Portal de Periódicos da CAPES

Variability, measuring the spice of life

2011; American Physiological Society; Volume: 111; Issue: 2 Linguagem: Inglês

10.1152/japplphysiol.00786.2011

8750-7587

Frank J. Jacono, Thomas E. Dick,

Chronic Obstructive Pulmonary Disease (COPD) Research

Invited EditorialsVariability, measuring the spice of lifeFrank J. Jacono, and Thomas E. DickFrank J. JaconoDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland; Louis Stokes Cleveland VA Medical Center, Cleveland; , and Thomas E. DickDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland; Department of Neurosciences, Case Western Reserve University, Cleveland, OhioPublished Online:01 Aug 2011https://doi.org/10.1152/japplphysiol.00786.2011This is the final version - click for previous versionMoreSectionsPDF (37 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat ventilatory control instability forms the theoretical basis for respiratory control disorders ranging from apnea to weaning failure from mechanical ventilation. While ventilatory arrhythmia has multiple mechanisms, an underlying issue is quantifying the arrhythmia. The article by Nemati et al. (14) in the July 2011 issue of the Journal of Applied Physiology takes a significant step in this direction. In this manuscript, the authors adopt a novel approach to assess ventilatory stability using the spontaneous breathing pattern. They based their method on trivariate autoregressive modeling using ventilation, end-tidal Pco2, and Po2 to estimate “loop gain” of the respiratory control system in sheep. They analyzed spontaneous breathing to characterize the dynamics of the respiratory control system and to determine the propensity for ventilatory instability. Their methodology is straightforward and resolves a number of the technical difficulties of experimental determination of loop gain.Classical physiology is grounded on the principle of homeostasis in which regulatory mechanisms act to reduce variability and to maintain a steady state, e.g., “set point” (9). With this model of behavioral control, fluctuations are often dismissed as “noise” of little or no significance. However, as argued by Ary Goldberger in his editorial (9), many systems in nature operate away from an equilibrium point, and their control systems aim to maintain homeodynamic, as opposed to homeostatic, physiology. Homeodynamics underlie the variability and complex temporal patterns manifested in physiologic signals.Cherniack and coworkers (5) applied a systems engineering approach to respiration, describing a controller (brain stem respiratory pattern generator), sensors (chemo- and mechanoreceptors), and a plant (airway, chest wall, muscles, and pulmonary tissue). This system maintains numerous controlled variables within their homeodynamic ranges, but is also responsible for rapidly changing ventilation in response to often-unpredictable stimuli. Brain stem pattern generation and processing of sensory information are reflected in the integrated motor response (breathing pattern), including variability in phase duration and pattern. Thus the respiratory system can be effectively modeled as a circuit with feedback in which the loop gain is the response to a disturbance divided by the disturbance itself.The strong theoretical underpinnings for loop gain as a model of ventilatory control have been bolstered and refined by empirical testing and measurement. Historically, measures for the stability of a rhythmic oscillation relied on the variability of the pattern and on the response to a perturbation, in particular the relaxation time or how quickly (the number of cycles needed) the oscillation returned to its intrinsic cycle period after a brief perturbation (16). Of course, the more variable the pattern the harder it is to determine when the oscillation returns to baseline. Nevertheless, this approach was applied to understanding the neural control of respiration in both reduced preparations and humans. In reduced preparations the brief perturbation was a resetting stimulus like superior laryngeal stimulation (7, 15). In these studies stability of the respiratory pattern was enhanced by hypercapnia and depended on an intact dorsolateral pons (7, 15). In humans, the stability of the respiratory cycle can be assessed by testing with pseudorandom binary stimulation, which