Constrained optimization for noninvasive estimation of work of breathing

Vicario, Francesco; Albanese, Anthony A.; Wang, Dong; Karamolegkos, Nikolaos; Chbat, Nicolas W.

doi:10.1109/embc.2015.7319594

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…Both the CO and PO estimates are able to catch the difference in the depth of the respiratory muscle effort. The interested reader is also referred to [11] where the estimated P mus signal is demonstrated to be valuable in computing the WOB breath by breath.…”

Section: Resultsmentioning

confidence: 99%

“…As a consequence, bias affects the estimates and its magnitude depends on how intense the active exhalation is. As discussed in [11], both early cycling off and active exhalation are forms of asynchrony between the patient and the ventilator. As such, they are undesired from a clinical point of view and they should be eliminated regardless of respiratory mechanics estimation.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Parameter and Input Estimation of a Respiratory Mechanics Model

Vicario

Albanese

Wang

et al. 2017

Modeling, Simulation and Optimization of Complex Processes HPSC 2015

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Real-time noninvasive estimation of respiratory mechanics in spontaneously breathing patients is still an open problem in the field of critical care. Even assuming that the system is a simplistic first-order single-compartment model, the presence of unmeasured patient effort still makes the problem complex since both the parameters and part of the input are unknown. This paper presents an approach to overcome the underdetermined nature of the mathematical problem by infusing physiological knowledge into the estimation process and using it to construct an optimization problem subject to physiological constraints. As it relies only on measurements available on standard ventilators, namely the flow and pressure at the patient's airway opening, the approach is noninvasive. Additionally, breath by breath, it continually provides estimates of the patient respiratory resistance and elastance as well as of the muscle effort waveform without requiring maneuvers that would interfere with the desired ventilation pattern.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Simultaneous Parameter and Input Estimation of a Respiratory Mechanics Model

Vicario

Albanese

Wang

et al. 2017

Modeling, Simulation and Optimization of Complex Processes HPSC 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other authors have shown non-invasive techniques to estimate the work of breathing. Vicario et al [41] presented a model that estimates the work of breathing of mechanically ventilated patients, using ventilatory signals such as airway pressure, volume, and airflow. Their model offers favorable results in cases in which the patients have normal and controlled respiratory efforts, but it has limitations for uncontrolled efforts.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Work of Breathing from Respiratory Muscle Activity In Spontaneous Ventilation: A Pilot Study

Muñoz

Mañanas

2019

Applied Sciences

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Work of breathing (WOB) offers information that may be relevant to determine the patient’s status under spontaneous mechanical ventilation in Intensive Care Unit (ICU). Nowadays, the most reliable technique to measure WOB is based on the use of invasive catheters, but the use of qualitative observations such as the level of dyspnea is preferred as a possible indicator of WOB level. In this pilot study, the activity of three respiratory muscles were recorded on healthy subjects through surface electromyography while they were under non-invasive mechanical ventilation, using restrictive and obstructive maneuvers to obtain different WOB levels. The respiratory pattern between restrictive and obstructive maneuvers was classified with the Nearest Neighbor Algorithm with a 91% accuracy and a neural network model helped classify the samples into three WOB levels with a 89% accuracy, Low: [0.3–0.8) J/L, Medium: [0.8–1.3] J/L and Elevated: (1.3–1.8] J/L, demonstrating the relationship between the respiratory muscle activity and WOB. This technique is a promising tool for the healthcare staff in the decision-making process when selecting the best ventilation settings to maintain a low WOB. This study identified a model to estimate the WOB in different ventilatory patterns, being an alternative to invasive conventional techniques.

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“…Hence, the disadvantages of the model are problematic in this application. However, the model is likely to perform well in patient triggered spontaneous breathing modes [23,34]. As a result, it performs better in cases for which it was designed, but less well in the highly variable Dataset used here, as seen in Figures 7 and 8.…”

Section: Constrained Optimisationmentioning

confidence: 95%

“…Instead, its goal was to capture the magnitude and timing of patient efforts. The modelled patient effort profile can then additionally be used to monitor the work of breathing [34]. In addition, it also captures respiratory mechanics, and thus offers a single method for both outcomes with no need for an extra comparison of pressure waveforms.…”

Section: Constrained Optimisationmentioning

confidence: 99%

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

Redmond

Chiew

Major

et al. 2019

Computer Methods and Programs in Biomedicine

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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.

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Constrained optimization for noninvasive estimation of work of breathing

Cited by 9 publications

References 8 publications

Simultaneous Parameter and Input Estimation of a Respiratory Mechanics Model

Simultaneous Parameter and Input Estimation of a Respiratory Mechanics Model

Estimation of Work of Breathing from Respiratory Muscle Activity In Spontaneous Ventilation: A Pilot Study

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

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