
[0124] 5. quantify preserved or attenuated sympathovagal
activation, including signal components reflecting ganglionic
plexus innervation of the atria;
[0125] 6. compute an index of metabolic balance ("health") from
said signal fluctuations in living patients.
[0126] Again, the concept that fluctuations in a clinical variable
can indicate lack of equilibrium in cellular mechanisms is novel.
As in the ventricle, fluctuations in atrial action potential
amplitude may occur at slow rates, again suggesting calcium
oscillations, which may predict AF. These fluctuations are more
likely to occur in patients with atrial abnormalities, and may
represent an important component of the AF substrate. Fluctuations
in the rate and shape of the action potentials may thus indicate a
wide variety of cellular abnormalities. Therapy can thus be
tailored to an individual's heart signal fluctuations and
calibrated to the disease process and other co-morbidities. Certain
embodiment may thus track whether these or other therapies (for
example, ablation, certain drugs) attenuate these markers.
[0127] As discussed, certain embodiments of system 1105 can thus
detect potentially detrimental effects of other treatments or
activities. For example, right ventricular pacing may reduce left
ventricular systolic function (David 2002). However, it is
difficult to predict individuals who will suffer this effect.
Certain embodiments of system 1105 can identify such patients from
fluctuations in human biosignals (action potentials, unipolar or
bipolar electrograms from a device lead, or T-wave from the ECG).
Autonomic innervation may also be tracked through its effects on
APD rate-behavior. Thus, sympathovagal stimulation that favors AF
may make APD rate-behavior steeper, which may be tracked in certain
embodiments. Worsening heart failure may lead to calcium overload
and other failed regulatory systems, causing signal fluctuations
that may be detected in certain embodiments. Pro-arrhythmia from
anti-arrhythmic and other medications (e.g. erythromycin) may be
detected in certain embodiments. Ischemia, both subclinical and
clinically evident, may be detectable using ventricular signal
fluctuations in certain embodiments. Inotropic therapy with
dobutamine or milrinone can produce arrhythmias. This may be
mediated by cellular changes that can be tracked in certain
embodiments.
[0128] Certain embodiments of system 1105 can also detect potential
therapeutic benefits. For example, biventricular pacing may improve
heart failure (Bristow, Saxon et al. 2004). An early sign of
improvement is reduced biosignal fluctuations, such as electrical
indices of action potentials (electrograms from implanted devices,
or monophasic action potentials) reflecting calcium overload, which
may be tracked in certain embodiments.
[0129] Further, autonomic effects can be tracked through effects on
APD rate-behavior (restitution). Improved sympathovagal `balance`
that protects against atrial and ventricular arrhythmias may
attenuate rate-response (making restitution more shallow), that can
be tracked in certain embodiments. In addition, Beta-blocker and
other neuro-hormonal therapy may produce less marked rate behavior
of electrical signals (restitution) and attenuate biosignal
fluctuations which may be traced in certain embodiments.
[0130] FIGS. 12A and 12B illustrate an embodiment of a method of
determining a risk level of a patient for developing heart
instability using certain embodiments of system 1105. In certain
embodiments, the method of FIGS. 12A and 12B analyzes electrical
events in the heart via electrodes as described with reference to
FIGS. 1A-1C. In FIG. 12A, at step 1200, a biosignal may be detected
at the electrodes described reference to FIGS. 1A-1C.
[0131] This signal could come from a wide variety of sources. In
one embodiment, at a step 1205 the signal type of the detected
biosignal may be identified from a lookup table for a multi-input
system. In other embodiments the user may manually input the signal
type being monitored/analyzed. The signal type identification also
determines if the signal arises from the heart, brain, respiratory
system, gastrointestinal tract, urogenital system, or some other
source. If heart-related, the signal can be identified as a surface
ECG, intracardiac, echocardiographic or other. If intracardiac, the
signal can be identified as an action potential (monophasic action
potential or approximated signal derived from contact pressure from
a bipolar electrode with wide bandpass filtering), bipolar
electrogram, unipolar electrogram, or some other appropriate signal
type. The lookup table may also indicate that the identified
biosignal is of a signal type made up of one or more components. In
certain embodiments, the lookup table can be a comprehensive
biosignal inventory, with data on the distinct components of each
biosignal type and the physiological significance of each
component. Each component of a biosignal may vary independently
with rate and may fluctuate between beats. Each signal component
may reflect a distinct aspect of normal or abnormal physiology and
thus reflect "high risk". Below are examples of signal types and
components that may be included in the lookup table. However, the
lookup table is not limited to the examples below and may include
signal types and components for other heart-related signals,
signals involving other muscles (e.g. skeletal muscle, bladder and
gastrointestinal tract), signal involving the brain, and/or signals
involving the nervous system.
