Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

Abstract

Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

“… 16 , 47 , 65 Furthermore, the respiratory elastance and resistance identified using the model has shown to be clinically relevant and similar to those identified from other models. 51 Finally, more descriptive nonlinear models risk issues, even when identifiable, with model mismatch to dynamics in the observed and measured data, which can lead to inaccurate parameter identification 54 , 71 and thus poor prediction and bedside decision support. It is important to note the single compartment linear lung model is also easily identifiable, which is of singular importance so it can be used with available clinical data.…”

Prediction and estimation of pulmonary response and elastance evolution for volume-controlled and pressure-controlled ventilation

“…Spontaneous breathing was prevented using sedation with muscle relaxants during RMs, to minimise asynchrony [44,[78][79][80]. Two staircase RMs were applied to each patient with increments and decrements of 4 cmH 2 O, consisting of 2 sets, Set 1 and Set 3, with increasing PEEP levels and 2 sets with decreasing PEEP levels, as shown in Fig.…”

Noninvasive Breathing Effort Estimation of Mechanically Ventilated Patients Using Sparse Optimization

“…We believe that this limitation on the patient effort is too stringent in practice. In [24], several different methods to estimate lung dynamics of sedated and spontaneously breathing patients are compared. It is shown to be challenging to obtain constant estimates in case of spontaneous breathing effort.…”