Transcript Heart Rate Variability

Insights from complexity
science for the practice of
medicine
Robert A. Lindberg, MD
Darien, CT
Plexus Institute
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Complexity Science

Other labels used:
– Chaos Theory
– Nonlinear Dynamics
– Science of Complex Adaptive Systems
– Systems Theory

Deals with the behavior and properties of
systems
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System definition

A collection of agents interconnected
around a common purpose
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System examples

Weather system
 Phone system
 Internet
 Stock Market
 Central Nervous System
 Immune System
 Human Body
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Complex Dynamic System
Properties
Weather
 Agents obey Simple Rules
– Wind, water, thermodynamics, etc

Continual Dynamic Interplay between all
the interconnected agents
 Net consequence cannot be forecast nor
engineered
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Weather patterns
U
N
I
T
TIME
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Complex Adaptive System
Stock Market
 Agents follow simple rules
– e.g. buy low, sell high

Dynamic interplay between agents that have
the ability to learn and adapt
 Consequences cannot be forecast or
engineered
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Dow Jones Average
U
N
I
T
TIME
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Properties of Complex
Nonlinear Systems

Simple Rules underlie complexity of system
 “Nonlinear” or variable
 Emergent order or stability created by the
dynamic interactions between the agents of
the system
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Relevance of Complexity
Science to Medicine

Alternative model to the Mechanistic or
Reductionist Model
– Understand the whole by studying the parts
– The body is similar to a machine with independent parts

Concept of the human body as a complex adaptive
system
 Systems embedded within systems
 The sum is greater than the parts
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Human Body = Complex
Adaptive System

Comprised of many systems
– Central Nervous System
– Immune System
– Cardiovascular System
– G.I. System
– Etc.

Systems embedded within systems
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Human Body Interacting with
Larger Systems

Nature
 Ecosystems
 Solar Cycles
 Micro-organisms
 Families, Organizations
 System embedded within systems
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Complexity Determinants

Number of Interconnected Agents
and
 Number of Connections
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Signature of Complex System
behavior over time

Waves, Rhythms, Oscillations, 1/f Noise,
Chaotic Resonance, Nonlinear Dynamics,
etc.
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Thermostat – Closed System
T
E
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P
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Thermostat – Open System
T
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P
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Simple vs Complex Systems
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Pattern of a Simple System:
two agents, one connection
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Pattern of a complex system:
many agents, many
connections
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Diurnal Thermostat System
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Circadian Body Temperature
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Circadian Body Temperature
wave on a wave
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Waves vs Particles
Observing the pattern of a system’s “waves”
provides insight into it’s relative health and
degree of complexity
 Wave patterns suggest the number of agents
and the number of connections and their
relative responsiveness to each other

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Some examples of waves or
rhythms

Heart rate
 Brainwaves
 Temperature curve
 Action potential of nerves, muscles
 Blood pressure
 Hormonal pulses
 Circadian rhythm
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Heart Rate Variability (HRV)

An Independent Risk Factor for All Cause
Mortality
 Why?
– Represents a wave or rhythm indicative of the
degree of physiologic health of the human
system
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Normal Heart Rate Variability
Beats
per
minute
time
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Heart Rate Variability

The Heart Rate cycles in a Wave like
pattern over time
 A reflection of the behavior of the
Cardiovascular System interacting and
connected to many other agents
 Its pattern has prognostic implications
 A signature of complex systems behavior
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Abnormal Heart Rate
Variability
Beats
Per
minute
time
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Chronotropic Response
Beats
per
minute
with
exercise
time
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Usefulness of impaired
chronotropic response to
exercise as a predictor of
mortality

Chronotropic incompetence is a strong and
independent predictor of death, even after
accounting for angio severity of CAD
 384 pt’s for Thallium stress tests
 Dresing;Am J Cardiol 2000;86:602
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Prognostic implications of
chronotropic incompetence in the
Framingham Heart Study