[0132] The signal may be an ECG with atrial components (P-wave, PR
interval) and ventricular components (QRST waves). In certain
embodiments described herein, for the atrium, it may be determined
how P-wave duration varies with rate as a measure of atrial
conduction slowing. For the ventricle it may be determined how the
QT interval varies with rate as a measure of ventricular APD
rate-behavior (restitution).
[0133] As described in detail above, the signal may be human action
potentials in the atrium and/or ventricle. The signals may be
unipolar electrograms from the human atrium and ventricle. Indeed,
such signals convey many of the same information as the monophasic
action potential. Moreover, with sufficient contact pressure and
wide filter settings (for instance, 0.01 to 500 Hz), a traditional
electrode can record a signal very similar to a monophasic action
potential. The signals may also be traditional bipolar electrograms
from the human atrium and ventricle. Certain embodiments of system
5 may determine rate response and fluctuations in each
component.
[0134] Other signals that can be handled by certain embodiments of
system 5 include electrical signals indicating brain wave activity,
respiratory activity, and gastrointestinal activity, and signals
measured from autonomic ganglia as sympathetic nerve activity.
[0135] Between steps 1210 and 1240, the components of the signal
may be parsed (processed stepwise). For a monophasic action
potential (MAP), the parsed components may include phase 0
(upstroke), phase I, phase II, phase III and phase IV. For an ECG,
the parsed components may include the PR, QRS, QTU, ST, JT and JTU
intervals. For example, the ECG signal may be separated into atrial
components (the P wave and PR interval), ventricular depolarization
(the QRS complex), and ventricular repolarization (the T wave). QRS
complexes can be identified using methods discussed by Watanabe et
al. (Watanabe, Bhargava et al. 1980) and U.S. Pat. No. 4,552,154
issued to Hartlaub, and U.S. Pat. No. 6,035,231 issued to Sommo.
Individual QRS complexes are then aligned using one of several
columnar techniques. Examples include methods that align
electrograms about the point of largest positive or negative slope,
methods that align electrograms about their peak values, methods
that minimize the mean square differences of the electrograms, and
methods based on metrics based on derived signals. T-waves may also
be identified and aligned similarly. Atrial activity may be
considered to lie in the intervening intervals.
[0136] If the signal is an action potential (FIG. 1), phases 0, 1,
2 and 3 may be separated. Components of interest of an action
potential include depolarization (phase 0), repolarization (phases
1-3), phase II amplitude and action potential duration (time
interval from phase 0 to phase 3). If the signal is a unipolar
electrogram, it may be segregated into depolarization and
repolarization phases. Each may be analyzed for the waveform shape
as well as duration. If the signal is a bipolar electrogram, it may
be segregated into depolarization and repolarization phases. Each
may be analyzed for the waveform shape as well as duration.
[0137] At step 1210, the next signal component of the signal is
selected. Continuing at step 1215 the rate response of the selected
component is measured at one or more rates and using one or more
pacing sequences. Rate response may be determined for a wide range
of observed heart rates. If available, pacing may also be used to
increase the heart rate to provide a wider range of rates at which
the component signal response is measured to comprehensively assess
rate response.
[0138] Certain embodiments may also use an adaptive series of
programmed sequences to attenuate fluctuations. Such sequences may
include a series of slow then fast beats tailored to the specific
non-linear processes under consideration. For example, suppose a
heart rate of 100 beats/min is desired (cycle length 600 ms), but
fluctuations arise above 90 beats/min (cycle length shorter than
667 ms), suggesting derangements in calcium homeostasis. A sequence
of 700 ms-700 ms-700 ms-700 ms-400 ms-400 ms provides the desired
average rate, but the first 4 beats should prevent fluctuations (by
allowing replenishment of cellular calcium stores). If stores are
sufficiently replenished, the next two fast cycles (#5-#6) now may
not give risk to fluctuations. The sequence can then repeat. A
variety of such sequences may be tested in step 1215, calibrated to
whether fluctuations arise or are attenuated. These tests may then
be used to tailor therapy in later stages of certain embodiments of
the described method.
