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Frank Jacono, MD Pulmonary , Critical Care, and Sleep Medicine September 26, 2009. Variability Analysis in the ICU: From Bench to Bedside. Disclosures. None. Funding Support. VA Advanced Career Development Award NIH R33 Cluster Grant Ohio Board of Regents. Objectives.

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frank jacono md pulmonary critical care and sleep medicine september 26 2009
Frank Jacono, MD

Pulmonary , Critical Care, and Sleep Medicine

September 26, 2009

Variability Analysis in the ICU:From Bench to Bedside

disclosures
Disclosures
  • None

Funding Support

  • VA Advanced Career Development Award
  • NIH R33 Cluster Grant
  • Ohio Board of Regents
objectives
Objectives
  • Review variability in biologic systems
  • Review measures of variability
  • Discuss breathing pattern variability in acute lung injury
slide4

Severe congestive heart failure, sinus rhythm

Healthy subject, normal sinus rhythm

Severe congestive heart failure, sinus rhythm

Atrial fibrillation

PNAS 2002; 99: 2466-2472

slide5

120

Heart Rate (bpm)

80

40

Normal

CHF

http://www.physionet.org/tutorials/ndc/

biologic patterns
Biologic Patterns
  • 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
homeokinesis
Homeokinesis

“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

variability
Variability
  • 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 91:1131-1141, 2001

respiratory pattern
Respiratory Pattern
  • 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

Normal Subject

Chest 1983: 84: 202-205

respiratory pattern1
Respiratory Pattern
  • Restrictive lung disease
    • Higher respiratory rate
    • Higher minute ventilation
    • Regular rhythm

Pulmonary Fibrosis

Chest 1983; 84: 286-294

respiratory pattern2
Respiratory Pattern

Restrictive

Normal

AJRCCM 2002; 165: 1260-1264

respiratory pattern3
Respiratory Pattern

AJRCCM 2002; 165: 1260-1264

variability1
Variability
  • 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
slide15

Pathologic Breakdown of Nonlinear Dynamics

Deterministic

Stochastic

CHF

Atrial Fibrillation

http://www.physionet.org/tutorials/ndc/

surrogates
Surrogates
  • “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
variability interim summary
Variability – Interim Summary
  • 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
slide19

Overall Hypothesis

  • 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
experimental design
Experimental Design
  • 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
data analysis
Data Analysis
  • 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
sample entropy sampen
Sample Entropy (SampEn)
  • Measure of disorder / randomness
  • A lower SampEn indicates more self-similarity, lower complexity and greater predictability
  • Measures both linear and nonlinear sources of variability
preliminary results
Preliminary Results
  • 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.

references
References
  • 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.
references1
References
  • 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.