Presentation on theme: "Frank Jacono, MD Pulmonary, Critical Care, and Sleep Medicine September 26, 2009."— Presentation transcript:
Frank Jacono, MD Pulmonary, Critical Care, and Sleep Medicine September 26, 2009
None VA Advanced Career Development Award NIH R33 Cluster Grant Ohio Board of Regents
Review variability in biologic systems Review measures of variability Discuss breathing pattern variability in acute lung injury
PNAS 2002; 99: Severe congestive heart failure, sinus rhythm Atrial fibrillation Healthy subject, normal sinus rhythm
Heart Rate (bpm) NormalCHF
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
“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
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
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
CHFAtrial Fibrillation Pathologic Breakdown of Nonlinear Dynamics Deterministic Stochastic
“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
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
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
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
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
Measure of disorder / randomness A lower SampEn indicates more self-similarity, lower complexity and greater predictability Measures both linear and nonlinear sources of variability
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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