Physiological noise versus white noise to drive a variable ventilator in a porcine model of lung injury.
OBJECTIVE
Variable ventilation is superior to control mode ventilation in a number of circumstances. The nature of the breathing file used to deliver the variable rate and tidal volume has not been formally examined.


METHODS
We compared two different noise files in a randomized prospective trial of variable ventilation. Pigs were anesthetized, intubated, and mechanically ventilated. Oleic acid was infused to introduce lung injury. The animals were ventilated at a tidal volume of 7 mL x kg(-1), in variable mode, with either physiologically-derived noise (variability file - 1,587 breath intervals-obtained from a spontaneously breathing volunteer; n = 10) or a variability file of identical length derived from computer- generated white noise (n = 10).


RESULTS
The physiologically-derived noise had a power law alpha-exponent of -0.27 and a Hölder exponent of -0.38, indicative of auto-correlated noise. The computer-generated noise had an alpha-exponent of -0.52 and a Hölder exponent of -0.49, indicative of white noise. Both files showed multifractal characteristics. There were no differences between groups, at any time period, for PaO2, PaCO2, and static or dynamic respiratory system compliance. No differences were observed between groups for wet:dry lung weight ratios or for interleukin-8 in bronchoalveolar lavage fluid.


CONCLUSIONS
This study demonstrates that the nature of the variability files, chosen to drive the variable ventilator, had no effect on indices of gas exchange or respiratory mechanics in this model. A considerable overlap of the multifractal files existed. The potential to drive a variable ventilator using algorithm-derived files with multifractal characteristics, thereby eliminating the requirement to use physiologically-derived signals, is discussed.
Froehlich
Kevin F
KF
Department of Anesthesia, University of Manitoba, Winnipeg, Manitoba, Canada.
Graham
M Ruth
MR
Buchman
Timothy G
TG
Girling
Linda G
LG
Scafetta
Nicola
N
West
Bruce J
BJ
Walker
Elizabeth K-Y
EK
McManus
Bruce M
BM
Mutch
W Alan C
WA
D000465:Algorithms; D000758:Anesthesia; D000818:Animals; D001794:Blood Pressure / Q000502:physiology; D001831:Body Temperature / Q000502:physiology; D002245:Carbon Dioxide / Q000097:blood; D002302:Cardiac Output / Q000502:physiology; D004797:Enzyme-Linked Immunosorbent Assay; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D016209:Interleukin-8 / Q000097:blood; D008171:Lung Diseases / Q000139:chemically induced / Q000503:physiopathology / Q000628:therapy*; D019301:Oleic Acid; D009929:Organ Size / Q000502:physiology; D010100:Oxygen / Q000097:blood; D011652:Pulmonary Circulation / Q000502:physiology; D012121:Respiration, Artificial / Q000379:methods* / Q000706:statistics & numerical data; D012128:Respiratory Distress Syndrome / Q000503:physiopathology; D015656:Respiratory Mechanics / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D013552:Swine; D013990:Tidal Volume / Q000502:physiology; D014655:Vascular Resistance / Q000502:physiology
D016209:Interleukin-8; D002245:Carbon Dioxide; D019301:Oleic Acid; D010100:Oxygen