An attenuated heart rate response to
exercise is predictive of increased mortality
and coronary heart disease incidence
 1575 males, mean age 43, prospective
 Lauer;Circulation.1996;93:1520
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Effects of exercise training on
chronotropic incompetence in
pt’s with heart failure

Exercise results in an increase in peak heart
rate and partial reversal of chronotropic
incompetence in patients with stable heart
failure
 Keteyian; Am Heart J. 1999;138:233
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Heart Rate Recovery
Beats
per
minute
time
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Heart-Rate Recovery
Immediately After Exercise as
a Predictor of Mortality

A delayed decrease in the heart rate during
the first minute after graded exercise…is a
powerful and independent predictor of the
risk of death
 Cole; NEJM 1999;341:1351-7
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Heart Rate Recovery after
Submaximal Exercise Testing
as a Predictor of Mortality

Healthy Cohorts, routine testing
 Heart rate recovery 2 minutes after ETT
 Reduced HR recovery a powerful
independent predictor of mortality in
healthy adults
 Cole; Annals of Int Med. 2000;132:552
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Heart rate variability
+
Chronotropic
response
Heart rate
recovery
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Heart Rate Variability
Beats
per
minute
time
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Normal Heart Rate Variability
rest
exertion
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Decreased Heart Rate
Variability
rest
exertion
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Decreased HRV and its
association with increased
mortality after acute MI

Multicenter Post-Infarction research group
 Reduced HRV post MI poor prognosis
independent of traditional risk factors
 Kleiger. Am J Cardiol. 1987;59:256
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HRV as a predictor of
mortality in the Elderly

Random sample of elderly over 65, # 347
followed for 10 yrs
 Prognostic power of traditional risk factors
compared
 24 hr HRV best predictor of death in elderly
subjects
 Circulation 1998;97:2031
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Reduced Heart Rate
Variability and Mortality Risk in
an Elderly Cohort

2 hour Holter Moniter analysis
 Estimation of HRV offers prognostic
information for all cause mortality beyond
that provided by evaluation of traditional
risk factors
 Circulation. 1994;90:878-883
 Framingham Heart Study
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HRV Components

The Wave Model of HRV
 Amplitude
– Rate of Change
– Degree of Change

Frequency
– Variation in frequency rate
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HRV Amplitude
-- degree of change
good
bad
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HRV Amplitude
-- rate of change
good
bad
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HRV Frequency
good
bad
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Cardiac Interbeat Interval
Dynamics From Childhood to
Senescence

Healthy aging is associated with a loss of
complex variability in R-R intervals
 New methods of R-R interval variability
based on nonlinear dynamics may give
insight into heart rate dynamics
 Pikkujamsa;Circulation.1999;100:393
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Heritability of HRV
The Framingham Heart Study

Holter moniter data, comparing siblings
 “Heritable factors may explain a substantial
proportion of the variance in HR and HRV”
 Singh;Circulation.1999;99:2251
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Association of Depression
With Reduced HRV in
Coronary Artery Disease

Depressed patients with CAD have decreased
HRV compared with nondepressed CAD patients
even after adjusting for relevant covariates
 Decreased HRV may explain the increased risk for
cardiac mortality and morbidity in depressed
patients
 Carney;Am J Cardiol 1995;76:562
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HRV in healthy middle age
pts, post MI pts and heart
transplants

HRV excellent predictor of death of any
cause or arrhythmic death
 Heart Transplant most reduced HRV
 Circulation. 1996;93:2142
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Association of hyperglycemia
with reduced HRV

Framingham Heart Study
 HRV is inversely associated with plasma
glucose levels. It is reduced in both DM and
in subjects with impaired fasting glucose
 Does reduced HRV contribute to increased
cardiac mortality of DM and impaired
FBG?
 Am J Cardiol 2000;86:309
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Short and long term effects of
cigarette smoking on HRV

