Effect of airway inertance on linear regression estimates of resistance and compliance in mechanically ventilated infants: a computer model study.
Respiratory inertance (I) is usually ignored when resistance (R) and compliance (C) of mechanically ventilated infants are estimated by least squares linear regression. Values of I that have been reported for these patients can cause impedances whose magnitudes approximate respiratory resistance. We show theoretically that if inertance is neglected no error is expected in resistance estimates, but a positive bias in compliance can be, proportional to the inertance, the compliance, and the sinusoidal frequency at which the measurements are made. To determine the errors in parameter estimates when the pressure waveform is non-sinusoidal, we simulated linear regression based on non-inertive and inertive models. R, C, and I of the simulated lung were varied over the range expected in an infant intensive care unit. The ventilator was simulated as a critically damped second order system with a square pulse input. The rise time (TR) of the pressure pulse was varied over the range reported in infant ICUs. Simulated measurements confirmed that resistance is correctly estimated if inertance is neglected. Maximum error in compliance estimates (13%) occurred when TR and R were low, and C and I were high. The variation in the error in estimated compliance was consistent with the theory. Coefficients of variation of the parameters, the standard errors, and R2 of the regressions tended to deteriorate with increasing compliance error, but the relationships were not single valued. These statistics may alert investigators to possible bias in compliance caused by neglected inertance, but cannot be used to correct any bias.
Turner
M J
MJ
Department of Electrical Engineering, University of the Witwatersrand, South Africa.
MacLeod
I M
IM
Rothberg
A D
AD
D000403:Airway Resistance / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007223:Infant; D008170:Lung Compliance / Q000502:physiology*; D015233:Models, Statistical; D012044:Regression Analysis; D012121:Respiration, Artificial