[0139] At step 1220 the rate-response ("restitution") curve may be
constructed for the selected component. Depending on the type of
signal component, different calculations may be done to construct
the restitution curve. Variations in monophasic action potential
duration (time from phase 0 to the phase III terminus) with rate
may be calculated at step 1220. This is known as APD restitution
(Franz, Swerdlow et al. 1988a). Restitution for additional
components may also be calculated. For instance, the rate response
(restitution) for phase 0 upstroke velocity may be calculated as it
is a measure of sodium channel fluctuations with rate (and resting
membrane potential), which may influence conduction slowing. Rate
response of the amplitude of phase II of the action potential (or
of a unipolar electrogram) may be calculated as it is a surrogate
index of calcium, as mentioned above. If at step 1220 it is
determined the slope of the calculated restitution curve is greater
than a threshold value, the process continues to a step 1225 where
a risk score is incremented. For example, at step 1225 high risk
may be assigned based on predefined properties of the biosignal
rate-behavior (restitution) and the signal type. For monophasic
action potential duration (MAPD), risk may be ranked higher at step
1225 if it is determined the rate-behavior (restitution) maximum
slope >1 at step 1220 (Weiss, Karma et al. 2006). The risk score
is described with greater detail below. The process then continues
to step 1230. If at step 1220 it is determined that the slope of
the restitution curve is not greater than the threshold value, the
process proceeds to step 1230.
[0140] At step 1230, it is determined whether the signal component
fluctuates. For example, the biosignal may fluctuate at a native
(baseline) heart rate. If it is determined the signal fluctuates,
the process proceeds to step 1235, where the risk score is
incremented. For example, at step 1235 a risk score of "Imminent
risk" may be assigned if the biosignal fluctuates at native
(baseline) heart rate. High risk may also be assigned at step 1235
if the signal fluctuates at the current heart rate. The process may
then continue to step 1240. If at step 1230 it is determined the
signal does not fluctuate, the process continues to step 1240. At
step 1240 it is determined if the selected signal component is the
last signal component of the biosignal (i.e., all the signal
components have been measured). If it is determined the selected
component is not the last signal component, the process returns to
step 1210 where a signal component that has not previously been
selected is selected to be measured. If it is determined the signal
component is the last signal component, the process proceeds to
step 1243 illustrated in FIG. 12B.
[0141] Steps 1243 to 1275 may track cardiac status during therapy
by measuring each component of the biosignal. Signal analysis and
risk assignment may be performed before intervention at steps
1210-1240 and may be repeated after an intervention at steps
1243-1275. As before, each signal component may be processed for
rate response.
[0142] At step 1243, a signal component of the biosignal is
selected to be measured. Continuing at step 1245, biosignal
variations may be assessed for all heart rates observed and, if
available, by pacing at faster heart rates and at various sequences
as described above. The effect of the intervention may then be
determined. Further, at step 1255 it is determined if rate response
(restitution) is steeper than it was before the intervention. If it
is determined that the slope is greater than before intervention,
the process may proceed to a step 1260, where the risk score is
flagged as "increased risk" and/or the intervention is flagged as
"detrimental intervention. The process then proceeds to step 1265.
If at step 1255 it is determined the restitution is not steeper
than previously measured, the intervention may not be detrimental
and the process proceeds to step 1265.
[0143] At step 1265 it is determined if the biosignal fluctuates
more than before treatment or fluctuates when previously it did
not. If at step 1265 it is determined that the signal fluctuation
has increased, the process proceeds to a step 1270 where the risk
score is flagged as "increased risk" and/or the intervention is
flagged as "detrimental intervention." The process then proceeds to
step 1275. If at step 1265 it is determined the signal fluctuation
has not increased, the intervention may not be detrimental and the
process proceeds to step 1275. At step 1275 it is determined if the
selected signal component is the last signal component of the
biosignal (i.e., all the signal components have been measured). If
it is determined the selected component is not the last signal
component, the process returns to step 1243 where a signal
component that has not previously been selected is selected to be
measured. If it is determined the signal component is the last
signal component, the process ends.