Smoking results in decreased vagal cardiac
control leading to diminished HRV
 Hayano; Am J Cardiol 1990;65:84
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Decreased HRV associations
– a few examples

Aging
 Diseases
– CHF, Parkinsons, DM, Cancer, Depression

Syndromes
– Chronic Fatigue Syndrome, Sleep Apnea,
Septic Shock

Lifestyle
– Smoking, Sedentary
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Reduced HRV precedes
Arrhythmias – atrial and ventricular
 Cardiac mortality
 All cause mortality
 Manifest disease

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Altered Complexity and Correlation
Properties of R-R Interval Dynamics
Before Spontaneous Paroxysmal
Atrial Fibrillation

A decrease in HRV precedes the onset of AF
in patients with no structural heart disease
 Vikman;Circulation.1999;100:2079
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Low HRV in a 2 minute rhythm
strip predicts rsk of CHD &
mortality from several causes

Middle aged men and women
 Low HRV predictive of increased mortality
rates…this relation could not be attributed
to cardiovascular risk factors or to
underlying disease
 Low HRV precedes manifest disease
 Dekker;Circulation.2000;102:1239
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Decomplexification in critical illness and
injury: Relationship between HRV,
severity of illness, and outcome
 135 pediatric ICU admissions, mean age 6.8
 Decomplexification of physiologic
dynamics is equivalent to loss of variability
or increased regularity
 The greater the severity of illness, the less
HRV was detected. Applied to all illnesses
 Crit Care Med 1998;26:352-357
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Multiple Organ Dysfunction
Syndrome

Linked with progressive reduction in Heart
Rate Variability as the syndrome progresses
 HRV reflects trends and level of severity
 Correlation holds regardless of the inciting
event of MODS
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Uncoupling of biologic
oscillators: A hypothesis re the
pathogenesis of MODS

Healthy organs behave as biologic
oscillators, coupled and maintained by a
communications network that includes
neural, humoral and cytokine components
 HRV is a reflection of the degree of
coupling between organ systems
 Godin; Crit Care Med;1996
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Coupling of biological
oscillators
Heart
CNS
Immune
Coupling
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MODS and HRV

SIRS initiates disruption of communication
and uncoupling which if severe enough
leads to MODS
 MODS a consequence of the uncoupling of
organ systems as reflected by loss of
biologic oscillations or variability
 HRV decreases as SIRS and MODS unfolds
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Uncoupling of biologic
oscillators: A hypothesis re the
pathogenesis of MODS

HRV decreases (organ isolation) with age
 HRV decreases (organ isolation) with SIRS
 Advanced age and SIRS means higher risk
for MODS (irreversible organ isolation)
 Crit Care Med 1996;24:1107
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Experimental human
endotoxemia increases
cardiac regularity





Prospective, randomized, crossover trial
Infusion of endotoxin into human volunteers
causes loss of HRV
HRV is an indicator of coupling between biologic
oscillators(e.g. heart, brain, lung)
MODS caused by an uncoupling of organ systems
Crit Care Med 1996;24:1117
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Decreased HRV

Implies reduced interconnections
 Associated with reduced waves or rhythms
throughout, ie
– Temperature Variability
– Diurnal Rhythms
– Hormonal Pulses
– Gait, agility, CNS activity, EEG pattern
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Wave resonance - healthy

Heart rate

Brain

Temperature

Diurnal
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Wave resonance - unhealthy

Heart

Brain

Temperature

Diurnal
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HRV Implications

HRV = Wave
 Wave = Signature of system dynamics
 System Dynamics = Complexity
 Complexity = Biologic Health/Resiliency
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Biologic Resiliency

Biology is mutually supportive systems
 Systems embedded within systems
 The rich and responsive interconnections
between systems is key to robust health
 Wave patterns reflect the status of the
interconnections and the responsiveness of
the agents
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Implications of HRV

Insights from wave patterns
 Pharmacology
 Lifestyle choices
 Influencing HRV with training
 Ubiquity of waves or rhythms
 Everything is connected to everything else
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HRV Implications

