Variability Analysis in the ICU: From Bench to Bedside

1. Frank Jacono, MD Pulmonary , Critical Care, and Sleep Medicine September 26, 2009 Variability Analysis in the ICU:From Bench to Bedside

2. Disclosures • None Funding Support • VA Advanced Career Development Award • NIH R33 Cluster Grant • Ohio Board of Regents

3. Objectives • Review variability in biologic systems • Review measures of variability • Discuss breathing pattern variability in acute lung injury

4. Severe congestive heart failure, sinus rhythm Healthy subject, normal sinus rhythm Severe congestive heart failure, sinus rhythm Atrial fibrillation PNAS 2002; 99: 2466-2472

5. 120 Heart Rate (bpm) 80 40 Normal CHF http://www.physionet.org/tutorials/ndc/

6. 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

7. 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

8. 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

9. 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

10. Respiratory Pattern • Restrictive lung disease • Higher respiratory rate • Higher minute ventilation • Regular rhythm Pulmonary Fibrosis Chest 1983; 84: 286-294

11. Respiratory Pattern Restrictive Normal AJRCCM 2002; 165: 1260-1264

12. Respiratory Pattern AJRCCM 2002; 165: 1260-1264

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

14. 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

15. Pathologic Breakdown of Nonlinear Dynamics Deterministic Stochastic CHF Atrial Fibrillation http://www.physionet.org/tutorials/ndc/

16. 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

17. Surrogates

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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.

24. Questions?

25. 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.

26. 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.