[0144] In some embodiments, ECG and electrogram data may be
uploaded from a database 160 for analysis in an analogous fashion
to the described real-time mode of operation. Data from the
database can be from the same or different patients, recorded at
any time and using any acquisition system.
[0145] Analytic Engine I 1175 in certain embodiments may be
implemented in software. Certain embodiments of Analytic Engine I
1175 operate quickly and are suitable for real-time as well as
off-line analysis.
[0146] In certain embodiments, steps 1220-1225 of the process of
FIG. 12A may be carried out by Analytic Engine I 1175. For example,
Analytic Engine I 1175 may determine the rate-response
(restitution) of each component.
[0147] Certain embodiments of the risk scoring system may be based
on APD rate-behavior (restitution), alternans, and/or conduction
rate-behavior (restitution) (Analytic Engine I). Certain
embodiments of system 1105 may initially use a "default" mode of
risk scoring. Some of these embodiments may also have an adaptive
design that may tailor both risk assignment and therapy to observed
fluctuations and rate response in the individual.
[0148] In certain embodiments of system 1105, steps 1230-1235 may
be carried out by the Analytic Engine II 1180. Analytic Engine II
1180 may determine whether biosignals or their components
fluctuate.
[0149] Elimination of signal fluctuations using certain embodiments
of system 1105 may prevent AF onset. It has been found in some
cases that rapid atrial pacing did not cause AF. Notably, in some
of these cases, conduction slowing arose near the pacing site at
very fast rates. Patients with less significant disease may show
conduction slowing only at very fast rates, while more significant
disease may produce more complex effects. In some cases, signal
fluctuations are present (at slow rates), yet are attenuated by
conduction slowing, which causes local capture block and thus
prevents AF.
[0150] As discussed above, signal fluctuations indicate disease
risk. Fluctuations at fast rates may occur in persons with
low/intermediate risk (and minimal/intermediate cellular or
structural disease). Conversely, fluctuations at slow rates may be
seen in patients with substantial disease and greater substrate.
The data presented shows this for the ventricle and atrium.
[0151] Embodiments of methods that may be performed using
embodiments of system 1105 include pacing methods to modulate the
shape of the rate-behavior (restitution) curve of electrical
signals, or to disrupt fluctuations in the biosignal, and thus
normalize abnormal calcium handling and prevent progression of
contractile dysfunction and/or arrhythmia.
[0152] In certain embodiments; the process controller 1170 controls
the pacing module 1150, to stimulate the heart using electrodes
1120, 1122 in the heart, electrodes 1130 on the body surface,
and/or electrodes elsewhere such as from the esophagus (electrodes
1155). The electrode controller 1140 receives signals from the
electrodes before, during and after pacing. Pacing may be used to
increase heart rate and introduce extra beats to alter biosignal
rate-response and disrupt biosignal fluctuations.
[0153] Pacing can be applied via any electrode. In certain
embodiments, pacing may be applied via implanted electrodes of a
pacemaker or implanted cardioverter-defibrillator in the outpatient
setting, or from an ablation catheter 1125 at electrophysiology
studies. Pacing techniques including burst pacing and multiple
extra stimuli can be used. The pacing stimulus may be monophasic,
biphasic, or triphasic.
[0154] Certain embodiments of system 1105 use pacing to modify the
rate-response (i.e. restitution) of the biosignal or to disrupt the
pattern of biosignal fluctuations. Flattening of rate-behavior
(restitution) or attenuation of fluctuations may reduce the
propensity to arrhythmias.
[0155] Certain embodiments of system 1105 may utilize an adaptive
pace sequence approach to eliminate signal fluctuations. Sequences
may be tailored to the individual patient, and may be modified at
different times. By monitoring electrical signal fluctuations in
real-time, certain embodiments of system 1105 can iteratively solve
for the optimum pace sequence. Iterations can commence from basic
"defaults" for each patient, established as part of Analytic Engine
II 1180.
[0156] Other cellular derangements can be addressed by tailored
pacing in this fashion, titrated to the relevant component of
electrical fluctuations. Such pacing may prevent the sequelae from
cellular derangements, including the onset of heart rhythm
disorders and progression of cardiomyopathy.