Wave pattern implications
– Decrease complexity = poor health
– Increase complexity = good health
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HRV Implications

Pharmacology
– Medications that can decrease HRV
 Amitryptiline, Anticholinergics, Anti-arryhthmics
– Medications that can increase HRV in CHF
 Beta blockers, spironolactone
– Testing of prospective new drugs
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HRV Implications

Lifestyle choices
– Decrease HRV
 Smoking
 Sedentary
– Increase HRV
 Exercise
 Meditation or relaxation techniques
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HRV Implications

Influencing HRV with training
– Sprinters have high HRV
– Ultra marathoners have low HRV

Sprint training may have more of a health
benefit than endurance training
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HRV Implications

Ubiquity of waves or rhythms at all levels
– Biochemical oscillation
– Cell cycles
– Organ system
– Organisms
– Biosphere
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HRV Implications

Everything is connected to everything else
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Circadian (24 hr) Rhythm
an indicator of system health
6 am
12 noon
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6 pm
12 pm
Healthy Circadian Rhythm
“waves on waves”
6 am
12 noon
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6 pm
12 pm
Abnormal Circadian Rhythm less “waves on waves”
6 am
12 noon
6 pm
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12 pm
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end
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Heart Rate Variability
A risk factor for all cause mortality
Robert A. Lindberg, MD
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Effects of Spironolactone on
HRV and LV systolic function
in severe ischemic heart
failure
In CHF pt’s on conventional medications,
the addition of spironolactone induces a
favorable sympathovagal balance
 Korkmaz; Am J Cardiol 2000;86:649

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Non-linear dynamics for
clinicians: chaos theory,
fractals, and complexity at the
bedside

Normal HRV represents multiscale fractal
complexity of the heart rate
 Abnormal HRV represents loss of
multiscale fractal complexity
 Goldberger;Lancet.1996;347:1312
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Multifractality in human
heartbeat dynamics

Physiological signals under healthy
conditions have a fractal temporal structure
 The healthy human heartbeat has fractal
scaling
 There is a loss of fractal scaling in
congestive heart failure
 Ivanov;Nature.1999;399:461
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Fractals

An object composed of subunits that
resembles the larger scale structure, a
property known as self-similarity
 At each scale of magnification, the pattern
remains the same
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Classical vs Fractal Geometry

Classical Geometry
– Smooth, regular, and integer dimensions (1, 2
and 3 for line, surface and volume respectively)

Fractal Geometry
– Rough, irregular and non-integer, or fractional
dimensions
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Classical (Euclidian) vs
Fractal Line

Classical: single scale and length

Fractal: multiple scales, self-similar
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Examples of Fractal
Structures

Trees, coral formations, clouds, coastlines,
mountain ranges, galaxies
 Arterial and venous trees, neurons,
tracheobronchial tree, His Purkinje network,
intestinal villi
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Examples of Non Fractal
Structures
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Fractal Structures
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Fractal Processes

Fractal processes generate irregular
fluctuations on multiple time scales,
analogous to fractal objects that have
wrinkly structure on different length scales
 The variation over time is statistically selfsimilar
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Examples of Fractal
Processes

Weather patterns, Dow Jones average,
population dynamics
 Heart Rate, Respirations, Blood pressure,
WBC counts, temperature
 Demonstrate Self-Similar Dynamics
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Complex Nonlinear Systems

A system consisting of a large and variable
number of component parts
 The components display marked variability
over time
 There is a high degree of connectivity and
interdependence between variables
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Complex nonlinear systems
are ubiquitous in nature

Weather patterns
 Biosphere of our planet
 Stock market
 Ecosystem of a tropical rain forest
 Central nervous system
 Immune system
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Relevance of Complexity
Science to Medicine

Concept of the human body as a complex
adaptive system
 Systems embedded within systems
 The sum is greater than the parts
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