Journal
JOURNAL OF APPLIED PHYSIOLOGY
Volume 106, Issue 4, Pages 1038-1049Publisher
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/japplphysiol.90769.2008
Keywords
nonlinear models; Volterra kernels; opioid; ventilation; chemoreflex
Categories
Funding
- Association of Anaesthetists of Great Britain and Ireland
- International Anesthesia Research Society
- European Social Fund
- National Resources, Operational Program Competitiveness, General Secretariat for Research and Development
- Medical Research Council (UK)
- Medical Research Council [G0500444] Funding Source: researchfish
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Mitsis GD, Governo RJ, Rogers R, Pattinson KT. The effect of remifentanil on respiratory variability, evaluated with dynamic modeling. J Appl Physiol 106: 1038-1049, 2009. First published February 5, 2009; doi:10.1152/japplphysiol.90769.2008.-Opioid drugs disrupt signaling in the brain stem respiratory network affecting respiratory rhythm. We evaluated the influence of a steady-state infusion of a model opioid, remifentanil, on respiratory variability during spontaneous respiration in a group of 11 healthy human volunteers. We used dynamic linear and nonlinear models to examine the effects of remifentanil on both directions of the ventilatory loop, i.e., on the influence of natural variations in end-tidal carbon dioxide (PETCO2) on ventilatory variability, which was assessed by tidal volume (VT) and breath-to-breath ventilation (i.e., the ratio of tidal volume over total breath time VT/TTOT), and vice versa. Breath-by-breath recordings of expired CO2 and respiration were made during a target-controlled infusion of remifentanil for 15 min at estimated effect site (i.e., brain tissue) concentrations of 0, 0.7, 1.1, and 1.5 ng/ml, respectively. Remifentanil caused a profound increase in the duration of expiration. The obtained models revealed a decrease in the strength of the dynamic effect of PETCO2 variability on VT (the controller part of the ventilatory loop) and a more pronounced increase in the effect of VT variability on PETCO2 (the plant part of the loop). Nonlinear models explained these dynamic interrelationships better than linear models. Our approach allows detailed investigation of drug effects in the resting state at the systems level using noninvasive and minimally perturbing experimental protocols, which can closely represent real-life clinical situations.
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