[0157] From a cell mechanism point of view, the methods that may be
implemented by system 1105 may be viewed as follows:
[0158] A. Design/quantify cycle periodicity
[0159] a. If varying on an even beat basis (every fourth or second
beat)--intervene on odd beats;
[0160] b. If varying on an odd beat basis (every third or fifth
beat)--intervene on even beats;
[0161] B. Determine Individualized Rate thresholds for different
Cellular Processes (e.g. heart) by progressively increasing rate
until:
[0162] a. AP Phase I oscillations occur signaling incomplete
recovery of I.sub.Na;
[0163] b. AP Phase II oscillations occur signaling abnormal calcium
handling, potentially abnormal I to kinetics, and/or abnormal late
sodium inactivation kinetics;
[0164] c. AP Phase III oscillations occur, which involves potassium
currents, such as I.sub.K, and is often dependent upon the APD
restitution. This operates at fast rates.
[0165] C. Calibrate against disease based on the measurable effects
of ion channels
[0166] a. In other tissues, such as the brain, the measurable
effects of other ion channels can be calibrated against disease.
For instance, variations in I.sub.Na, I.sub.K can be measured and
modeled against observed epileptiform activity.
[0167] b. In the atrium, P-P intervals may be varied to ameliorate
calcium overload on some cycles to reduce AP shape variations.
[0168] c. In the ventricle, the R-R intervals may be varied to
ameliorate calcium overload on some cycles to reduce AP shape
variations. PR intervals may also be varied, thus varying R-R
intervals to ameliorate cellular metabolic dynamics and AP shape
variations. This variation may support the beneficial effect of
certain irregular rhythms (e.g., bigeminy or trigeminy) which are
not pro-arrhythmic in atrium or ventricle.
[0169] Certain embodiments of system 1105 may use a clinical
measure to indicate cellular health. In one embodiment, calcium
fluctuations that indicate a cellular manifestation of
cardiomyopathy are determined. As shown above, fluctuations in the
action potential shape (particularly phase II) may indicate
oscillations in cellular calcium. Such oscillations, in turn, may
occur after calcium overload as described in cardiomyopathy. Thus,
certain embodiments of system 1105 may provide a clinical index
that probes cardiomyopathy from oscillations in cardiac signals
(particularly phase II of action potentials).
[0170] Certain embodiments of system 1105 may indicate to the
healthcare provider or patient whether fluctuations are favorably
or unfavorably altered by interventions. This may be repeated until
the fluctuations are attenuated or abolished.
[0171] Certain embodiments of system 1105 are also designed to
stimulate cardiac nerves to modulate their impact on heart
functioning. For instance, it is known that elevated sympathovagal
balance can precipitate AF in animals (Patterson, Po et al. 2005).
System 1105 may perform electrical or other energy source
stimulation of the atrium or ventricle to alter the biosignal
rate-behavior or the presence of biosignal fluctuations.
[0172] In some embodiments, Therapy Module II 1185 may operate
during invasive electrophysiology studies, and may modify tissue
structure where the biosignal rate-response is steep, or where
biosignal fluctuations occur. This may be achieved by ablating
tissue, by altering it via heating or cooling, and/or by using
electromagnetic fields.
[0173] In certain embodiments, the energy generator 1160 may be
activated to apply energy (radiofrequency, infrared, cryoablation,
microwave radiation, or other energy) via the ablation electrode
1125. The electrode 1125 can be moved within the heart manually by
an operator, or remotely using robotic or computer assisted
guidance. In certain embodiments, Therapy Module II 1185 may only
be actuated after pacing interventions have failed to attenuate the
steepness of rate-behavior (restitution).
[0174] In certain embodiments, after ablation, system 1105 remotely
moves the catheter 1125, or prompts the user to move the catheter
1125, to an adjacent location to assess its rate response. If
rate-behavior (restitution) is steep, or fluctuations are observed,
ablation may be repeated. This may result in elimination of tissue
where steep rate-behavior (restitution) or fluctuations are
observed (both indicating diseased tissue and/or cardiomyopathy,
see FIGS. 2A-4). In the certain embodiments, system 1105 can engage
remote catheter navigation to survey the entire atrium for such
fluctuations.
[0175] Certain embodiments of system 1105 may also be designed to
ablate cardiac nerves to modulate their impact on heart
functioning. Accordingly, certain embodiments of system 1105 may
use radiofrequency or other energy source to alter the biosignal
rate-behavior or the presence of biosignal fluctuations.
