Transcript ppt - Cardiovascular Division Heart Rate Variability Laboratory

Heart Rate Variability for
Clinicians
Phyllis K. Stein, Ph.D.
Research Associate Professor of
Medicine and Director, Heart Rate
Variability Laboratory
Washington University School of
Medicine
St. Louis, MO
The Big Picture
 Heart rate is under both sympathetic (SNS)
and parasympathetic (PNS) control.
 Rapid fluctuations in heart rate usually reflect
PNS control only (respiratory sinus
arrhythmia).
 Therefore, changes in heart rate over time
provide a window onto autonomic
physiology.
 Analysis of heart rate variability (HRV)
quantifies these changes over time.
Parasympathetic
Nervous system
Heart Rate
Cardiac output
Blood pressure
Renin angiotensin
system
Sympathetic
Nervous system
Simplified model of cardiovascular control showing
modulation of heart rate by parasympathetic and sympathetic
feedback loops
Influences on HRV
Extrinsic Rhythms
 Activity
 Sleep-wake cycle
 Eating Meals
 Mental Stress
 Physical Stress
Influences on HRV
Intrinsic Periodic Rhythms
 Respiratory sinus arrhythmia
 Baroreceptor reflex regulation
 Thermoregulation
 Neuroendocrine secretion
 Circadian rhythms
 Sleep stages during the night
 Other, unknown rhythms
Quantifying HRV
Approach 1: The Physiologist’s
Paradigm
HR data collected over short period of time
(~5-20 min), with or without interventions,
under carefully controlled laboratory
conditions.
Approach 2: The Clinician’s and the
Epidemiologists’s Paradigm
Ambulatory Holter Recordings collected over
24-hours or less, usually on outpatients.
Quantifying HRV
Approach 1: How much variability is there?
Time Domain and Geometric Analyses
Approach 2: What are the underlying
rhythms? What physiologic process do they
represent? How much power does each
underlying rhythm have?
Frequency Domain Analysis
Approach 3: How much complexity or selfsimilarity is there?
Non-Linear Analyses
Time Domain HRV
Calculated from normal-to-normal interbeat
(N-N) intervals
 SDNN-Standard deviation of N-N
intervals in ms (Total HRV)
 SDANN-Standard deviation of 5min mean values of N-Ns for each 5
minute interval in ms (Circadian
HRV)
o Both reflect “longer-term” HRV
Time Domain HRV
 SDNNIDX-Average of standard
deviations of N-Ns for each 5 min
interval in ms (Combined sympathetic
and parasympathetic HRV)
o Reflects “intermediate-term” HRV
 AVGNN-Average of N-N intervals in
ms
o Equivalent to heart rate of normal-tonormal beats. (HR=60,000/AVNN)
Time Domain HRV
Calculated from differences between
successive N-N intervals
 rMSSD-Root mean square of
successive differences of N-N
intervals in ms
 pNN50-Percent of successive N-N
differences >50 ms
Referred to as “short-term” HRV and
reflect parasympathetic influence
on heart rate
Frequency Domain HRV
 Based on autoregressive techniques or
fast Fourier transform (FFT).
 Partitions the total variance in heart rate
into underlying rhythms that occur at
different frequencies.
 These frequencies can be associated
with different intrinsic, autonomicallymodulated periodic rhythms.
What are the Underlying Rhythms?
One rhythm
5 seconds/cycle or
12 times/min
5 seconds/cycle=
1/5 cycle/second
1/5 cycle/second=
0.2 Hz
What are the Underlying Rhythms?
Three Different Rhythms
High Frequency = 0.25 Hz (15
cycles/min
Low Frequency = 0.1 Hz (6
cycles/min)
Very Low Frequency = 0.016 Hz
(1 cycle/min)
What are the Underlying Rhythms in
the Heart Rate Signal?
 High frequency band (HF)
At respiratory frequencies (9-24
cycles/minute)
PNS influences on HR
 Low frequency band (LF)
At Mayer wave frequencies~every 8- 10
sec frequency
Baroreceptor, SNS, PNS influences on HR
What are the Underlying Rhythms
in the Heart Rate Signal?
 Very low frequency band (VLF) At
~every 20 sec-every 5 min frequency
Reflects vasomotor changes,
thermoregulatory, possibly PNS influences
on HR
 Ultra low frequency power band (ULF)
At >every 5 min to once in 24 hours
Reflects circadian, neuroendocrine, activity,
other unknown rhythms
LF peak
HF peak
0
0.20 Hz
0.40 Hz
24-hour average of 2-min power spectral
plots in a healthy adult
Non-Linear HRV
 Non-linear HRV characterize the structure
of the HR time series, i. e., is it random or
self-similar.
 Increased randomness of the HR time
series is associated with worse outcomes
in cardiac patients.
Non-Linear HRV
 Most commonly used measure of
randomness is the short-term fractal
Scaling exponent (DFA1). Decreased
DFA1  increased randomness of the HR.
 Another index is power law slope, a
measure of longer term self-similarity of
HR. Decreased slope  worse outcome.
 Normal DFA1 is about 1.1. DFA1<0.85 is
associated with higher risk.
Comparison of Normal and Highly
Random HRV Plots
Clinical Correlates of Decreased HRV
 Inducible VT and VF
 Risk of Sudden Cardiac Death
 Increased mortality post-MI
 Congestive heart failure
 Poorer survival in CHF
 COPD
Clinical Correlates of Decreased HRV
 Diabetic neuropathy
 Alcoholic neuropathy
 Post cardiac transplant
 Depression
 Susceptibility to SIDS
 Poor survival in premature babies
 Increased mortality in population
studies
Pitfalls in Understanding HRV
 HRV reflects phasic not tonic modulation
of HR and cannot measure autonomic
tone, just modulation of HR.
 HRV is a “black box.” Decreased HRV
can mean decreased autonomic input or
decreased cardiac responsiveness.
 HRV cutpoints for risk stratification best
established in immediate post-MI period.
Pitfalls in Understanding HRV
 Association is not causation
o Endurance athletes have high HRV.
o Does exercise training cause
increased HRV, or are people with
high HRV more suited to endurance
exercise?
 HRV can be exaggerated by increased
randomness, which is not a truly normal
sinus rhythm despite the normal
appearance of the ECG.
Clinical Implications
 Normal HRV is a good negative risk
stratifier.
 Patients with preserved HRV (SDNN>100
ms) are low risk in the absence of other
significant risk factors.
 Sensitivity and specificity of decreased
HRV (SDNN<70 ms) for immediate postMI is about 30%.
How Can HRV Be Increased?

Medications
o
o
o
Beta-blockers
ACE inhibitors in CHF
Digoxin?

Ventricular resynchronization therapy

Cardiac rehab?
How Can HRV Be Increased?

Lifestyle modifications:
o
Smoking cessation
o
Weight loss
o
Exercise
o
Stress management
o
Lipid lowering?
o
Glycemic control
Final Thoughts
 Analysis of HRV is a non-invasive method
for identifying abnormalities in cardiac
autonomic modulation.
 Decreased HRV by itself is not sufficient
to risk stratify patients, e.g. for AICD
implantation, but HRV in combination with
other risk stratifiers improves risk
stratification.
Final Thoughts
 Normal HRV identifies patients at lower
risk for events.
 HRV cutpoints are population-specific and
have not been fully elucidated.
 It now appears that decreased HRV and
increased randomness of HR are
independently associated with higher
risk.