Iterative integral parameter identification of a respiratory mechanics model.
BACKGROUND
Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual's model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions.


METHODS
An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS) patients.


RESULTS
The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested.


CONCLUSIONS
These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.
Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany. scc@hs-furtwangen.de
Christoph
C
Schranz
Paul D
PD
Docherty
Yeong Shiong
YS
Chiew
Knut
K
Möller
J Geoffrey
JG
Chase
D006801:Humans; D008954:Models, Biological; D012044:Regression Analysis; D012128:Respiratory Distress Syndrome / Q000503:physiopathology; D015656:Respiratory Mechanics; D013997:Time Factors