[0176] Permanent tissue modification may not be necessary in some
circumstances.
[0177] Accordingly, in certain embodiments, subthreshold pacing may
be applied, or an electromagnetic field may be used to modulate
tissue function for a desired period of time. The type of treatment
used may be modulated in real-time depending upon risk assessment
(from signal restitution and fluctuations).
[0178] The above described approach of introducing "planned" rhythm
irregularities is novel and appealing. Although such irregularities
may appear potentially detrimental, it should be noted that many
naturally observed "regular irregularities" are benign (i.e., not
dangerous), such as atrial or ventricular bigeminy (where
every-other-beat is faster and/or from a different source) and
normal fluctuations in heart rate ("sinus arrhythmia"). Similarly,
slow rates are central to beta-blocker therapy, which is widely
accepted to improve health at body, organ, and cellular levels.
[0179] Certain embodiments also predict which individuals will
develop AF and deliver therapy that carefully modulates atrial
function to reduce these fluctuations and prevent the development
of AF.
[0180] The embodiments above described in relation to heart-related
electrical signals. However, one of ordinary skill in the art will
recognize that similar embodiments may be used for analyzing other
electrical signals. Additional embodiments may be used for striated
muscle (peripheral muscle), smooth muscle in the gastrointestinal,
urogenital and respiratory systems, and in association with the
electroencephalogram or invasive surgical techniques such as open
heart or brain surgery.
[0181] One embodiment analyzes action potentials or surrogate
signals of activation and recovery from the beating heart. These
signals may be detected from an ECG, implanted pacing electrodes
(in the heart or other tissues via far-field detection), an
echocardiogram (that indicates activation and recovery), or other
sources. In certain embodiments, pacing and ablation electrodes can
be used within the beating heart and in nerves that supply the
heart. Other embodiments may use signals derived from heart
contraction, including reflected sound waves or electrical
impedance changes. Certain embodiments may analyze signals and
apply pacing to skeletal muscle in individuals with muscular
diseases. Some embodiments may analyze signals and apply treatment
to the gastrointestinal tract. Other embodiments may apply
treatment to the muscles of the respiratory system.
[0182] Certain embodiments calculate the changes in response to
rate (also known as "restitution") of electrical heart signals, and
fluctuations in said signals, to calculate a risk index for
abnormal rhythms such as atrial fibrillation (AF; in the top
chambers, or atria) or ventricular fibrillation (VF; in the bottom
chambers, or ventricles). As described below, steep slope of the
rate-behavior or fluctuations may reflect "a sick heart," or other
metabolic abnormalities that may indicate a predisposition to AF in
the atria or VT/VF in the ventricles. Certain embodiments can use
action potentials (such as monophasic action potentials or other
surrogates such as electrograms from implanted atrial leads) from
the human heart to construct this risk index. Some embodiments may
use unipolar or bipolar electrograms and/or signals representing
cardiac motion from detailed echocardiographic, CT, or magnetic
resonance imaging.
[0183] If fluctuations are observed, or expected from the
rate-response (restitution) curve, certain embodiments may apply
therapy to disrupt the pattern of fluctuations or modify
restitution. This therapy may attenuate cellular derangements in
cardiomyopathy and potentially reduce the risk of arrhythmias.
[0184] Certain embodiments can thus determine if cardiac disease
(cardiomyopathy) is progressing--due to native disease or as a
side-effect of potentially detrimental interventions such as right
ventricular pacing, or inotrope therapy (e.g. with dobutamine).
Certain embodiments may use ablation to modify tissue that exhibits
abnormal fluctuations. Therapy can also be applied to nerves that
regulate heart function and may cause rhythm disorders. Certain
embodiments can track such therapy. Certain embodiments may also
track progression of cardiac disease from biventricular pacing,
after ablation (for atrial fibrillation or ventricular
tachycardia), beta-blocker therapy, etc.
