Iterative integral parameter identification of a respiratory mechanics model.

Christoph Schranz,J Geoffrey Chase,Paul D Docherty,Knut Möller,Yeong Shiong Chiew

doi:10.1186/1475-925x-11-38

Christoph Schranz, J Geoffrey Chase + Show 3 more

Open Access

https://doi.org/10.1186/1475-925x-11-38

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Abstract

BackgroundPatient-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.MethodsAn 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.ResultsThe 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.ConclusionThese 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.

Highlights

Mechanical ventilation (MV) therapy in intensive care units (ICU) carries the risk of severe additional complications for the patient due to non-optimal ventilator settings [1]
Parameter identification using simulation data The identified parameter values from each parameter identification method were equal in the noise free synthetic data set and were effectively equal to the simulation values (R = 1.000)
The solutions reported by the Simplex Search Method (SSM) were used to define the new error minimum

Summary

Introduction

Mechanical ventilation (MV) therapy in intensive care units (ICU) carries the risk of severe additional complications for the patient due to non-optimal ventilator settings [1]. Mathematical models of respiratory mechanics effectively predict the outcome of specific ventilator settings and support clinical evaluation of lung protective settings [2]. To assure clinical applicability at the bedside, especially in closed loop settings [2,3], reliable and immediate parameter identification is required. 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. They are difficult to apply at the bedside to support therapeutic decisions

Results

Discussion

Conclusion