challenges the respiratory system with either hypoxia or hypercapnia, delivered in repeatable but random like sequences such that the autocorrelation function of the sequence approximates that of white noise. Thus the loop gain of the impulse response can be determined mathematically by cross-correlating the test input with measured output (10). In these studies, the patients with obstructive sleep apnea (OSA) compared with a control group had a higher peak response and shorter settling and response duration times (10). These differences were observed in wakefulness, indicating that OSA patients had abnormal dynamics in their respiratory control that could contribute to ventilatory instability during sleep (10).Nemati et al. developed an approach to characterize and quantify overall loop gain that may prove clinically relevant, as subjects would not need to participate in complex protocols. In addition to congestive heart failure and obstructive sleep apnea highlighted by the authors, the ability to assess respiratory instabilities could have other applications. For instance, respiratory pattern variability decreases in restrictive lung disease (3, 13) and in septic and acutely ill patients (1). In contrast, pattern variability increases in patients with obstructive lung disease (13), panic disorder (17), and sleep-disordered breathing during the wake-sleep transition (11). This computational tool could assess how closed loop gain plays a role in these changes in ventilation.In addition to evaluating mechanisms of instability, this tool could be applied to track patients to determine recovery or disease progression (12). A bedside assessment of respiratory instability would assist in the evaluation of weaning from mechanical ventilation in critically ill patients, which is a challenging task. The benefits of early successful extubation must be weighed against the risks of unnecessary delay and premature weaning attempts. A few clinical studies have examined novel indexes based on respiratory pattern variability (RPV) to predict extubation failure. For example, Engoren (8) studied patients who required either prolonged (>7 days) or brief (>12 h) ventilatory support. Breathing patterns were recorded during spontaneous ventilation with 5 cmH2O positive end-expiratory pressure [increased lung volume may affect these measures (6)]. Patients with prolonged ventilation and who failed a weaning trial had increased complexity of the tidal volume (VT) time series as measured by Approximate Entropy compared with patients who were successfully weaned after prolonged and brief ventilation. In distinct contrast Bien et al. (2) noted that decreased RPV was associated with a high incidence of weaning failure. In this study, RPV was measured by coefficient of variation (CV) and Poincaré plot analysis of respiratory intervals and VT. Other studies support both increases and decrease in RPV but a critical factor in these studies is that these are single rather than multiple measures over time. A simple assessment of closed loop gain could provide information at multiple time points and whether the propensity for respiratory instability is decreasing or increasing.Finally closed loop gain would affect both RPV and complexity but these variables can change in opposite directions, and it is our perspective that a comprehensive analysis will include multiple analytical techniques to capture different aspects of pattern variability (12). Taken together, the available studies suggest that measurements of RPV hold potential not only as weaning but also as health indexes. Nemati et al. provide a computational tool that can provide insight for further study.DISCLOSURESNo conflicts of interest, financial or otherwise, are declared by the authors.REFERENCES1. Askanazi J , Silverberg PA , Hyman AI , Rosenbaum SH , Foster R , Kinney JM. Patterns of ventilation in postoperative and acutely ill patients. Crit Care Med 7: 41–46, 1979.Crossref | ISI | Google Scholar2. Bien MY , Hseu SS , Yien HW , Kuo BI , Lin YT , Wang JH , Kou YR. Breathing pattern variability: A weaning predictor in postoperative patients recovering from SIRS. Intensive Care Med 30: 241–247, 2004.Crossref | ISI | Google Scholar3. Brack T , Jubran A , Tobin MJ. Dyspnea and decreased variability of breathing in patients with restrictive