Frank Jacono, MD Pulmonary, Critical Care, and Sleep Medicine September 26, 2009.
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Transcript of Frank Jacono, MD Pulmonary, Critical Care, and Sleep Medicine September 26, 2009.
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Frank Jacono, MDPulmonary , Critical Care, and Sleep Medicine September 26, 2009
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None
VA Advanced Career Development Award
NIH R33 Cluster Grant Ohio Board of Regents
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Review variability in biologic systems Review measures of variability Discuss breathing pattern variability in
acute lung injury
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PNAS 2002; 99: 2466-2472
Severe congestive heart failure, sinus rhythm
Severe congestive heart failure, sinus rhythm
Atrial fibrillation
Healthy subject, normal sinus rhythm
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http://www.physionet.org/tutorials/ndc/
Hea
rt R
ate
(bpm
)
Normal CHF
120
80
40
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Rhythmic patterns are present throughout biologic systems
Homeostasis – short term fluctuations dismissed as “noise”
However, this “noise” may actually contain deterministic information on longer time scales
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“ability of an organism functioning in a variable external environment to maintain a highly organized internal environment fluctuating within acceptable limits by dissipating energy in a far-from equilibrium state”
Variability is normal Excessive or lack of variability is abnormal
Results form excessive or limited energy utilization
J Appl Physiol 91:1131-1141, 2001
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Non-random variability in “homeostatic” systems has been reported in: Heart rate Blood pressure Minute ventilation Tidal volume Leukocyte count Renal blood flow
CHF Sleep apnea Asthma Arrhythmias Shock
Critical Care 2004, 8:R367-R384J Appl Physiol 91:1131-1141, 2001
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Previous attempts have been made to evaluate breathing patterns
In 1983 Tobin published findings on breathing patterns in normal and diseased subjects using respiratory inductive plethysmography
Chest 1983: 84: 202-205
Normal Subject
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Restrictive lung disease Higher
respiratory rate Higher minute
ventilation Regular rhythm
Chest 1983; 84: 286-294
Pulmonary Fibrosis
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Restrictive
Normal
AJRCCM 2002; 165: 1260-1264
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AJRCCM 2002; 165: 1260-1264
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Proc Am Thorac Soc 2006; 3: 467–472
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Methods for evaluating variability in complex systems are not broadly applied to biological sciences
Stochastic Present state unrelated to the next state Random fluctuations
Deterministic Temporal structure Memory
Both types of variability can exist simultaneously
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CHF Atrial Fibrillation
Pathologic Breakdown of
Nonlinear Dynamics
http://www.physionet.org/tutorials/ndc/
Deterministic
Stochastic
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“Shuffles” the raw data set Preserves linear measures Eliminates non-linear relationships
Comparison of measures made on raw and surrogate data sets allow quantification of nonlinear information present
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dx(t)
dt (x y)
dy(t)
dtx( z) y
dz(t)
dtxy z
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Biological systems are complex and measured outputs exhibit variability
Variability itself is neither good nor bad, and may increase or decrease with stress or disease
Growing appreciation that changes in variability are clinically relevant (changes occur in disease states)
Different measures (tools) reflect distinct aspects of overall signal variability
Surrogate data sets are a useful technique for isolating nonlinear variability
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Acute lung injury will alter breathing pattern variability
Changes in breathing pattern variability will reflect the severity of lung injury, and will be predictive of progression or resolution of lung injury
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Male Sprague Dawley rats (wt 120 – 200 g) intratracheal injection of: 1 unit Bleomycin
3 units Bleomycin
PBS
Plethysmography recordings were made before and 7 days after intra-tracheal instillation of either BM or placebo
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Stationary, artifact-free epochs (30 - 60 sec) of the raw whole-body plethysmography signal
Standard linear measures (mean, standard deviation, coefficient of variation) were used to evaluate the plethysmography signal
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Measure of disorder / randomness
A lower SampEn indicates more self-similarity, lower complexity and greater predictability
Measures both linear and nonlinear sources of variability
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Respiratory rate increase with induction of acute lung injury
Coefficient of variation does not change with induction of acute lung injury
Nonlinear complexity of breathing pattern variability increases with induction of lung injury Changes persist even during hyperoxia
Young et al., ATS 2009 Abstract Presentation. Manuscript in preparation.
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Rubenfeld GD et al. Incidence and Outcomes of Acute Lung Injury. N Engl J Med 2005; 353: 1685-93.
Goldberger AL. Heartbeats, Hormones, and Health: Is Variability the Spice of Life? AJRCCM 2001; 163: 1289–1296.
Goldberger AL et al. Fractal dynamics in physiology: Alterations with disease and aging. PNAS 2002; 99: 2466-2472.
Goldberger AL. Complex Systems. Proc Am Thorac Soc 2006; 3: 467–472.
Tapanainen JM et al. Fractal Analysis of Heart Rate Variability and Mortality After an Acute Myocardial Infarction. Am J Cardiol 2002; 90: 347–352.
Ware LB and Matthay MA. The Acute Respiratory Distress Syndrome. N Engl J Med 2004; 342(18): 1334-1349.
Pincus SM and Goldberger AL. Physiological time-series analysis: what does regularity quantify? Am J Physiol 1994; 266: H1643-H1656.
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Brack T et al. Dyspnea and Decreased Variability of Breathing in Patients with Restrictive Lung Disease. AJRCCM 2002; 165: 1260-1264.
Tobin MJ et al. Breathing Patterns 1: Diseased Subjects. Chest 1983: 84: 202-205.
Tobin MJ et al. Breathing Patterns 2: Diseased Subjects. Chest 1983; 84: 286-294.
Goldberger AL. Nonlinear Dynamics, Fractals, and Chaos Theory: Implications for Neuroautonomic Heart Rate Control in Health and Disease. http://www.physionet.org/tutorials/ndc/
Jacono FJ et al. Acute lung injury augments hypoxic ventilatory response in the absence of systemic hypoxemia. J Appl Physiol 2006; 101: 1795-1802.
Remmers JE. A Century of Control of Breathing. AJRCCM 2005; 172: 6-11. Seely AJE and Macklem PT. Complex systems and the technology of
variability analysis. Critical Care 2004, 8:R367-R384. Que C et al. Homeokinesis and short-term variability of human airway
caliber. J Appl Physiol 91:1131-1141, 2001.