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

Frank Jacono, MDPulmonary , 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: 2466-2472

Severe congestive heart failure, sinus rhythm

Severe congestive heart failure, sinus rhythm

Atrial fibrillation

Healthy subject, normal sinus rhythm

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

Hea

rt R

ate

(bpm

)

Normal CHF

120

80

40

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 91:1131-1141, 2001

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

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

Restrictive lung disease Higher

respiratory rate Higher minute

ventilation Regular rhythm

Chest 1983; 84: 286-294

Pulmonary Fibrosis

Restrictive

Normal

AJRCCM 2002; 165: 1260-1264

AJRCCM 2002; 165: 1260-1264

Proc Am Thorac Soc 2006; 3: 467–472

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

CHF Atrial Fibrillation

Pathologic Breakdown of

Nonlinear Dynamics

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

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

dx(t)

dt (x y)

dy(t)

dtx( z) y

dz(t)

dtxy z

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 (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

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.

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.

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.