lung disease. Am J Respir Crit Care Med 165: 1260–1264, 2002.Crossref | PubMed | ISI | Google Scholar4. Bruce EN. Assessing respiratory control during spontaneous breathing: practice may be more difficult than theory. Am J Respir Crit Care Med 165: 1033–1034, 2002.Crossref | ISI | Google Scholar5. Cherniack NS , Longobardo GS , Levine OR , Mellins R , Fishman AP. Periodic breathing in dogs. J Appl Physiol 21: 1847–1854, 1966.Link | ISI | Google Scholar6. Dhingra RR , Jacono FJ , Fishman M , Loparo KA , Rybak IA , Dick TE. Vagal-dependent nonlinear variability in the respiratory pattern of anesthetized, spontaneously breathing rats. J Appl Physiol 111: 272–284, 2011.Link | ISI | Google Scholar7. Eldridge FL , Paydarfar D , Wagner PG , Dowell RT. Phase resetting of respiratory rhythm: effect of changing respiratory “drive.” Am J Physiol Regul Integr Comp Physiol 257: R271–R277, 1989.Link | ISI | Google Scholar8. Engoren M. Approximate entropy of respiratory rate and tidal volume during weaning from mechanical ventilation. Crit Care Med 26: 1817–1823, 1998.Crossref | ISI | Google Scholar9. Goldberger AL. Heartbeats, hormones, and health; is variability the spice of life? Am J Respir Crit Care Med 163: 1289–1290, 2001.Crossref | ISI | Google Scholar10. Hudgel DW , Gordon EA , Thanakitcharu S , Bruce EN. Instability of ventilatory control in patients with obstructive sleep apnea. Am J Respir Crit Care Med 158: 1142–1149, 1998.Crossref | PubMed | ISI | Google Scholar11. Ibrahim LH , Patel SR , Modarres M , Johnson NL , Mehra R , Kirchner HL , Redline S. A measure of ventilatory variability at wake-sleep transition predicts sleep apnea severity. Chest 134: 73–78, 2008.Crossref | ISI | Google Scholar12. Jacono FJ , De Georgia MA , Willson CG , Dick TE , Loparo KA. Data acquisition and complex systems analysis in critical care: Developing the ICU of the future. J Healthcare Engineering 1: 337–356, 2010.Crossref | ISI | Google Scholar13. Kuratomi Y , Okazaki N , Ishihara T , Arai T , Kira S. Variability of breath-by-breath tidal volume in normal and diseased subjects. Monitoring with electrical impedance pneumography. Jpn J Med 24: 141–149, 1985.Crossref | Google Scholar14. Nemati S , Edwards BA , Sands SA , Berger PJ , Wellman A , Verghese GC , Malhorta A , Butler JP. Model-based characterization of ventilatory stability using spontaneous breathing. J Appl Physiol 111: 55–67, 2011.Link | ISI | Google Scholar15. Oku Y , Dick TE. Phase resetting of the respiratory cycle before and after unilateral pontine lesion in cat. J Appl Physiol 72: 721–730, 1992.Link | ISI | Google Scholar16. Schöner G , Kelso JA. Dynamic pattern generation in behavioral and neural systems. Science 239: 1513–1520, 1988.Crossref | PubMed | ISI | Google Scholar17. Yeragani VK , Radhakrishna RK , Tancer M , Uhde T. Nonlinear measures of respiration: Respiratory irregularity and increased chaos in patients with panic disorder. Neuropsychobiol 46: 111–120, 2002.Crossref | PubMed | ISI | Google ScholarAUTHOR NOTESAddress for reprint requests and other correspondence: T. E. Dick, Division of Pulmonary, Critical Care and Sleep Medicine, Dept. of Medicine, Case Western Reserve Univ., 10900 Euclid Ave., Cleveland, OH 44106 (e-mail: thomas.[email protected]edu). Download PDF Previous Back to Top Next FiguresReferencesRelatedInformationCited ByA new approach for digestive disease diagnosis: Dynamics of gastrointestinal electrical activityMedical Hypotheses, Vol. 128Kölliker-Fuse nuclei regulate respiratory rhythm variability via a gain-control mechanismRishi R. Dhingra, Mathias Dutschmann, Roberto F. Galán, and Thomas E. Dick1 February 2017 | American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, Vol. 312, No. 2Variability in respiratory rhythm generation: In vitro and in silico modelsCommunications in Nonlinear Science and Numerical Simulation, Vol. 32Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?Critical Care, Vol. 18, No. 2Pattern variability in a computational model of respiratory rhythm generation16 July 2012 | BMC Neuroscience, Vol. 13, No. S1 More from this issue > Volume 111Issue 2August 2011Pages 351-352 https://doi.org/10.1152/japplphysiol.00786.2011PubMed21719724History Received 2 June 2011 Accepted 27 June 2011 Published online 1 August 2011 Published in print 1 August 2011 Metrics Downloaded 299 times