[0185] In one embodiment, stimulation of the top chamber of the
heart (atria) or bottom chamber (ventricle) can be performed. The
stimulation assists in cellular regulation of the targeted deranged
metabolic (or ionic) process. Accordingly, certain embodiments may
attenuate heart signal oscillations that occur due to abnormal
calcium homeostasis. For example, in certain embodiments, slow rate
pacing may first be applied to the heart to restore sarcoplasmic
reticulum calcium stores. The duration of slow pacing may be
titrated carefully, to avoid calcium overload that produces extra
beats (from after depolarizations) and triggers arrhythmias. After
this period of relative calcium loading, a period of faster pacing
may be used to achieve the desired average heart rate. Again, this
may be titrated carefully to avoid calcium depletion that may again
produce oscillations. The reason for this rate variation is that
calcium sequestration and release kinetics are non-linear. In
certain embodiments the impact of rate variations on action
potential duration (via restitution) is also considered.
[0186] In other embodiments, methods of treatment are performed
including increasing or reducing the activation rate, and directly
disrupting the regularity of oscillation. For example, if
fluctuations occur every third beat, in certain embodiments the
apparatus may alter activation of the atrium or ventricle every
fourth or second beat--out of phase with the native oscillation--to
disrupt the fluctuations.
[0187] In some embodiments, destruction (ablation) may be targeted
at tissue responsible for fluctuations or abnormal rate-behavior
(which leads to fluctuations) of biological signals. Ablation may
include, but is not limited to, conventional sources
(radiofrequency energy, cryoablation), and alternative sources,
including microwave, ultrasound and external beam irradiation.
[0188] In yet other embodiments, function of the tissue responsible
for the heart signal fluctuations may be altered using external
electromagnetic fields, pacing at a subthreshold intensity, and/or
other interventions.
[0189] Atrial cardiomyopathy (heart failure of the top chamber of
the heart) may be detected and tracked in certain embodiments.
Certain embodiments detect signatures of atrial cardiomyopathy.
Certain embodiments may provide methods for attenuating
derangements in the intracellular handling of calcium and other
ions due to cardiomyopathy. The attenuation reduces the severity of
atrial cardiomyopathy (by potentially reducing contractile
dysfunction) as well as "side-effects" such as heart rhythm
disorders (atrial fibrillation). Certain embodiments provide a
method for tracking atrial cardiomyopathy in humans. Certain
embodiments described herein calculate an index, in the intact
human heart, of specific abnormalities in atrial cell function
("cell health") that indicate atrial cardiomyopathy and risk for
side-effects including atrial fibrillation. This index can be used
to track whether these abnormalities improve or worsen with
therapy. The index can also be used to analyze atrial action
potentials, a surrogate signal from a catheter, signals from an
implanted atrial lead (from a pacemaker or defibrillator), signals
from echocardiography, and/or signals from the
electrocardiogram.
[0190] Certain embodiments described herein detect specific
abnormalities in atrial cell function ("cell health") that likely
cause heart rhythm disorders and which may represent early forms of
atrial cell disease (cardiomyopathy). These abnormalities may be
present at heart rates easily found during the activities of daily
living, yet become exaggerated just prior to AF. Thus, certain
embodiments can be used to continuously track the propensity for AF
during therapy and then deliver therapy if the propensity (signal
oscillations) is observed.
[0191] Certain embodiments described herein target specific
cellular abnormalities in regulation of atrial cell physiology,
which may be central to weakened atrial contraction and the
initiation of AF. Thus, ameliorating this disequilibrium may
improve atrial cardiomyopathy as well as risk for AF and other
sequelae. As a result, certain embodiments are configured to
improve and reverse abnormal features of atrial cardiomyopathy and
may use methods described herein to track whether the therapy is
working.
[0192] Certain embodiments herein introduce a paradigm shift in
assessing ventricular cardiomyopathy. Because they may assess,
detect, and act upon the biochemical balance of ventricular cells,
guided by homeostatic mechanisms that are very likely central both
to contractile (mechanical) and heart rhythm abnormalities in human
beings in real time, they can dynamically indicate cellular health
and disease risk and guide therapy to normalize these
abnormalities. In animal experiments, cellular calcium balance
provides a precise measurement of ventricular cell function and the
risk for VT/VF (Laurita and Rosenbaum 2008). Embodiments of methods
are described herein that significantly improve upon existing
methods for detecting risk for worsening ventricular cardiomyopathy
or risk for VT/VF. These embodiments can be used to analyze
ventricular action potentials, signals from a ventricular lead,
signals from echocardiography or signals from the
electrocardiogram.
