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Frank Jacono, MD Pulmonary, Critical Care, and Sleep Medicine September 26, 2009.

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Presentation on theme: "Frank Jacono, MD Pulmonary, Critical Care, and Sleep Medicine September 26, 2009."— Presentation transcript:

1 Frank Jacono, MD Pulmonary, Critical Care, and Sleep Medicine September 26, 2009

2  None  VA Advanced Career Development Award  NIH R33 Cluster Grant  Ohio Board of Regents

3  Review variability in biologic systems  Review measures of variability  Discuss breathing pattern variability in acute lung injury

4 PNAS 2002; 99: Severe congestive heart failure, sinus rhythm Atrial fibrillation Healthy subject, normal sinus rhythm

5 Heart Rate (bpm) NormalCHF

6  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

7 “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: , 2001

8  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-R384 J Appl Physiol 91: , 2001

9  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: Normal Subject

10  Restrictive lung disease  Higher respiratory rate  Higher minute ventilation  Regular rhythm Chest 1983; 84: Pulmonary Fibrosis

11 Restrictive Normal AJRCCM 2002; 165:

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13 Proc Am Thorac Soc 2006; 3: 467–472

14  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

15 CHFAtrial Fibrillation Pathologic Breakdown of Nonlinear Dynamics Deterministic Stochastic

16  “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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18  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

19  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

20  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

21  Stationary, artifact-free epochs ( sec) of the raw whole-body plethysmography signal  Standard linear measures (mean, standard deviation, coefficient of variation) were used to evaluate the plethysmography signal

22  Measure of disorder / randomness  A lower SampEn indicates more self-similarity, lower complexity and greater predictability  Measures both linear and nonlinear sources of variability

23  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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25  Rubenfeld GD et al. Incidence and Outcomes of Acute Lung Injury. N Engl J Med 2005; 353:  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:  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):  Pincus SM and Goldberger AL. Physiological time-series analysis: what does regularity quantify? Am J Physiol 1994; 266: H1643-H1656.

26  Brack T et al. Dyspnea and Decreased Variability of Breathing in Patients with Restrictive Lung Disease. AJRCCM 2002; 165:  Tobin MJ et al. Breathing Patterns 1: Diseased Subjects. Chest 1983: 84:  Tobin MJ et al. Breathing Patterns 2: Diseased Subjects. Chest 1983; 84:  Goldberger AL. Nonlinear Dynamics, Fractals, and Chaos Theory: Implications for Neuroautonomic Heart Rate Control in Health and Disease.  Jacono FJ et al. Acute lung injury augments hypoxic ventilatory response in the absence of systemic hypoxemia. J Appl Physiol 2006; 101:  Remmers JE. A Century of Control of Breathing. AJRCCM 2005; 172:  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: , 2001.


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