[0193] Certain embodiments of methods may focus on oscillations in
action potential shape (see below, particularly phase II).
Examining action potential shape is a new field, since most or all
prior art examines action potential duration. Some methods
described herein may focus on fluctuations in action potential
shape, which may indicate cellular imbalances (disequilibrium) in
calcium and other metabolic processes, as changes in action
potential duration may not be sensitive to such fluctuations.
Certain embodiments of methods described herein measure slow rate
oscillations and provide potential therapy.
[0194] Embodiments described herein link the risk for VT/VF with
cellular abnormalities that are detectable from the beating heart
in individuals. Unlike prior methods, whose association with VT/VF
is indirect, certain embodiments described herein assess specific
abnormalities in ventricular cell function ("cell health") caused
by ventricular cardiomyopathy which explains why VT or VF initiates
at a cellular level. Some such embodiments may measure these
fluctuations in the action potentials of ventricular cells.
Furthermore, in certain embodiments these fluctuations may be
tracked to determine if therapy is effective.
[0195] Embodiments described herein may use novel pacing
strategies, and other techniques, to assist the heart in
normalizing cellular homeostasis (in calcium and other metabolic
processes). Treatment may be tailored to each patient. Certain
embodiments may be used to detect attenuation of fluctuations in
action potentials (or surrogate signals) providing a method to
determine if cardiac resynchronization therapy is improving heart
failure at the tissue and cellular level. Similarly, certain
embodiments can detect exaggerated fluctuations if heart failure is
worsening (such as from right ventricular pacing). In certain
embodiments, after treatment of these abnormalities, these
fluctuations can be continuously tracked as a marker of cellular
health.
[0196] The implementation of the systems and methods described
herein is based largely upon digital signal processing techniques.
However, it should be appreciated that a person of ordinary skill
in this technology area can easily adapt the digital techniques for
analog signal processing.
[0197] Those of skill will recognize that the various illustrative
logical blocks, modules, circuits, and algorithm steps described in
connection with the embodiments disclosed herein may be implemented
as electronic hardware, computer software, or combinations of both.
To clearly illustrate this interchangeability of hardware and
software, various illustrative components, blocks, modules,
circuits, and steps have been described above generally in terms of
their functionality. Whether such functionality is implemented as
hardware or software depends upon the particular application and
design constraints imposed on the overall system. Skilled artisans
may implement the described functionality in varying ways for each
particular application, but such implementation decisions should
not be interpreted as causing a departure from the scope of the
present invention.
[0198] The various illustrative logical blocks, modules, and
circuits described in connection with the embodiments disclosed
herein may be implemented or performed with a general purpose
processor, a digital signal processor (DSP), an application
specific integrated circuit (ASIC), a field programmable gate array
(FPGA) or other programmable logic device, discrete gate or
transistor logic, discrete hardware components, or any combination
thereof designed to perform the functions described herein. A
general purpose processor may be a microprocessor, but in the
alternative, the processor may be any conventional processor,
controller, microcontroller, or state machine. A processor may also
be implemented as a combination of computing devices, e.g., a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration.
[0199] The steps of a method or algorithm described in connection
with the embodiments disclosed herein may be embodied directly in
hardware, in a software module executed by a processor, or in a
combination of the two. A software module may reside in RAM memory,
flash memory, ROM memory, EPROM memory, EEPROM memory, registers,
hard disk, a removable disk, a CD-ROM, or any other form of storage
medium known in the art. An exemplary storage medium is coupled to
the processor such the processor can read information from, and
write information to, the storage medium. In the alternative, the
storage medium may be integral to the processor. The processor and
the storage medium may reside in an ASIC. The ASIC may reside in a
user terminal. In the alternative, the processor and the storage
medium may reside as discrete components in a user terminal.
[0200] While the above detailed description has shown, described,
and pointed out novel features of the invention as applied to
various embodiments, it will be understood that various omissions,
substitutions, and changes in the form and details of the device or
process illustrated may be made by those skilled in the art without
departing from the scope of the invention. As will be recognized,
the invention may be embodied within a form that does not provide
all of the features and benefits set forth herein, as some features
may be used or practiced separately from others. The scope of the
invention is indicated by the appended claims rather than by the
foregoing description. All changes which come within the meaning
and range of equivalency of the claims are to be embraced within
their scope.
* * * * *